From e7fd7c22492279da2342856418e3c193ac36bf25 Mon Sep 17 00:00:00 2001 From: Hadrien Mary Date: Sun, 4 Sep 2022 09:01:00 -0400 Subject: [PATCH 01/15] news + cleaning + new tutorials wip + remove actions/reactions modules + doc for standardize_mol --- datamol/__init__.py | 2 - datamol/actions/__init__.py | 18 - datamol/actions/_actions.py | 791 ---- datamol/fp.py | 2 +- datamol/mol.py | 52 +- datamol/reactions/__init__.py | 5 - datamol/reactions/_reactions.py | 93 - datamol/utils/jobs.py | 11 +- docs/api/datamol.actions.md | 3 - docs/api/datamol.reactions.md | 3 - docs/tutorials/new/Clustering.ipynb | 1658 ++++++++ docs/tutorials/new/Descriptors.ipynb | 215 + docs/tutorials/new/Fragment.ipynb | 1442 +++++++ docs/tutorials/new/Fuzzyscaffolds.ipynb | 3451 +++++++++++++++++ docs/tutorials/new/Generatescaffold.ipynb | 598 +++ docs/tutorials/new/Generatingconformers.ipynb | 612 +++ docs/tutorials/new/Preprocessing.ipynb | 555 +++ mkdocs.yml | 3 +- news/tutos.rst | 24 + pyproject.toml | 2 +- tests/test_actions.py | 78 - tests/test_reactions.py | 4 - 22 files changed, 8607 insertions(+), 1015 deletions(-) delete mode 100644 datamol/actions/__init__.py delete mode 100644 datamol/actions/_actions.py delete mode 100644 datamol/reactions/__init__.py delete mode 100644 datamol/reactions/_reactions.py delete mode 100644 docs/api/datamol.actions.md delete mode 100644 docs/api/datamol.reactions.md create mode 100644 docs/tutorials/new/Clustering.ipynb create mode 100644 docs/tutorials/new/Descriptors.ipynb create mode 100644 docs/tutorials/new/Fragment.ipynb create mode 100644 docs/tutorials/new/Fuzzyscaffolds.ipynb create mode 100644 docs/tutorials/new/Generatescaffold.ipynb create mode 100644 docs/tutorials/new/Generatingconformers.ipynb create mode 100644 docs/tutorials/new/Preprocessing.ipynb create mode 100644 news/tutos.rst delete mode 100644 tests/test_actions.py delete mode 100644 tests/test_reactions.py diff --git a/datamol/__init__.py b/datamol/__init__.py index 68894207..53effead 100644 --- a/datamol/__init__.py +++ b/datamol/__init__.py @@ -69,8 +69,6 @@ from . import fragment from . import scaffold -from . import reactions -from . import actions from . import molar from . import descriptors from . import predictors diff --git a/datamol/actions/__init__.py b/datamol/actions/__init__.py deleted file mode 100644 index c34235af..00000000 --- a/datamol/actions/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -from ._actions import pick_atom_idx -from ._actions import add_bond_between -from ._actions import remove_bond_between -from ._actions import update_bond -from ._actions import compute_fragment_join -from ._actions import all_atom_join -from ._actions import all_join_on_attach_point -from ._actions import all_fragment_attach -from ._actions import all_atom_add -from ._actions import all_atom_replace -from ._actions import all_bond_add -from ._actions import all_bond_remove -from ._actions import all_fragment_on_bond -from ._actions import all_fragment_update -from ._actions import all_mmpa_assemble -from ._actions import all_fragment_assemble -from ._actions import all_transform_apply -from ._actions import mmpa_fragment_exchange diff --git a/datamol/actions/_actions.py b/datamol/actions/_actions.py deleted file mode 100644 index 707e403d..00000000 --- a/datamol/actions/_actions.py +++ /dev/null @@ -1,791 +0,0 @@ -from typing import Union -from typing import List -from typing import Optional - -import copy -import itertools -import operator -import random - -import numpy as np -from rdkit import Chem -from rdkit.Chem import AllChem -from rdkit.Chem import rdmolops - -import datamol as dm - - -def pick_atom_idx(mol: Chem.rdchem.Mol, prepick: Optional[int] = None): - """pick an atom from the molecule""" - - mol.UpdatePropertyCache() - if not (prepick is not None and prepick >= 0 and prepick < mol.GetNumAtoms()): - pickable_atoms = [a.GetIdx() for a in mol.GetAtoms() if a.GetImplicitValence() > 0] - if pickable_atoms: - prepick = random.choice(pickable_atoms) - else: - prepick = None - return prepick - - -def add_bond_between( - mol: Chem.rdchem.Mol, - a1: Union[int, Chem.rdchem.Atom], - a2: Union[int, Chem.rdchem.Atom], - bond_type: Chem.rdchem.BondType, - sanitize: bool = True, -): - """Add a new bond between atom""" - - if isinstance(a1, Chem.rdchem.Atom): - a1 = a1.GetIdx() - - if isinstance(a2, Chem.rdchem.Atom): - a2 = a2.GetIdx() - - emol = Chem.EditableMol(dm.copy_mol(mol)) - emol.AddBond(a1, a2, bond_type) - - if sanitize: - return dm.sanitize_mol(emol.GetMol()) - - return emol.GetMol() - - -def remove_bond_between( - mol: Chem.rdchem.Mol, - a1: Union[int, Chem.rdchem.Atom], - a2: Union[int, Chem.rdchem.Atom], - sanitize: bool = True, -): - """Remove a bond between atoms.""" - - if isinstance(a1, Chem.rdchem.Atom): - a1 = a1.GetIdx() - - if isinstance(a2, Chem.rdchem.Atom): - a2 = a2.GetIdx() - - emol = Chem.EditableMol(dm.copy_mol(mol)) - emol.RemoveBond(a1, a2) - - if sanitize: - return dm.sanitize_mol(emol.GetMol()) - - return emol.GetMol() - - -def update_bond( - mol: Chem.rdchem.Mol, - bond: Union[int, Chem.rdchem.Bond], - bond_type: Chem.rdchem.BondType, - sanitize: bool = True, -): - """Update bond type between atoms""" - new_mol = dm.copy_mol(mol) - - if isinstance(bond, Chem.rdchem.Bond): - bond = bond.GetIdx() - - with dm.without_rdkit_log(): - new_bond = new_mol.GetBondWithIdx(bond) - new_bond.SetBondType(bond_type) - - if sanitize: - return dm.sanitize_mol(new_mol) - - return new_mol - - -def all_atom_join( - mol: Chem.rdchem.Mol, - a1: Union[int, Chem.rdchem.Atom], - a2: Union[int, Chem.rdchem.Atom], -): - """Join two atoms (a1, a2) in a molecule in all possible valid manner.""" - - if isinstance(a1, int): - a1 = mol.GetAtomWithIdx(a1) - - if isinstance(a2, int): - a2 = mol.GetAtomWithIdx(a2) - - new_mols = [] - with dm.without_rdkit_log(): - try: - Chem.Kekulize(mol, clearAromaticFlags=True) - except: - pass - - v1, v2 = a1.GetImplicitValence(), a2.GetImplicitValence() - bond = mol.GetBondBetweenAtoms(a1.GetIdx(), a2.GetIdx()) - - if bond is None: - if v1 > 0 and v2 > 0: - new_mols.append(add_bond_between(mol, a1, a2, dm.SINGLE_BOND)) - if v1 > 1 and v2 > 1: - new_mols.append(add_bond_between(mol, a1, a2, dm.DOUBLE_BOND)) - if v1 > 2 and v2 > 2: - new_mols.append(add_bond_between(mol, a1, a2, dm.TRIPLE_BOND)) - - elif bond.GetBondType() == dm.SINGLE_BOND: - if v1 > 0 and v2 > 0: - new_mols.append(update_bond(mol, bond, dm.DOUBLE_BOND)) - if v1 > 1 and v2 > 1: - new_mols.append(update_bond(mol, bond, dm.TRIPLE_BOND)) - - elif bond.GetBondType() == dm.DOUBLE_BOND: - if v1 > 0 and v2 > 0: - new_mols.append(update_bond(mol, bond, dm.TRIPLE_BOND)) - - return [mol for mol in new_mols if mol is not None] - - -def compute_fragment_join( - mol: Chem.rdchem.Mol, - fragment: Chem.rdchem.Mol, - mol_atom_count: int, - bond_between_rings: bool = True, - asMols: bool = True, -): - """List all posibilities of where a fragment can be attached to a mol.""" - - fragment = copy.copy( - fragment - ) # need to copy the fragment copy is faster than all the other methods - - with dm.without_rdkit_log(): - - combined = Chem.CombineMols(mol, fragment) - for i1 in range(mol.GetNumAtoms()): - a1 = combined.GetAtomWithIdx(i1) - - if a1.GetImplicitValence() == 0: - continue - - for i2 in range(fragment.GetNumAtoms()): - i2 += mol_atom_count - a2 = combined.GetAtomWithIdx(i2) - if a2.GetImplicitValence() == 0: - continue - - # no bond between atoms already in rings - if not bond_between_rings and a1.IsInRing() and a2.IsInRing(): - continue - - # no bond to form large rings - else: - possibilities = all_atom_join(combined, a1, a2) - for x in possibilities: - x = dm.sanitize_mol(x) - if x is not None: - if not asMols: - x = dm.to_smiles(x) - yield x - - -def _compute_mmpa_assembly( - cores: List[Chem.rdchem.Mol], - side_chains: List[Chem.rdchem.Mol], - max_num_action: int = None, -): - """Enumerate core and side_chains assembly combination. - Input Core and side_chain are expected to have [1*] in place of the attachment point - - Note that this is based on a dm.SINGLE_BOND mmpa cut. - - Args: - cores: List of core. - side_chains: List of side chains. - max_num_action: Maximum number of assembly. None means infinite. - - Returns: - mols: Molecules obtained by merging core and side_chains. - """ - - if max_num_action is None: - max_num_action = int(float("Inf")) - - reaction = AllChem.ReactionFromSmarts("[*:1]-[1*].[1*]-[*:2]>>[*:1]-[*:2]") - molecules = [] - n_seen = 0 - random.shuffle(side_chains) - stop = False - for core in cores: - if not stop: - for sidechain in side_chains: - molecules.append(reaction.RunReactants((core, sidechain))[0][0]) # first only - n_seen += 1 - if n_seen > max_num_action: - stop = True - break - random.shuffle(molecules) - return molecules - - -def all_join_on_attach_point(mol1: Chem.rdchem.Mol, mol2: Chem.rdchem.Mol): - """Join two molecules on all possible attaching point - - Args: - mol1: Input molecule 1. - mol2: Input molecule 2. - - Returns: - iterator of all possible way to attach both molecules from dummy indicators. - """ - atom_map_min = 100 - mol_idxs = [] - count = 0 - mod_mols = [] - - for ind, m in enumerate([mol1, mol2]): - atms = [(a.GetIdx(), a) for a in m.GetAtoms() if not a.IsInRing() and a.GetAtomicNum() == 0] - atms.sort(reverse=True, key=operator.itemgetter(0)) - for a_idx, a in atms: - for a_nei in a.GetNeighbors(): - a_nei.SetAtomMapNum(atom_map_min + count) - count += 1 - mod_mol = dm.fix_mol(m) - mod_mols.append(mod_mol) - mol_idxs.append( - [a.GetIdx() for a in mod_mol.GetAtoms() if a.GetAtomMapNum() >= atom_map_min] - ) - - for ind1, ind2 in itertools.product(*mol_idxs): - yield random_fragment_add(copy.copy(mod_mols[0]), copy.copy(mod_mols[1]), ind1, ind2) - - -def all_fragment_attach( - mol: Chem.rdchem.Mol, - fragmentlist: List[Chem.rdchem.Mol], - bond_between_rings: bool = True, - max_num_action: int = 10, - asMols: bool = True, -): - """List all possible way to attach a list of fragment to a dm.SINGLE_BOND molecule. - - .. warning:: - This is computationally expensive - - Args: - mol: Input molecule - fragmentlist: Molecular fragments to attach. - bond_between_rings: Whether to allow bond between two rings atoms - max_num_action: Maximum fragment attachment to allow. Reduce time complexity - asMols: Whether to return output as molecule or smiles - - Returns: - All possible molecules resulting from attaching the molecular fragment to the root molecule - - """ - fragment_set = set([]) - mol_atom_count = mol.GetNumAtoms() - generators = [None] * len(fragmentlist) - empty_generators = np.zeros(len(generators)) - while len(fragment_set) < max_num_action and not np.all(empty_generators): - for i, fragment in enumerate(fragmentlist): - if len(fragment_set) >= max_num_action: - break - if generators[i] is None: - generators[i] = compute_fragment_join( - mol, fragment, mol_atom_count, bond_between_rings, asMols - ) - if not empty_generators[i]: - try: - fragment_set.add(next(generators[i])) - except StopIteration as e: - empty_generators[i] = 1 - continue - return fragment_set - - -def all_atom_add( - mol, - atom_types=["C", "N", "O", "F", "Cl", "Br"], - asMols=True, - max_num_action=float("Inf"), - **kwargs, -): - """Add a new atom on the mol, by considering all bond type - - .. warning:: - This is computationally expensive - - Args: - mol: - Input molecule - atom_types: list - List of atom symbol to use as replacement - (Default: ["C", "N", "O", "F", "Cl", "Br"]) - asMols: bool, optional - Whether to return output as molecule or smiles - max_num_action: float, optional - Maximum number of action to reduce complexity - Returns: - All possible molecules with one additional atom added - - """ - new_mols = [] - stop = False - with dm.without_rdkit_log(): - for atom in mol.GetAtoms(): - if stop: - break - if atom.GetImplicitValence() == 0: - continue - for atom_symb in atom_types: - emol = Chem.RWMol(mol) - new_index = emol.AddAtom(Chem.Atom(atom_symb)) - emol.UpdatePropertyCache(strict=False) - new_mols.extend(all_atom_join(emol, atom, emol.GetMol().GetAtomWithIdx(new_index))) - if len(new_mols) > max_num_action: - stop = True - break - - new_mols = [dm.sanitize_mol(mol) for mol in new_mols] - new_mols = [mol for mol in new_mols if mol is not None] - if not asMols: - return [dm.to_smiles(x) for x in new_mols if x] - return new_mols - - -def all_atom_replace( - mol, - atom_types=None, - asMols=True, - max_num_action=float("Inf"), - **kwargs, -): - """Replace all non-hydrogen atoms by other possibilities. - - .. warning:: - This is computationally expensive - - Args: - mol: - Input molecule - atom_types: list - List of atom symbol to use as replacement - (Default: ['C', 'N', 'S', 'O']) - asMols: bool, optional - Whether to return output as molecule or smiles - max_num_action: float, optional - Maximum number of action to reduce complexity - - Returns: - All possible molecules with atoms replaced - - """ - if atom_types is None: - atom_types = ["C", "N", "S", "O"] - new_mols = [] - stop = False - with dm.without_rdkit_log(): - for atom in mol.GetAtoms(): - if stop: - break - if atom.GetAtomicNum() > 1: - for atom_symb in atom_types: - emol = Chem.RWMol(mol) - emol.ReplaceAtom(atom.GetIdx(), Chem.Atom(atom_symb)) - new_mols.append(emol) - if len(new_mols) > max_num_action: - stop = True - break - - # Sanitize and remove bad molecules - new_mols = [dm.sanitize_mol(mol) for mol in new_mols] - new_mols = [mol for mol in new_mols if mol is not None] - - if not asMols: # Return SMILES - return [dm.to_smiles(x) for x in new_mols] - return new_mols - - -def all_bond_add( - mol, - allowed_ring_sizes=None, - bond_between_rings=True, - asMols=True, - max_num_action=float("Inf"), - **kwargs, -): - """Add bond to a molecule - - .. warning:: - This is computationally expensive - - Args: - mol: - Input molecule - allowed_ring_sizes: list, optional - Set of integer allowed ring sizes; used to remove some - actions that would create rings with disallowed sizes. - bond_between_rings: bool, optional - Whether to allow actions that add bonds - between atoms that are both in rings. - asMols: bool, optional - Whether to return output as molecule or smiles - max_num_action: float, optional - Maximum number of action to reduce complexity - - Returns: - All possible molecules with additional bond added between atoms - """ - new_mols = [] - num_atoms = mol.GetNumAtoms() - stop = False - for i1 in range(num_atoms): - if stop: - break - a1 = mol.GetAtomWithIdx(i1) - if a1.GetImplicitValence() == 0: - continue - for i2 in range(i1 + 1, num_atoms): - a2 = mol.GetAtomWithIdx(i2) - # Chem.rdmolops.GetShortestPath(mol, i1, i2) - all_paths = get_all_path_between(mol, i1, i2, ignore_cycle_basis=True) - all_path_len = {len(path) for path in all_paths} - if a2.GetImplicitValence() == 0: - continue - # no bond between atoms already in rings - bond = mol.GetBondBetweenAtoms(i1, i2) - if not bond_between_rings and a1.IsInRing() and a2.IsInRing(): - continue - # no bond to form large rings - if ( - (bond is None) - and (allowed_ring_sizes is not None) - and not all_path_len.issubset(allowed_ring_sizes) - ): - continue - new_mols.extend(all_atom_join(mol, a1, a2)) - if len(new_mols) > max_num_action: - stop = True - break - if not asMols: - return list({dm.to_smiles(x) for x in new_mols if x}) - return [m for m in new_mols if m is not None] - - -def all_bond_remove( - mol: Chem.rdchem.Mol, - as_mol: bool = True, - allow_bond_decrease: bool = True, - allow_atom_trim: bool = True, - max_num_action=float("Inf"), -): - """Remove bonds from a molecule - - Warning: - This can be computationally expensive. - - Args: - mol: Input molecule - allow_bond_decrease: Allow decreasing bond type in addition to bond cut - max_num_action: Maximum number of action to reduce complexity - allow_atom_trim: Allow bond removal even when it results in dm.SINGLE_BOND - - Returns: - All possible molecules from removing bonds - - """ - new_mols = [] - - try: - Chem.Kekulize(mol, clearAromaticFlags=True) - except: - pass - - for bond in mol.GetBonds(): - if len(new_mols) > max_num_action: - break - - original_bond_type = bond.GetBondType() - emol = Chem.RWMol(mol) - emol.RemoveBond(bond.GetBeginAtomIdx(), bond.GetEndAtomIdx()) - new_mol = dm.sanitize_mol(emol.GetMol()) - - if not new_mol: - continue - - frag_list = list(rdmolops.GetMolFrags(new_mol, asMols=True)) - has_single_atom = any([x.GetNumAtoms() < 2 for x in frag_list]) - if not has_single_atom or allow_atom_trim: - new_mols.extend(frag_list) - if allow_bond_decrease: - if original_bond_type in [dm.DOUBLE_BOND, dm.TRIPLE_BOND]: - new_mol = update_bond(mol, bond, dm.SINGLE_BOND) - if new_mol is not None: - new_mols.extend(list(rdmolops.GetMolFrags(new_mol, asMols=True))) - if original_bond_type == dm.TRIPLE_BOND: - new_mol = update_bond(mol, bond, dm.DOUBLE_BOND) - if new_mol is not None: - new_mols.extend(list(rdmolops.GetMolFrags(new_mol, asMols=True))) - - new_mols = [mol for mol in new_mols if mol is not None] - - if not as_mol: - return [dm.to_smiles(x) for x in new_mols if x] - - return new_mols - - -def all_fragment_on_bond(mol, asMols=False, max_num_action=float("Inf"), break_aromatic=True): - """Fragment all possible bond in a molecule and return the set of resulting fragments - This is similar to `random_bond_cut`, but is not stochastic as it does not return a random fragment - but all the fragments resulting from all potential bond break in the molecule. - - .. note:: - This will always be a subset of all_bond_remove, the main difference being that all_bond_remove, allow decreasing - bond count, while this one will always break a molecule into two. - - Args: - mol: - input molecule - asMols: bool, optional - Whether to return results as mols or smiles - max_num_action: float, optional - Maximum number of action to reduce complexity - break_aromatic: bool, optional - Whether to attempt to break even aromatic bonds - (Default: True) - - Returns: - set of fragments - - """ - mol.GetRingInfo().AtomRings() - fragment_set = set([]) - bonds = list(mol.GetBonds()) - stop = False - if bonds: - if break_aromatic: - Chem.Kekulize(mol, clearAromaticFlags=True) - for bond in bonds: - if stop: - break - if break_aromatic or not bond.GetIsAromatic(): - truncate = Chem.FragmentOnBonds(mol, [bond.GetIdx()], addDummies=False) - truncate = dm.sanitize_mol(truncate) - if truncate is not None: - for frag in rdmolops.GetMolFrags(truncate, asMols=True): - frag = dm.sanitize_mol(frag) - if frag: - if not asMols: - frag = dm.to_smiles(frag) - fragment_set.add(frag) - if len(fragment_set) > max_num_action: - stop = True - break - return fragment_set - - -def all_fragment_update( - molparent, - fragmentlist, - bond_between_rings=True, - max_num_action=float("Inf"), - asMols=False, -): - """ - Break molecule a molecules into all set of fragment (including the molecule itself). - Then enumerate all possible combination with blocks from the fragmentlist. - This corresponds to exploring all valid actions by adding/replacing fragments in a molecules. - - .. warning:: - This is computationally expensive - - .. note:: - You should perform a valency check after - - Args: - molparent: - input molecule - fragmentlist: list - List of blocks to use for replacement, or addition to molparent - bond_between_rings: bool, optional - Whether to allow bond between rings - (Default: True) - max_num_action: float, optional - Maximum number of action to reduce complexity - asMols: bool, optional - Whether to return smiles or mols - - Returns: - set of modified mols - """ - fragment_set = set([]) - mol_frags = dm.fragment.anybreak(molparent, remove_parent=False) - for mol in mol_frags: - mol_update = all_fragment_attach( - mol, fragmentlist, bond_between_rings, max_num_action, asMols - ) - fragment_set.update(mol_update) - if len(fragment_set) > max_num_action: - break - return list(fragment_set) - - -def all_mmpa_assemble(molist, max_num_action=float("Inf"), asMols=True, **kwargs): - """Enumerate all mmpa assembly of molecules in molist - - Args: - molist: list of - List of molecules to fragmente and reconstruct - asMols: bool, optional - Whether to return smiles or mols - max_num_action: int, optional - Maximum number of assembly - (Default: inf) - - Returns: - res: list of - Molecules obtained by merging core and side_chains - """ - frags = set([]) - cores = [] - side_chains = [] - for mol in molist: - mol_frag = dm.fragment.mmpa_frag(mol, max_bond_cut=30) - if not mol_frag: - continue - _, mol_frag = map(list, zip(*mol_frag)) - for m in mol_frag: - core, sidechain = m.split(".") - cores.append(Chem.MolFromSmiles(core.replace("[*:1]", "[1*]"))) - side_chains.append(Chem.MolFromSmiles(sidechain.replace("[*:1]", "[1*]"))) - new_mols = _compute_mmpa_assembly(cores, side_chains, max_num_action=max_num_action) - if not asMols: - new_mols = [dm.to_smiles(x) for x in new_mols if x] - return new_mols - - -def all_fragment_assemble( - fragmentlist, - max_num_action=float("Inf"), - asMols=True, - seen=None, -): - """Assemble a set of fragment into a new molecule - - .. warning:: - This is computationally expensive - - Args: - fragmentlist: list - List of blocks to use for replacement, or addition to molparent - max_num_action: float, optional - Maximum number of action to reduce complexity. No limit by default - asMols: bool, optional - Whether to return smiles or mols - seen: list, optional - List of initial molecules - - Returns: - reconstructed molecules - - """ - mols = [] - for m in dm.fragment.assemble_fragment_order( - fragmentlist, - seen=seen, - allow_incomplete=False, - max_n_mols=max_num_action, - ): - if len(mols) > max_num_action: - break - mols.append(m) - - if not asMols: - mols = [dm.to_smiles(x) for x in mols if x is not None] - return mols - - -def all_transform_apply( - mol, - rxns, - max_num_action=float("Inf"), - asMols=True, -): - """ - Apply a transformation defined as a reaction from a set of reaction to the input molecule. - - The reaction need to be one reactant-only - - Args: - mol: - Input molecule - rnxs: list - list of reactions/ reaction smarts - max_num_action: int, optional - Maximum number of result to return - (Default: inf) - asMols: bool, optional - Whether to return smiles or mols - - Returns: - Products obtained from applying the chemical reactions - """ - - mols = set([]) - with dm.without_rdkit_log(): - for rxn in rxns: - if len(mols) >= max_num_action: - break - if isinstance(rxn, str): - rxn = AllChem.ReactionFromSmarts(rxn) - try: - pcdts = [products[0] for products in rxn.RunReactants([mol])] - pcdts = [dm.sanitize_mol(x) for x in pcdts] - mols.update([dm.to_smiles(x) for x in pcdts if x]) - except: - pass - mols = [x for x in mols if x is not None] - if np.isfinite(max_num_action): - mols = mols[:max_num_action] - - mols = [dm.to_mol(x) for x in mols] - if not asMols: - mols = [dm.to_smiles(x) for x in mols if x is not None] - return mols - - -def mmpa_fragment_exchange(mol1, mol2, return_all=False, **kwargs): - """Perform a fragment exchange between two molecules using mmpa rules - - Args: - mol1: - input molecule 1 - mol2: - input molecule 1 - return_all: bool, optional - Whether to return list of all molecules - - Returns: - modified_mol1, modified_mol2 - Molecules obtained by exchanging fragment between mol1 and mol2. - In case of failure, mol1, mol2 are returned - - """ - - unwanted = [dm.to_smiles(m) for m in [mol1, mol2]] + [None] - res = all_mmpa_assemble([mol1, mol2]) - # find unique - res = set([dm.to_smiles(m) for m in res]) - res = list(res - set(unwanted)) - out = [] - for sm in res: - r = None - try: - r = dm.to_mol(sm, sanitize=True) - except: - continue - if r is not None: - out.append(r) - - if return_all: - return out - random.shuffle(out) - out.extend([mol1, mol2]) - return out[0], out[1] diff --git a/datamol/fp.py b/datamol/fp.py index 44885c0f..44759938 100644 --- a/datamol/fp.py +++ b/datamol/fp.py @@ -248,7 +248,7 @@ def to_fp( complete list. fold_size: If set, fold the fingerprint to the `fold_size`. If set, returned array is always a numpy array. - fp_args: Arguments to build the fingerprint. Refer to the official RDKit documentation. + **fp_args: Arguments to build the fingerprint. Refer to the official RDKit documentation. Returns: A fingerprint vector or None diff --git a/datamol/mol.py b/datamol/mol.py index b965a1e9..57e06352 100644 --- a/datamol/mol.py +++ b/datamol/mol.py @@ -367,19 +367,53 @@ def standardize_mol( stereo: bool = True, ): r""" - This function returns a standardized version the given molecule, with or without disconnect the metals. - The process is apply in the order of the argument. + This function returns a standardized version the given molecule. It relies on the + RDKit [`rdMolStandardize` module](https://www.rdkit.org/docs/source/rdkit.Chem.MolStandardize.rdMolStandardize.html) + which is largely inspired from [MolVS](https://github.com/mcs07/MolVS). Arguments: - mol: The molecule to standardize. - disconnect_metals: Whether to disconnect the metallic atoms from non-metals - normalize: Whether to apply normalization (correct functional groups and recombine charges). - reionize: Whether to apply molecule reionization - uncharge: Whether to remove all charge from molecule - stereo: Whether to attempt to assign stereochemistry + mol: A molecule to standardize. + + disconnect_metals: Disconnect metals that are defined as covalently bonded to non-metal. + Depending on the source of the database, some compounds may be reported in salt form + or associated to metallic ions (e.g. the sodium salt of a carboxylic compound). + In most cases, these counter-ions are not relevant so the use of this function is required + before further utilization of the dataset. In summary the process is the following: + + - Break covalent bonds between metals and organic atoms under certain conditions. + - First, disconnect N, O, F from any metal. Then disconnect other non-metals from transition metals (with exceptions). + - For every bond broken, adjust the charges of the begin and end atoms accordingly. + + normalize: Applies a series of standard transformations to correct functional groups and recombine charges. + It corrects drawing errors and standardizes functional groups in the molecule as well as ensuring the + overall proper charge of the compound. It includes: + + - Uncharge-separate sulfones + - Charge-separate nitro groups + - Charge-separate pyridine oxide + - Charge-separate azide + - Charge-separate diazo and azo groups + - Charge-separate sulfoxides + - Hydrazine-diazonium system + + reionize: If one or more acidic functionalities are present in the molecule, this option ensures the correct + neutral/ionized state for such functional groups. Molecules are uncharged by adding and/or removing hydrogens. + For zwitterions, hydrogens are moved to eliminate charges where possible. However, in cases where there is a + positive charge that is not neutralizable, an attempt is made to also preserve the corresponding negative charge + The algorithm works as follows: + + - Use SMARTS to find the strongest protonated acid and the weakest ionized acid. + - If the ionized acid is weaker than the protonated acid, swap proton and repeat. + + uncharge: This option neutralize the molecule by reversing the protonation state of protonated and deprotonated groups, + if present (e.g. a carboxylate is re-protonated to the corresponding carboxylic acid). + In cases where there is a positive charge that is not neutralizable, an attempt is made to also preserve the + corresponding negative charge to ensure a net zero charge. + + stereo: Stereochemical information is corrected and/or added if missing using built-in RDKit functionality to force a clean recalculation of stereochemistry (`AssignStereochemistry`). Returns: - mol: The standardized molecule. + mol: A standardized molecule. """ mol = copy_mol(mol) diff --git a/datamol/reactions/__init__.py b/datamol/reactions/__init__.py deleted file mode 100644 index a98168a2..00000000 --- a/datamol/reactions/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from ._reactions import is_reaction_ok -from ._reactions import compute_reaction_product -from ._reactions import apply_reaction -from ._reactions import can_react -from ._reactions import inverse_reaction diff --git a/datamol/reactions/_reactions.py b/datamol/reactions/_reactions.py deleted file mode 100644 index eef6925f..00000000 --- a/datamol/reactions/_reactions.py +++ /dev/null @@ -1,93 +0,0 @@ -from functools import singledispatch - -import numpy as np -from rdkit.Chem import rdChemReactions -from rdkit.Chem import AllChem - -import datamol as dm - - -def is_reaction_ok(rxn): - """Check is the reaction is synthetically valid""" - return rdChemReactions.SanitizeRxn(rxn) == rdChemReactions.SanitizeFlags.SANITIZE_NONE - - -def compute_reaction_product(out, single_output=True): - """Compute the product of a reaction""" - out = [dm.fix_mol(x[0], n_iter=0) for x in out] - if not single_output: - return [dm.sanitize_mol(x) for x in out] - # Might be a important to make a tradeoff decision in selecting products for greater speed. - # product = sorted(out, key=lambda x: MoleculeEnv.compute_reward_from_mol(x, True))[-1] - # sampling from list of products is an alternative - return dm.sanitize_first(np.random.permutation(out)) - - -@singledispatch -def apply_reaction(rxn, mol, react_pos, single_output=False): - """Apply a chemical reaction on a molecule""" - raise ValueError - - -@apply_reaction.register(rdChemReactions.ChemicalReaction) -def _(rxn, mol, react_pos, single_output=False): - # only cares about the first product right now - # Anyway, there is only one major product in the database - - if not rxn.IsInitialized(): - rxn.Initialize() - out = rxn.RunReactant(mol, react_pos) - return compute_reaction_product(out, single_output) - - -@apply_reaction.register(tuple) -def _(rxn, mol, react_pos, single_output=False): - reaction, reactants = rxn - if not reaction.IsInitialized(): - reaction.Initialize() - # now substitute one of the reactant by the mol - reactants = list(reactants) - reactants[react_pos] = mol - out = reaction.RunReactants(tuple(reactants)) - return compute_reaction_product(out, single_output) - - -@singledispatch -def can_react(rxn, mol): - """Check if a molecule is a reactant to a chemical reaction and return position""" - raise ValueError - - -@can_react.register(rdChemReactions.ChemicalReaction) -def _(rxn, mol): - if not rxn.IsInitialized(): - rxn.Initialize() - return _find_rct_position(rxn, mol) - - -@can_react.register(tuple) -def _(rxn, mol): - reaction = rxn[0] - if not reaction.IsInitialized(): - reaction.Initialize() - return _find_rct_position(reaction, mol) - - -def _find_rct_position(rxn, mol): - """Find the position of a reactant in a reaction""" - react_pos = -1 - for pos, rct in enumerate(rxn.GetReactants()): - if mol.HasSubstructMatch(rct): - react_pos = pos - return react_pos - - -def inverse_reaction(rxn): - """Get the reverse reaction of the input reaction""" - rxn2 = AllChem.ChemicalReaction() - for i in range(rxn.GetNumReactantTemplates()): - rxn2.AddProductTemplate(rxn.GetReactantTemplate(i)) - for i in range(rxn.GetNumProductTemplates()): - rxn2.AddReactantTemplate(rxn.GetProductTemplate(i)) - rxn2.Initialize() - return rxn2 diff --git a/datamol/utils/jobs.py b/datamol/utils/jobs.py index 55b66643..ceb0193b 100644 --- a/datamol/utils/jobs.py +++ b/datamol/utils/jobs.py @@ -45,7 +45,7 @@ def __init__( progress: whether to display progress bar total: The number of elements in the iterator. Only used when `progress` is True. tqdm_kwargs: Any additional arguments supported by the `tqdm` progress bar. - job_kwargs: Any additional arguments supported by `joblib.Parallel`. + **job_kwargs: Any additional arguments supported by `joblib.Parallel`. Example: @@ -98,7 +98,7 @@ def sequential( callable_fn (callable): function to call data (iterable): input data arg_type (str, optional): function argument type ('arg'/None or 'args' or 'kwargs') - fn_kwargs (dict, optional): optional keyword argument to pass to the callable funciton + **fn_kwargs (dict, optional): optional keyword argument to pass to the callable funciton """ total_length = JobRunner.get_iterator_length(data) @@ -130,7 +130,7 @@ def parallel( callable_fn (callable): function to call data (iterable): input data arg_type (str, optional): function argument type ('arg'/None or 'args' or 'kwargs') - fn_kwargs (dict, optional): optional keyword argument to pass to the callable funciton + **fn_kwargs (dict, optional): optional keyword argument to pass to the callable funciton """ total_length = JobRunner.get_iterator_length(data) @@ -238,7 +238,7 @@ def parallelized( - "kwargs": the input is passed as a map: `fn(**kwargs)`. total: The number of elements in the iterator. Only used when `progress` is True. tqdm_kwargs: Any additional arguments supported by the `tqdm` progress bar. - job_kwargs: Any additional arguments supported by `joblib.Parallel`. + **job_kwargs: Any additional arguments supported by `joblib.Parallel`. Returns: The results of the execution as a list. @@ -291,7 +291,8 @@ def parallelized_with_batches( job_kwargs: Any additional arguments supported by `joblib.Parallel`. flatten_results: Whether to flatten the results. joblib_batch_size: It corresponds to the `batch_size` argument of `dm.parallelized` that - is forwarded to `joblib.Parallel` under the hood.. + is forwarded to `joblib.Parallel` under the hood. + **job_kwargs: Any additional arguments supported by `joblib.Parallel`. Returns: The results of the execution as a list. diff --git a/docs/api/datamol.actions.md b/docs/api/datamol.actions.md deleted file mode 100644 index b19f47ab..00000000 --- a/docs/api/datamol.actions.md +++ /dev/null @@ -1,3 +0,0 @@ -# `datamol.actions` - -::: datamol.actions._actions diff --git a/docs/api/datamol.reactions.md b/docs/api/datamol.reactions.md deleted file mode 100644 index e9520c9e..00000000 --- a/docs/api/datamol.reactions.md +++ /dev/null @@ -1,3 +0,0 @@ -# `datamol.reactions` - -::: datamol.reactions._reactions diff --git a/docs/tutorials/new/Clustering.ipynb b/docs/tutorials/new/Clustering.ipynb new file mode 100644 index 00000000..f35520b5 --- /dev/null +++ b/docs/tutorials/new/Clustering.ipynb @@ -0,0 +1,1658 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "85f43cfe", + "metadata": {}, + "source": [ + "# Clustering Molecules\n", + "\n", + "💡 [Clustering](https://en.wikipedia.org/wiki/Cluster_analysis) - the act of *grouping a set of objects in such a way that objects in the same group (called a **cluster**) are more similar (in some sense) to each other than to those in other groups (clusters).*\n", + "\n", + "One of the largest challenges in early-stage drug discovery is narrowing down the massive chemical space of approximately **10 to the power of 60 molecules (10^60)** to a list of molecules that have the desired properties for a specific target of interest. This is where computational approaches comes in, taking a large library of small molecules and reduce its size by filtering in/out molecules based on similarity, patterns, predicted physicochemical properties, specific rules, etc. This selection process allows scientists to focus on compounds with the highest chance of success before experimental testing in a lab, saving time and money.\n", + "\n", + "Clustering molecules is an extremely useful process where you can easily manipulate and subdivide large datasets to group compounds into smaller clusters with similar properties. Comparing molecules and their similarities can then be used to discover new molecules with optimal properties and desired biological activity. \n", + "\n", + "### How are compounds clustered?\n", + "\n", + "Compounds can be clustered via multiple clustering algorithms. There are also multiple ways to measure similarity between compounds, and theoretically, any [molecular descriptor](https://pubs.acs.org/doi/abs/10.1021/jm401411z) can be used. ***The current common approach for structural clustering is the [Butina](https://pubs.acs.org/doi/abs/10.1021/ci9803381) algorithm which can use multiple similarity measures. In Datamol, the measure set as the default is the [Tanimoto similarity](http://www.biotech.fyicenter.com/1000134_What_Is_Tanimoto_coefficient.html#:~:text=Tanimoto%20coefficient%20is%20a%20metric,union%20of%20the%20two%20sets.) index, measured on a scale between 0 (not similar) to 1 (most similar)***. After clustering molecules, you can also identify **centroids**. These are essentially the molecules in the middle of the cluster and are frequently used to **represent** **the cluster as a whole**. \n", + "\n", + "For a more detailed breakdown of clustering methods and their uses in computational chemistry, read [here](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.630&rep=rep1&type=pdf).\n", + "\n", + "**Note:** centroids are highlighted here only as an example. Centroid identification is not linked to clustering itself, and there are algorithms commonly utilized that have nothing to do with centroids (i.e. [hierarchical clustering](https://chemaxon.com/presentation/hierarchical-clustering-of-chemical-structures-by-maximum-common-substructures)).\n", + "\n", + "## Molecular Fingerprints\n", + "\n", + "In order for us to perform machine learning techniques or statistical analyses on molecules, we must represent molecules as mathematical objects (i.e vectors). Molecular fingerprints essentially encode the structural characteristics of molecules in the form of vectors enabling us to subsequently leverage statistical techniques to uncover new insights. \n", + "\n", + "The most common fingerprint used today is ECFP4 (extended connectivity fingerprints), also known as the Morgan fingerprint. [Here](https://towardsdatascience.com/a-practical-introduction-to-the-use-of-molecular-fingerprints-in-drug-discovery-7f15021be2b1) is a practical blog that explains what and how to use ECFP4. \n", + "\n", + "## Tutorial\n", + "\n", + "This tutorial will walk you through the following:\n", + "\n", + "1. Loading an example dataset\n", + "2. Calculate fingerprints\n", + "3. Then generate distance matrix\n", + "4. Cluster with the Butina algorithm \n", + "5. Pick diverse molecules from a list\n", + " 1. Why is this useful? \n", + " 1. Resource limitations generally prevent you from experimentally testing as many compounds as you want/are available. Therefore, you want to be able to collect as much information as possible through diversity. By selecting diverse molecules (i.e. one representative example from each chemical series in a list), you can quickly gain information around the effect of structural changes on in vitro activity while exploring a larger chemical space in fewer “shots”.\n", + "6. Pick centroids from a set of molecules\n", + "\n", + "First let’s see what this process would look like on RDKit: \n", + "\n", + "## RDKit Example" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e69926c7", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'dm' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mrdkit\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mSimDivFilters\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrdSimDivPickers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MaxMinPicker\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m### Clustering compounds\u001b[39;00m\n\u001b[1;32m 9\u001b[0m \n\u001b[1;32m 10\u001b[0m \u001b[38;5;66;03m# Get some mols\u001b[39;00m\n\u001b[0;32m---> 11\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mdm\u001b[49m\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mfreesolv()\n\u001b[1;32m 12\u001b[0m smiles \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msmiles\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39miloc[:]\u001b[38;5;241m.\u001b[39mtolist()\n\u001b[1;32m 13\u001b[0m mols \u001b[38;5;241m=\u001b[39m [Chem\u001b[38;5;241m.\u001b[39mMolFromSmiles(s) \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m smiles]\n", + "\u001b[0;31mNameError\u001b[0m: name 'dm' is not defined" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "from rdkit import Chem\n", + "from rdkit.Chem import DataStructs\n", + "from rdkit.ML.Cluster import Butina\n", + "from rdkit.SimDivFilters.rdSimDivPickers import MaxMinPicker\n", + "\n", + "### Clustering compounds\n", + "\n", + "# Get some mols\n", + "data = dm.data.freesolv()\n", + "smiles = data[\"smiles\"].iloc[:].tolist()\n", + "mols = [Chem.MolFromSmiles(s) for s in smiles]\n", + "\n", + "# Create fingerprints\n", + "fps = [Chem.RDKFingerprint(x) for x in mols]\n", + "\n", + "# Calculate distance matrix\n", + "dists = []\n", + "n_mols = len(mols)\n", + "\n", + "for i in range(1, n_mols):\n", + " dist = DataStructs.cDataStructs.BulkTanimotoSimilarity(\n", + " fps[i], fps[:i], returnDistance=True\n", + " )\n", + " dists.extend([x for x in dist])\n", + "\n", + "cutoff = 0.2\n", + "# now cluster the data\n", + "cluster_indices = Butina.ClusterData(dists, n_mols, cutoff, isDistData=True)\n", + "cluster_mols = [operator.itemgetter(*cluster)(mols) for cluster in cluster_indices]\n", + "\n", + "# Make single mol cluster a list\n", + "cluster_mols = [[c] if isinstance(c, Chem.rdchem.Mol) else c for c in cluster_mols]\n", + "\n", + "### Pick diverse compounds\n", + "# Get some mols\n", + "data = dm.data.freesolv()\n", + "smiles = data[\"smiles\"].iloc[:].tolist()\n", + "mols = [Chem.MolFromSmiles(s) for s in smiles]\n", + "\n", + "# Calculate fingerprints\n", + "fps = [Chem.RDKFingerprint(x) for x in mols]\n", + "\n", + "def distij(i, j, features=fps):\n", + " return 1.0 - DataStructs.cDataStructs.TanimotoSimilarity(fps[i], fps[j])\n", + "\n", + "npick = 10\n", + "seed = 0\n", + "\n", + "picker = MaxMinPicker()\n", + "initial_picks = []\n", + "picked_inds = picker.LazyPick(distij, len(mols), npick, firstPicks=initial_picks, seed=seed)\n", + "picked_inds = np.array(picked_inds)\n", + "picked_mols = [mols[x] for x in picked_inds]\n", + "\n", + "picked_inds, picked_mols" + ] + }, + { + "cell_type": "markdown", + "id": "fa727157", + "metadata": {}, + "source": [ + "## Datamol Example\n", + "\n", + "**Note:** Datamol abstracts away the explicit steps 2 (calculating fingerprints) and 3 (generating a distance matrix) of the tutorial" + ] + }, + { + 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[dm.to_mol(s) for s in smiles]\n", + "\n", + "# Cluster the mols\n", + "clusters, mol_clusters = dm.cluster_mols(mols, cutoff=0.7)\n", + "\n", + "# Cluster #1\n", + "dm.viz.to_image(mol_clusters[0], mol_size=(100, 100), n_cols=6, max_mols=18)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "71b09c2e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + 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+ "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Pick a diverse set of molecules\n", + "indices, picks = dm.pick_diverse(mols, npick=18)\n", + "dm.viz.to_image(picks, mol_size=(100, 100), n_cols=6)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "50c4fb2c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", 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"\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Pick centroids from a set of molecules\n", + "indices, centroids = dm.pick_centroids(mols, npick=18, threshold=0.7, method=\"sphere\", n_jobs=-1)\n", + "dm.viz.to_image(centroids, mol_size=(100, 100), n_cols=6)" + ] + }, + { + "cell_type": "markdown", + "id": "a233a779-6ce1-4e8c-ade5-86eaa627f039", + "metadata": { + "tags": [] + }, + "source": [ + "**Note**: Datamol provides one method (Butina using Tanimoto/ECFP for distances computations) for clustering molecules. In practice, an infinite number of methods exists and the user should build them as needed. Please feel free to contribute to Datamol if you wish to add any specific methods that are useful! \n", + "\n", + "## Understanding key parameters\n", + "\n", + "- Determining an appropriate threshold for cutoff\n", + " - Butina uses distances (which is 1 - distance) and the cutoff is dependent on the distance metric used. As mentioned earlier, Datamol uses Tanimoto with ECFP fingerprint. Therefore the distance cutoff is 1 - Tanimoto.\n", + " - Generally speaking, if you have a very small distance cutoff, compounds must be extremely similar (i.e. high Tanimoto score) in order to be grouped into one cluster. Therefore, with a small distance cutoff, you’ll get more clusters with fewer compounds per cluster. Vice versa is true.\n", + "\n", + "**Note:** This is an extremely general overview, in reality, the output greatly depends on both the size and diversity of the dataset being used. There is no “default” cutoff that is set in Datamol and instead each user should set cutoffs according to their specific dataset and use case. \n", + "\n", + "You can also see a more detailed definition of the methods, arguments and their returns, [here](https://github.com/datamol-org/datamol/blob/main/datamol/cluster.py#L173). \n", + "\n", + "## References\n", + "\n", + "- Macs in Chemistry - [https://www.macinchem.org/reviews/clustering/clustering.php](https://www.macinchem.org/reviews/clustering/clustering.php)\n", + "- TeachOpenCADD - [https://projects.volkamerlab.org/teachopencadd/talktorials/T005_compound_clustering.html#Picking-diverse-compounds](https://projects.volkamerlab.org/teachopencadd/talktorials/T005_compound_clustering.html#Picking-diverse-compounds)\n", + "- [https://jcheminf.biomedcentral.com/articles/10.1186/s13321-020-00445-4](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-020-00445-4)\n", + "- [https://towardsdatascience.com/a-practical-introduction-to-the-use-of-molecular-fingerprints-in-drug-discovery-7f15021be2b1](https://towardsdatascience.com/a-practical-introduction-to-the-use-of-molecular-fingerprints-in-drug-discovery-7f15021be2b1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6acbd158-cca3-4899-b781-c6493c6754a7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git 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" + } + }, + "cell_type": "markdown", + "id": "db5685a5", + "metadata": {}, + "source": [ + "# Descriptors\n", + "\n", + "## Molecular Descriptors\n", + "\n", + "> Molecular descriptors can be defined as mathematical representations of molecules’ properties that are generated by algorithms. The numerical values of molecular descriptors are used to quantitatively describe the physical and chemical information of the molecules. An example of molecular descriptors is the LogP which is a quantitative representation of the [lipophilicity](https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/lipophilicity) of the molecules, it is obtained by measuring the partitioning of the molecule between an aqueous phase and a lipophilic phase which consists usually of water/*n*-octanol. - [source](https://www.sciencedirect.com/topics/medicine-and-dentistry/molecular-descriptor)\n", + "> \n", + "\n", + "Molecular descriptors can generally classified in four ways:\n", + "\n", + "![image.png](attachment:c13f670f-e6ec-4364-adc7-044cea35600c.png)\n", + "\n", + "([source](https://chem.libretexts.org/Courses/Intercollegiate_Courses/Cheminformatics_OLCC_(2019)/05%3A_5._Quantitative_Structure_Property_Relationships/5.03%3A_Molecular_Descriptors))\n", + "\n", + "## Tutorial\n", + "\n", + "In this tutorial, we’ll show how descriptors can be useful as filters in the drug discovery process. This tutorial was inspired from the [TeachOpenCADD talktorial](https://projects.volkamerlab.org/teachopencadd/talktorials/T002_compound_adme.html?highlight=descriptors), we highly encourage you to read through the theory, understand ADME and why we care about it in the drug discovery process from the talktorial before diving into this tutorial. It provides the necessary background information to fully understand the purpose of this tutorial. \n", + "\n", + "The set of descriptors that will be focused on today are: \n", + "\n", + "- Molecular weight ≤ 500 Da\n", + "- Number of hydrogen bond acceptors (HBAs) ≤ 10\n", + "- Number of hydrogen bond donors (HBD) ≤ 5\n", + "- Calculated LogP (octanol-water coefficient) ≤ 5\n", + "\n", + "These descriptors and their limits are collectively known as **[Lipinkski’s rule of five (Ro5)](https://www.sciencedirect.com/science/article/abs/pii/S0169409X96004231)**, this is a method used to estimate a compounds bioavailability based solely on its chemical structure. If a molecule violates any of the rules listed above (i.e. a molecular weight of 700 Da), it’s probable that the compound will **exhibit poor absorption or permeation** and subsequently be removed from your list.\n", + "\n", + "## Tutorial\n", + "\n", + "This tutorial will show you a real-world scenario of \n", + "\n", + "- **Part 1:** Obtaining a virtual screening library from **[Enamine](https://enamine.net/compound-libraries/targeted-libraries/dna-library)**\n", + " - The DNA library is designed to identify novel active compounds against proteins which are essential for DNA stability. At 5530 compounds, this is one of Enamine’s smaller libraries. The same functions could easily be applied to some of the larger libraries using Datamol’s parallelize functions.\n", + "- **Part 2:** Then calculate the relevant molecular properties for the Ro5 for the list\n", + "- **Part 3:** Investigate compliance with Ro5\n", + "- **Part 4:** And finally, revealing the statistics for the dataset of compounds using Ro5 as a filter. With this, we will be able to find the answer to our question; how many fulfill vs. violate Ro5?\n", + " - Subsequently, we can show different ways of displaying the data to make it more visually appealing using Matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2136858", + "metadata": {}, + "outputs": [], + "source": [ + "import datamol as dm\n", + "\n", + "# Part 1: Obtain a list of molecules and visualize\n", + "# Load sdf downloaded from Enamine with the flag as_df set to True\n", + "# This will automatically create a 'smiles' column from the sdf file\n", + "data = dm.read_sdf('/home/data/Enamine_DNA_Libary_5530cmpds_20200831.sdf', as_df=True)\n", + "smiles = data[\"smiles\"].iloc[:].tolist()\n", + "mols = [dm.to_mol(s) for s in smiles]\n", + "dm.to_image(mols[900:909], n_cols = 3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "396835e5", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate a specific descriptor for a compound\n", + "n_aromatic_atoms = dm.descriptors.n_aromatic_atoms(mols[0])\n", + "print(\"Number of aromatic atoms in the compound is\", n_aromatic_atoms)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41388855", + "metadata": {}, + "outputs": [], + "source": [ + "# Part 2: Calculate the relevant molecular properties for the Ro5 for the list\n", + "\n", + "# Calculate many descriptors for a compound\n", + "dm.descriptors.compute_many_descriptors(mols[900])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "108c4837", + "metadata": {}, + "outputs": [], + "source": [ + "# Batch compute many descriptors for a list of compounds\n", + "df = dm.descriptors.batch_compute_many_descriptors(mols)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6733ca23", + "metadata": {}, + "outputs": [], + "source": [ + "# Part 3: Investigate compliance with Ro5\n", + "\n", + "df = df[df['mw'] <= 500]\n", + "df = df[df['n_lipinski_hba'] <= 10]\n", + "df = df[df['n_lipinski_hbd'] <= 5]\n", + "df = df[df['clogp'] <= 5]\n", + "df\n", + "\n", + "# 5350 of the 5530 compounds in the dataset satisfy all criteria in the rule of 5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc9f95ed", + "metadata": {}, + "outputs": [], + "source": [ + "# Part 4: Reveal the statistics for the dataset of compounds using Ro5 as a filter. How many fulfill vs. violate Ro5? \n", + "# Plotting the RO5 descriptors\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "fig, axs = plt.subplots(ncols=4, figsize=(25, 6))\n", + "plt.rcParams['font.size'] = 12\n", + "sns.histplot(df, x='mw', ax=axs[0])\n", + "sns.histplot(df, x='n_lipinski_hba', ax=axs[1])\n", + "sns.histplot(df, x='n_lipinski_hbd', ax=axs[2])\n", + "sns.histplot(df, x='clogp', ax=axs[3])" + ] + }, + { + "cell_type": "markdown", + "id": "415ac881", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "If you’re curious to learn more about some of the other established rules in the drug discovery industry, feel free to run this list through a Google search: \n", + "\n", + "- Rules of CNS\n", + "- BBB score\n", + "- Rule of Egan\n", + "- Rule-of-5\n", + "- Beyond Rule-of-5\n", + "- Rule-of-4\n", + "- Ghose Filter\n", + "- Zinc Rule\n", + "- Rule of GSK (4/400)\n", + "- Lead-Like Soft Rule\n", + "- Oprea’s Rule\n", + "- Pfizer Rule (3/75)\n", + "- REOS Filter\n", + "- Rule-of-3\n", + "- Extended Rule-of-3\n", + "- Veber Filter\n", + "\n", + "## References:\n", + "\n", + "- TeachOpenCADD - [https://projects.volkamerlab.org/teachopencadd/talktorials/T002_compound_adme.html?highlight=descriptors](https://projects.volkamerlab.org/teachopencadd/talktorials/T002_compound_adme.html?highlight=descriptors)\n", + "- ADME criteria ([Wikipedia](https://en.wikipedia.org/wiki/ADME) and [Mol Pharm. (2010), 7(5), 1388-1405](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3025274/))\n", + "- What are lead compounds? ([Wikipedia](https://en.wikipedia.org/wiki/Lead_compound))\n", + "- What is the LogP value? ([Wikipedia](https://en.wikipedia.org/wiki/Partition_coefficient))\n", + "- Lipinski et al. “Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings.” ([Adv. Drug Deliv. Rev. (1997), 23, 3-25](https://www.sciencedirect.com/science/article/pii/S0169409X96004231))" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/tutorials/new/Fragment.ipynb b/docs/tutorials/new/Fragment.ipynb new file mode 100644 index 00000000..5cf3acf8 --- /dev/null +++ b/docs/tutorials/new/Fragment.ipynb @@ -0,0 +1,1442 @@ +{ + "cells": [ + 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k9URABERABERABPKLgIyz/Hremq0IiIAIiIAIiEDECcg4i/gDknoiIAIiIAIiIAL5RUDGWX49b81WBERABERABEQg4gRknEX8AUk9ERABERABERCB/CIg4yy/nrdmKwIiIAIiIAIiEHECMs4i/oCkngiIgAiIgAiIQH4RkHGWX89bsxUBERABERABEYg4ARlnEX9AUk8EREAEREAERCC/CMg4y6/nrdmKgAiIgAiIgAhEnICMs4g/IKknAiIgAiIgAiKQXwRknOXX89ZsRUAEREAEREAEIk5AxlnEH5DUEwEREAEREAERyC8CMs7y63lrtiIgAiIgAiIgAhEnIOMs4g9I6omACIiACIiACOQXARln+fW8NVsREAEREAEREIGIE5BxFvEHJPVEQAREQAREQATyi4CMs/x63pqtCIiACIiACIhAxAnIOIv4A5J6IiACIiACIiAC+UVAxll+PW/NVgREQAREQAREIOIEZJxF/AFJPREQAREQAREQgfwiIOMsv563ZisCIiACIiACIhBxAjLOIv6ApJ4IiIAIiIAIiEB+EZBxll/PW7MVAREQAREQARGIOAEZZxF/QFJPBERABERABEQgvwjIOMuv563ZioAIiIAIiIAIRJyAjLOIPyCpJwIiIAIiIAIikF8EZJzl1/PWbEVABERABDJEYODAgWPHjs2QcImFwPvvv3/dddf98ccfOU+jas7PUBMUAREQAREQgYwSWLFixQknnDBq1KgqVaoMGzbs+OOPz+hw+Sn8rbfe2n///X///fdnn332pZde2njjjXOYgzxnOfxwNTUREAEREIHMEigoKHj44Ye32morLDNGWrlyJVbaXnvt9dFHH2V24HyS/u233x599NF77LEHlhnz/vDDD7fYYoubbrrpr7/+ylUMMs5y9clqXiIgAiIgApklMG3atPbt2+MnW7BggX+kSZMmtWnT5owzzli8eLH/us7DEmAF89prr916662feOIJ17dSpUqc4Kq87LLLWrVq9fzzz4eVmRXtZZxlxWOSkiIgAiIgAhEigDV23HHHYZlhnzm1dt999+nTpz/00ENuue2///677777cPAMGjTon3/+iZDq2aPKk08+iVl2zTXXOIdZzZo1+/fv/9VXXx1xxBFuEnPmzDnkkEP22WefmTNnZs+0Amkq4ywQJjUSAREQAREQAQj8+eefN9xww5ZbbvnII4+wpsmVxo0bY0a8+eabeMtY08R6wKmz9tprc+vXX3/t3bt369atX375ZdELTuCDDz7A2O3Zs+c333xDL7xlmMKA7dOnz+abb+5o77DDDk7gxIkTt99++3POOeeXX34JPkTEW8o4i/gDknoiIAIiIAJRIUBgWYsWLa688srffvsNnWrUqMGi2xdffOH5crhYq1atG2+88bPPPsOp4/T+8ssvu3TpcuCBB3ISlZlEVY8ff/zxlFNO2Wmnnd5++22n4y677DJ16tQRI0Y0aNDA0xrTjZ2b999//0YbbcRFQv3uuusu/JR33nnnv//+6zXL3hMZZ9n77KS5CIiACIhAORH4+OOPO3bsePjhh8+bN88NSYg6xtZVV11VvXr14ko0a9aMTYUTJkzYZptt3F02GOJCw5GGO614e135+++/b731VgwstruyKAyQhg0bstninXfeadeuXXE+lStXxoybNWvWRRddVK1aNRrgOTv33HO32267V199tXj77Loi4yy7npe0FQEREAERKFcCP/30E6H9LFm+8cYbbuAdd9xx8uTJjz322CabbJJYlU6dOs2YMQOnzgYbbEBLgs8IQcP+GDp0qLM/EnfPn7svvPACVuwll1yybNkyZr3OOutcccUVrGMee+yxbgdAaShq166NSUfMWdeuXV0bfJadO3c++OCDZ8+eXVqv6F+XcRb9ZyQNRUAEREAEKoAAttTtt9+OLUVoPwtnaECw/4MPPvjuu+926NAhoEJkPjvrrLNw8ODUqVo1lluULZynn3461h6bOgMKyeFmn3/++X777detWzcQuWkedthhXLz++uvXXXfdgBMnBBDz7pVXXmHR2XUZM2YMezkvvfTS5cuXBxQSqWYyziL1OKSMCIiACIhAJAjwTb/tttteeOGFS5cuRaG11lqLb3p8OSeeeGJiX06J2q+33npDhgwh+dm+++7rGnBOOrQePXp466Qldszhi0uWLDn//POBPH78eDdNViRff/31Z555ZrPNNkti4hh5rD5jT9etW5furJPefPPN2NbY01nnp5RxlsQHQF1EQAREQARylgAWGMH7BxxwAJH+bpKskbFYNmDAAIL9U5l2y5YtMURw6rDl0MnBEMHZ07dvX7fDIBXhWdQXN+Q999yD2YTB6uL369Wrd++995KLBIM1lYngm8Tgwwl35pln4rNE1KJFi04++WSi1liJTkVyOfeVcVbOwDWcCIiACIhARAkQqk90OcFPBO87FVkaI7r8ueeea968ebqUPuiggz799FOcOsRLIZPcHCzhUWPg0Ucfdbk50jVQNOW89tprZMFgqffnn39GQ2L5L7jgAswplnqdOZW62ph6d999N/k42MPhpGH27bbbbkcddRTFBlKXXw4SZJyVA2QNIQIiIAIiEGkCLHuRlwFfzm233eZyxq6//vrkZWDxkRynaVedRVKC33HRnXTSSew6RP73339P8DuhbAS0pX24iAicO3fuoYceyiaJTz75xKlErUwWItkk4RYi06snC6YYgmQ/adq0qZM8cuRIstqS/ST6pdNlnKX3wyBpIiACIiACWUaA/LFt27Y97bTTXLUllsbIaIov5+yzzw7ly/GWQQPOv379+qSNwBrDqeO6kNCLtF5ksv3hhx8CCsmKZlRbuvzyy1nVJb2IU5gQ/hdffJHcvFhLwaewcOHCsIlIMAfZXkBpAQoMMBDFBig5wKBksg0+bgW0ZNtqggMXqw4REAERyAoC/ldZVigsJSucAMH4hOT7v3oJ2CcvQxKKsXCGD4zEs9QUSqI7tSM33XRTTxNXqogVzyRERaoLLsnhw4f788fWqVNn4MCBROsnoWf37t033HBDt3k2bHdXccu/mQObmKcWVk4m2vvfXe7cil/yX8mEEpIpAiIgApkgoHdXJqjmqkwC8AnDJ5+WZw8RpE8V7aTn63m/KNxE+SY+jWFF4dS5+uqr/SltyWQ7evTosHKi037KlCn+/LEYr6eeeio1AJLTkDJN3sOiXhNp55KQQy1UfJOeHFRiuwAOuSREpbGL/93lzmWcpRGvRImACFQkAf8LriL10NjRJoAvh9D7Ro0aed/Q7MG86aab/vrrr6QVZ/shwWr4hDyZZESjCDpjhZVJNUlqSnpyONl7770JzAorp2Lbf/fdd7169fK7qfbYY48PP/wwFa1YnfQSkTg+FGz4+uuvw8rkoVAXlfIDHmR2Ztxyyy2pfADC6hDX3v/uknEWB0e/ioAIZDcB/wsuu2ci7TNGgACv9u3be9/KWA/kLUuX4wSfEFsOXYC/G4IakVQfSmI2b731FllqPT0JfSM3BLUKkhBVzl2Itb/uuuv8+WObNGny1FNPpUsNvJteIhL44Puk1CkxbWHl04UiBH7XKdtByHISVk5a2vvfXTLO0oJUQkRABKJCwP+Ci4pO0iMyBAg5ItDe78vZddddKZ6ddgWp17Tnnnt6dhUnVOEkg0PYgfDGPfDAA2wa8ESRyXbw4MFsJg0rqtzaP/300/78sRSG79evH+ZaehXAxUUiEn/OOfygeEOT8FPieKMggUeYE0o/kegkvQqXKc3/7pJxViYuNRABEcgmAv4XXDbpLV0zTICw+htvvNFt1nNfw4TeP/744xkdNl1mCpsTSbpB6g3PgCBpLdULMqp8EsLjTFKM4GOOOYbFzSREBeyCv9NLROLgEEyGZzRgd38zyhJQnMAjzHbd8847jzLq/jYZPfe/u2ScZRS1hIuACJQ3Af8LrrzH1nhRJUDuBsLqve9dwu2vuuoqdgOUg754jOIKRCa9wEdeD1LXerPghDrfpEkrh1mUOQSLuWQhSctibpljFW+A7xMPqEcGo/D444/HS1q8ZeIr+CkpUUD2Wk8UteqpWE8Bg8Qd03LX/+6ScZYWpBIiAiIQFQL+F1xUdJIeFUeATKfkO/W+azk58sgj58+fX84apTE0fty4caQK82aEO+3iiy/GtVbOM/KGIx1GurZBeDKTO0lXIhLKfVKugKIFHuTWrVuzSzQ5rYL38r+7ZJwF56aWIiACWUDA/4LLAnWlYsYIEDhPdSB//liC68k0m7EByxacrqQSBJxRj5LgM8962GijjahtgOOnbCXS2oIKV5Sc8tRwCUSWL1+e1kFCCMMbmq5EJGwLpbKqNzVOyK+WXPq6gBPwv7tknAWEpmYiIALZQcD/gssOjaVlugmUaLsQVl/+tkvxmRGuPmLEiLSkY61Y65NCCF26dPHbLkmn3i1OKcUraUxEQgGDcrM+/e8uGWcpfgbUXQREIFoE/C+4aGkmbcqFQNRW/UqcNL6lPn364GfyjBsKGY0dO7bExokvlv+67dKlSy+88EL/qh9F4idMmJBYz/K/m65EJKzbUszAn74O25qCB0lsC00Mwf/uknGWmJXuioAIZBkB/wsuy1SXuqkRiHK8fIkzowQ4K2WefcYJJcBZTSuxceKL5bPjAdcjRZMoneTpXJ7x8okJlHgXhdOViKQcdjz4310yzkp8oLooAiKQrQT8L7hsnYP0DkmgxEwTuNBCiqmY5kSaE2/umTtkcCAanZj0sNoUzxWyySabpDFXCIWS4jJNnHvuueWZaSIsEK99Gj8elDfwp69Lb64Q/7tLxpn3+HQiAiKQCwT8L7hcmI/mkJBAGl0jCcfJ7E0yNdx99914oTwTjWwO99xzTxIZHH744Ye0Z9klRyslkjzdOKGAUvnnaE3xGZBzJF2JSCh1QD4UDwiFECiHkHqWXf+7S8ZZio9b3UVABKJFwP+Ci5Zm0ibdBNIVVJRuvZKUhxeKrKd4zrxv/W233ZbMqEmIK7E+FXZbWFFUN6Iskr+6UYqF4cMqkPb26QpJzER9Kv+7S8ZZ2h+9BIqACFQkAf8LriL10NiZJJDG7XiZVDMZ2Z999tl+++3n2WecUFmI6LSwsghXT6Wye/Hu1AWnXFIF1gUPS6C09mnczEv6Okog+KuBseiZdGV3/7tLxllpj0/XRUAEspKA/wWXlROQ0gkJ/P777+lKZJVwnAq++cILL1CB2zPR8F1dfvnlSRT2Ju9X3759/a6v5s2bUzU88fRwvFEEyRudvP+USEpXYfjEQ5fb3RITkeCLTUIBqtpT296Pi2IJbCAIK8r/7pJxFpae2ouACESagP8FF2lFpVx4AulKAR9+5ArogY/qlltuwV/lfes3bNjw4YcfTiKDw7x583r06OHJ4WSfffaZOXNm8VlR8ojCR35XUIYKwxcfukKupCsRCQ+F5Boppq/zv7tknFXI50GDioAIZIqA/wWXqTEkt9wJpKt4YrkrnuqAixYtOuWUU/w1K3feeeepU6cmIXfSpEnbb7+9Z6IR3HbOOef8/PPPTlTxzZ4UhscaTmKgrOtSPBEJ3tkkSq+Svu6yyy7zp68jgS1pbAMC8b+7ZJwFhKZmIiAC2UHA/4LLDo2lZUICrKaxpua3TlhxmzZtWsJOuXbzgw8+2H333T27Cs/Wscce+/3334edJ5tbhw4d6k9Utv76699xxx3PPPNMXGH45KyTsPpEp32JtmlyiUgo8RSXvo4yUEHS1/nfXTLOovPZkCYiIAJpIOB/waVBnERUHAHW9Yi78ptljRo1euSRR5JY16u4SaRz5JEjRzZu3Ngz0cjgcMMNNySRwYEU/7179/an+PdkckJhePZbpFPv7JGVxlXduPR12NPUGFi8eHECGP53lzuv7H8wOhcBERABERCBCieAZ2j27NmYYmhCSDs5Hb788stevXr5I6IqXMnyVACziYqW1157bY0aNRiXdbcrrriiZcuWo0aNCqUGVgL1iIg5O/DAA/0dKQxPRDwmIAua/uv5c+7qMuGXbd++vZv15MmTCfY/+eSTWV8OxWHvvfdm2+Zdd93l0tdhk5ELl9X5UEIqYaMl6FCrVq0Ed3VLBERABKJDgLAPTxm9uzwUWXpCLlaColCePKibbbZZls4i7WqTweF///sfAWGe5L322mvw4MHkRfOuBDy56KKLbrvtNhqzbEoNAL+fMqCEnGyGLcWa5qWXXspfCG6CvEz484DiDWuttVaoKVPsAQew+xiTxcOfxy5Ojv/d5W7JcxaHSL9GjgD754cNG/btt99GTrMsV4j95IAl+2WWz0Pq5yYBIqLcxGSZ+R+wq8uEU2fHHXd017Gr8HudeeaZ/B/tb1nmueciIkGXLDMPF95ZEpjhqfVy8GI5Yau1atVqzJgxXrMgJ+utt55/F2eQLl4bGWceCp1EkQCvmz322IMtS7xHSMYYRRWzUyf+KOzQoQNg2S1P9vDsnIS0zmUCMhcSPF3+5yUh2YMPPrjxxhvTjGD/e++9FyfN7bffTt2nBB11KyABV5eJpWQvEQnr7AcffHDnzp2pXhVQSCrNZJylQk99M0iAVwy+erImTp8+nWEwJrbeeus+ffqQjDGDo+aBaLYmUQyOSiyzZs1iurx9OL/xxhsJwc6D2WuKIpAjBHDwnHjiiVSNZJXTLbcR7H/hhRdSSf2VV17JkUlW9DQoo0kxTX8ikldffZWkJK7ue0a1k3GWUbwSniQBiqARQsEav4uJdH9Ds2Y/YMCALbfccsSIEYQFJCk6v7s9/fTT2LhXXXUVJhokHFgWjklB3qJFi9GjR+c3Hs1eBLKMAOFQN910E3WfcOo41flzi/QNXbt2xW7LsslEVV1Wb/AR3HfffS4RCY6DO++8Ez8lIf/4LDOktYyzDIGV2CQJ8ELhtbL//vuTGwYRVapUOeOMM+bPn3/NNddUr16dK6zBnXDCCSRjnDJlSpJj5GW3GTNmEFlyxBFHABMA/Kl9ySWXcO7tqyfsmkJ+7DP6+OOP85KQJp19BO6//37C4Tnefvvt7NM+fRqzwvDcc8/h1CEuykkl/ek222xDyD/7BNM3Tv5K4u9Y6jKx2uC9MInWJZHvdtttN2HChIxwKZ5dw38lQVoO3RKB9BLgJcKrxJ+Ah3fuRx995I0SV/DYhW2yd8lroJMSCVDo7dRTT/VH8HTr1o23jNeY0NcuXbp47xcMYoKLE2fl8fpG6kTvrkg9jhSVIcG6+0wmkENOdteGPO8JmuXPLZw65JX19lIAp1+/fqVNHz+6o0fke2ltdL04gbgXJtlJ+PIq3sxdwUR2kFn5Ka0N1/3vLncuz5njpn8rkgDZjB544AG8xCTg4ROMKk2bNiVv9euvv+7fH+4qipCMp23btrThA/3YY4/xBr/++uvdIl1FziGSYwOTrfKAxcHgUkaRGGn8+PEUPybOzFOZlWL+zn755ZdZ8eQijvp77rmHXsT8KbjYo6QTEYg+Af6ywp3Dn178yzmZe/H0RF/t7NIw7oVJQQV/FdR0zUXGWbpISk6SBDC22BOOawcHDyJc5mtCKFhiK1HibrvtxjYlckDUr1+fBiRj7Nu3L1aF91dgib3y8OJLL73kX9dgU/eQIUPwRO67774l0mApmQXNQYMG1a1blwYEFxPzh3FM/F+J7XVRBEQgmgRcXSb+Zyc8lzdqNJXMdq3cC5O/e7GDMzEXGWeZoCqZgQiwTEnaa2ItSaZMB5YpqRlHzBnB6eQETyCCFToq7tGSqCm3TYnYKaKpiKkisipBxzy55SKCyQDuIoL5A/rss8/mj2l2GCXIgggc1pQxyGh5+umn04srRP7xDjrooIOcqDwBqGmKQA4QIP6sU6dOOTCRyE6BFybZiPyhOGlUVcZZGmFKVFACbA/EFYy7i13Kro8L8H/44YcbNmwYUAqe5JtvvpmUM5gOrsubb77JiieGBfFSAYXkWDO3lx53l7eXnrczBit7i1whkSDzJTM7OZMot0zMn2s/duxYnHAXX3wxwRBBJKiNCIiACIhAKgRknKVCT32TIUBlDALFCFOlai/9scbwvbP1EvssCXHETpG1mTgqoqnoTmTV0KFDiZci1sqFryUhMxu7ECjGTm8mThZKN3E2cBElzU4iLyI11Lyw8Ij5I/LP5WdHJhGByCc60IWvhZKmxiIgAiIgAsEJyDgLzkotUyVA5Vfy0VMZgy2WyFp77bVZwWTny3HHHceaZirSiaMiwIKYKiKrkOM2fpKMkbirVMRmS19Xv4WcI65+C6mPSAiHT/GQQw5JcQpE/rGyyZYLF7niNn4SI0ikYIqS1V0EREAERKA0AjLOSiOj6+kksHDhQqLE2rVr98477zi5hx56KN/6N9xwQ82aNdMyEtFUxFQRL3XWWWe5eCnMPuKuSBJBDFZahoigkHnz5h1++OEdO3Z0ycmwcUkCR3wYleCwfdOiMPF/V1xxBTKJCHQ2NDGCRAoSL0jUYFqGkBAREAEREAE/ARlnfho6Tz+Bv//+m8gw9h4/9NBDJL9gANbLXnvttVGjRpEvI+3jEVlF1mairLxIWDJEMCJVTYjHSvtwFSiQbapkJyKtPySdGpQfZR8rnF25vfTqxuozEYH+1WfiBYkaJHaQCML0jiVpIiACIpDnBGSc5fkHILPTJ58WoWB4cZYvX85IRJqTQItIczw9wQemJAD1SUJVfiTKilgrIq6aNWvGQMRLEYlFvBRRWZmrthF8Rim2xMZ95JFHsHfxO7oEb5tssgkp3/BKsuAYXDgOxbDlR+L2bRA1SOwgEYTEEQYfVy1FQAREQAQSE5BxlpiP7iZJgIAn4sCIeZozZw4iWHM8//zzWXMkLsqtOQaXS7Fz8oBj5GFsBe9FS0YnXxolvYnB4lfisRid7ZxEaIWSE6nG06ZNw0NGlN6CBQtQjJJWpHljAffoo48OqyfeRDL0UMQ3VPkRl/GEEYkXdCunRBASR0g0ITGFYXVQexEQAREQgeIEZJwVZ6IrKRGg4hixX/6KYyTK+uSTT/BdueymoaRjgjzxxBN0mTt3LmFqLFYiKrgErAcMO+KliMRy8VLsG8BvR5wW0VrB5UShJSiwybDMsM+cPqR2I24P31WNGjXCaghGtrjSa+bMmX4zOqAcIgXx2zE6D8V1wW9HTCGRhcQXBhSiZiIgAiIgAiUSkHFWIhZdTIYAK4bk02L1kH/d6iFLb6TI8uoCJSGUUCdC3b3KjwSr7bDDDoT8//zzz8GlEYNFJJbzOblexGkRrUXMFpFbweVUVEuWdPv378/qIauZLm4Pd9ekSZOefPLJJk2aJKcVW1lxdO2+++6uOwvQpKzEkHUL0AFlEjUISR4KUX10QTc489BZhibWMKAQNRMBERABEYgjIOMsDoh+TZIAS2N4y/CZ4TlDBLVgb731Vrwy7JdMUmJRN4wSKj+SFIMTrmH2ucqPJM4IVflxp512wrvz6KOPEqGFHKK18P1gSXgWT9GA0fo5evRo7Ej2S65YsQLNNtpoIxK5TZ8+nf2SKSqKmUva3pEjRzZu3BhRmIAYVQAZPny4MwEDyscTSRwhD4WYQrpg3rllaAy+gBLUTAREQAREwE9Axpmfhs6TIUBUGdFdLI0RZ0Z/aitR0YKVxIsuuiiNdS0OOOAAVuJILevWRpcsWUIQGw4btzYXXG+io4iXIk7LVYjy1grZ5xhcSPm0xGW49957k2ns66+/ZkRgghSw1CEFcrp0ICMGOwOuvfZatzbKouSJJ57oT3oSZCDiCInnI6aQh+IqRMV9KoIIURsREAEREAFHIG2veAHNQwL4SNiJSai+5yNhmYzFMmrB4uBJOxCsE2LYsU4o0OSsE2Ke9ttvP8o3YRYEHw4rhDgtLJIePXq4Xqx47rLLLscff7yLsg8uKkMt2btw5plntmnThhz9bggckLghcUbikkz7oOwquOqqq7BZjzrqKCc8Ll1wwBGxm4ksxIYmytB1cf5Uth04f2pAOWomAiIgAnlOQMZZnn8Akpy+F11EDjMXXcTSGCFQLJOxWJak0GDdNtxwQ1f5kTLnroer/EgR9FCVH4nWIlMXkVvEbyGHGZHHi5VTortCpe0IpnXQVizUDh48mLg95uji9sglRtAec2TBMaiUpNqx2ktGjLfffpsNrU6AK7R13XXXuUJbAaXGKcwsSNjhIhFDLUMHHE7NREAERCD3CMg4y71nmvEZxe3LwxHFohiOKDYPZnzsogGIbyMjxtNPP+0qP2Ig4lXCAhg2bFioyo9EbhG/RRQXNh+yiesiuosYLyK9ioYqv59UKydO/4ILLnD5cos7ospBFTJivPfeew8++KDLZEuCWZxqAPFK1AfUIc7V5/bwYge/+uqrASWomQiIgAjkLQEZZ3n76JOZOBmtyKflz2jFryyH8f3N0lgyElPrQ0YMVjb9lR8JdyPwH/dPcMGskBLFxcJo7969XZAcMV5EehHv5WoiBReVdEvWart27UpcHTYuQoqHcCUtOYmO5Bwh7AyV/ve//6211lpImD9/PqFpGLIUbgousHiQHFGJnTt3Pvjgg2fPnh1cjlqKgAiIQL4RkHGWb088yfl6ueBd1jGkkIx+8uTJJKZ3mx+TlJtyN1f5EQOxV69eLpMZOwcJfevZs2eoyo/Ecg0cONC/vZR4L6K+iP1y1cRT1rRkAdRov/jiiylpwI5U1yJu82PJ3TJ/lcy9bN4ki2+3bt3caBQ756GfdtpplD8PPr7bXkoQm7e9dMyYMaTt8OpGBBelliIgAiKQJwRknOXJg05pmnFVFFnwYtmL7Y0dOnRISW76Ojdq1IiMGG691UklAI7gp2uuuSZU5UfiuvyJ2YiXIvaL1VLiwNIeL8XyKzsnGBGjkAJTqB2XNix9eJKX1Lx5c3Z7sBaJOYWU4joHFE0koj8xm6u4Clg+SKHSdgQcTs1EQAREIKsJyDjL6seXceVZxsLhwZKW80KxyIXDgwUvlr2cmyrjGoQZgB2XU6dOHTFiRIMGDeiHt49gOEw0z9sXUJgraTBo0CCXtoMIMOLAiAYjJiyghDKbsXPC74UqnnC/TAnl2WCfffahlvwdd9yx/vrrM25xb19AZeJKGixatOjkk09mGRoXbEAJaiYCIiAC+UBAxlk+POVk5sjSFZFYGBAsZrn+hAqxyDVgwABXqjIZoZnvg8lIjSPMRypyusqP3377LYFxu+22G4H/wccnWRcGmb8YKNFgxIQRGYbw4HKKt3TxW2w1dfFbKHzssceyLOuVqizeJQpXAEJGDIDwr6uOCgcXJ0fYX3ANixcD5bnwdHhGPKngctRSBERABHKYgIyzHH64SU6NJTYW2lhyeuCBB9zOR5a0WNh67rnnWORKUmj5dsMRRUYMjIbu3bu7kfHN4KHBT4O3JrgupLwn8T1BbHvttZfrRWQY8WFEieE9Ci7HtSy+83HnnXeeMmUKKTyoUhVWWoW0x3OG/4z6pPjSnAJ4E0kF7O0wDagVcYpEK/JQsP5dF7yb+DjxdIZK2xFwODUTAREQgewiIOMsu55XxrUl4gpTDOPD5Qzjy5hCmf4v44xrkL4BCOEiI8bEiRNZkUQqsU1EOGF0ernZAg6F8cHmgGeeeQaBdHHGK7FiRIwFT9sRlzMMawybDMsM+yygGtFpFmesF8/NFlBVYhaJXKQcp5e2gxhBTDTiBQNKUDMREAERyEkCMs5y8rEmMyn8TMRaedn2vWWss88+2y1jJSM0An3IiMEC4t13373BBhugjqtqgHnBnsFQ2pFcg1VdynGuu+66dGTZl32L/mXf0qTFZdtneykrmKwJspoZwbi90mZR/Lpb5mZHp1vmLl7VoHiX4lcgcMIJJ0CDWEa3DE10Iztt2W+Lw7J4e10RAREQgXwgIOMsH55yGXN0Ae/4h8aNG+eaxgWAl9E/8rcxLsmIQbzUeeed5yo/kmcL24KcW64eaMAZFLervA0TRJIVF1K8TmWchVe8S3ZdYYMIudAwrbwNInH1QANOB/OOWEaeBUVaXRcy1bEMTda6UGk7Ag6nZiIgAiIQcQIyziL+gDKrHqkiiKnyp4ogqozYMi91QmaHL1/p6623HhkxsB4ox+lGZprkrD/33HNDVX4sviJJqhFy6JOJ10vbQQEoXEosfQ4fPtyliohbGy3fqWd2tOKpVVhNBgi1Fqi4EHxsPnvPPvss5TiJ6qMXS8bUe+DDSe0HVyIsuCi1FAEREIGsJiDjLKsfX0rKuySrZ511lkuy6iUdxaWUktxod8ZoIIb9hRde4FsfTYmXIqiOc+o/ulqWAdWPi+UnjJ0alJTmJLYM65Zl08suu4wlVKQV31UQcIjsasYKLwH+TN8lJcY8ZU8GQAitC5XJrFOnTqTt4KG4tB3EPlI1FXONaMjsAiJtRUAERCBpAjLOkkaXxR0xTUiI75UnIu4nrlxPFs8tmOrkgGARDZdM7dq16YHnjAwR1OvEbRNMQKyVy4LBoh4xZKx4coXyVscccwxbROfMmcOv1C+Ky8cR65bTx1FHHeUv57VgwYLjjz+e/HPTpk0LPm+WoYl0ZOkZp6ZbhmZJmmhI/n5gS0dwOWopAiIgAllKQMZZlj64lNQmFt5txkSK2zHHd57bMZeS3Kzq7Co/8q1PYBPlNdEdc23fffcl7MmZVgFnw/4AdgmwV4B4Mn8XdlewhOplsvXfyu3zGjVqkBGDtHCknHUzZUtm+/btyT+HrRZ87ixDDxkyhJ3CPBTXi0VS1j2DS1BLERABEchSAjLOsvTBpaQ2Gf9df3w8/lxTKQnNzs5UfiQjBhsq2R7oZkC1opYtW3qLkgGnRZYNcm2wAOfaY4i8/PLLZIUI2D33mjVu3JiMGBRCoHATs2Nlk/paBOFhyP7555/B58uzGD9+PAa06+IZfMElqKUIiIAIZB0BGWdZ98jSoDCb7JwUQqPSIC77RWBAYEZgTGBSMBvCz104Pym4QsVLtW3b1sFgIS/7qaRhBpi8GL6YvxjBiPvtt9+uvPJKwv5GjRoVSrrbIkAXl8ckVF81FgEREIGsIyDjLOsemRTOFAG8MizGsSTHwhxjkAjjpJNOateuHfXUMzVkHshlyRi/F8vHF110EUvJzHjevHmHH354x44dWbLMAwCaogiIgAiEJiDjLDQydchhAlR+JCMGIe0EtrtpxqWQzeG5Z3RqbLxg+wVRfWzFcAO98cYbOBrPOOMMt1k4o6NLuAiIgAhkFwEZZ9n1vKRteRAgGQQpIfzReK74Ur9+/VT5MZUHQMoSdgqTyoSVTeSQu+S+++7j4u23305RrFQkq68IiIAI5BIBGWe59DTTORe+Qdkux0Gh63TKzR5ZcftYSTB79dVXE+NPytnsmUQUNSUJMPtYMcj4dKEfBSouvPBCf4GKKCotnURABESgHAnIOCtH2Fk1FJ4MvjU5yCaaVYqnU9niGeCo/EiO2XSOkZeyyF52/vnnE4hGWS1XuZVoP/4eyEsYmrQIiIAIxBOQcRZPRL+LQBwBf+0E9gqwkTOugX5NjgCl6ClIT31SNgdQRwHHZHJy1EsEREAEcoxA1Rybj6YjAhki4KqOUuB80003zdAQ+Sm2devWr732Gi7JunXr5icBzVoEREAE4gjIcxYHRL+KQCICTZo0SXRb95Il4DLMJdtb/URABEQgpwjIOMupx6nJiIAIiIAIiIAIZDsBGWfZ/gSlvwiIgAiIgAiIQE4RkHGWU49TkxEBERABERABEch2AjLOsv0JSn8REAEREAEREIGcIiDjLKcepyYjAiIgAiIgAiKQ7QRknGX7E5T+IiACIiACIiACOUVAxllOPU5NRgREQAREQAREINsJyDjL9ico/UVABERABERABHKKgIyznHqcmowIiIAIiIAIiEC2E5Bxlu1PUPqLgAiIgAiIgAjkFAEZZzn1ODUZERABERABERCBbCcg4yzbn6D0FwEREAEREIESCBQUFJRwVZfSRyBzhGWcpe8pSZIIiIAIiIAIRIDAokWLTll1RECXnFXh2WefbdGixVdffZWJGco4ywRVyRQBERABERCBCiDw999/33rrrVtuueWwYcMeeuih9957rwKUyPUhP/nkk06dOh166KFffvnlhRdemInpyjjLBFXJFAEREAEREIHyJvDCCy9ss802l1xyybJlyxh77bXXxowobyVyeryff/75rLPO2mGHHV577TU30YULFzra6Z131fSKkzQREAEREAERKB8CJ5xwwm677cZYO+64Y/mMGNlRPvvsM1w448eP9zQ8/PDDb7nlls0228y7opNUCPz777933333Nddcs2TJEienfv36/fv350NYuXL6/VwyzlJ5WOorAiIgAiJQYQS2WnVU2PDRGBhb4eqrr77nnnuwHpxG22233e23377XXntFQ8Fc0GLcuHHYvp9//rmbzFprrXX++edfeeWVtWvXztD0ZJxlCKzEioAIiIAIiEAGCaxcufK+++676qqrWGtzw9SrV+/6668/9dRTM+HLyeBMIix61qxZvXv3Hjt2rKfjQQcddNttt22++ebelUycyDjLBFXJFAEREAEREIEMEpg4ceIFF1wwc+ZMN0a1atXOOeccDLW6detmcNR8Ek0k2XXXXTdkyBD2WLh5t2zZEpfkvvvuWw4YZJyVA2QNIQIiIAIiEI7AihUrwnXIm9Zz58696KKLnnvuOW/GBxxwwKBBg1jj9a6EOvnnn39Ctc/5xv/999+DDz54xRVX/Pjjj26y66233rXXXnvmmWdWrRrOavLWmsNCS38UW1gN1F4EREAEREAEPAI4Km6++ebvv//eXSG459dff/Xu5vMJBmufPn3w33iWGQbZiy+++NJLL4W1zNjFyaZOB/POO++cMGFCPoP1z/2tt95ifwlLw84yq1KlCtszWdw899xzQ1lmmLysftLRCV++fLl/lDLPZZyViUgNREAEREAEyonAmDFjWrVqdemll3rjsa5E1q4HHngAf4Z3Md9OyEQ/YsQIOAwYMOCvv/5i+nXq1OG7HxurS5cuoWh4ySDmzZvnOv72228s1R1yyCFz5swJJSrHGn/zzTc9e/bcY489PvzwQzc1kpnNmDHjrrvu2mCDDUJNFouZnCY4OIkLdB3feeedUBJknIXCpcYiIAIiIAIZIUAyiM6dOx988MGzZ892A2y44YYusB0fBp4M/Bl4NTIydrSFTpkyZeeddyZlww8//ICmMDnttNNwybB/kFCz4LqzxIalu8UWW7C10xkNNWrUwMhzEp5//nl8cpjFYX08wRWIbMvff/+dHa9bb731k08+6ZRs1qwZBQBwKGJjhVL7iy++YJW5a9euXuUAkqKxSyOsDS3jLBR2NRYBERABEUgzgV9++eW8884jAcSrr77qRG+//fZvvPEGNtlHH320zz77uIv4M/BqHHnkkXg40qxBVMWxtturV69dd93VS/S/5557Tp8+nU2aWK6htCYZxLbbbssasUvTRTIIljWx9n766Sfi1dw2AregjH+O0gKZqxoZSu1yaPzEE0+wKNyvX78//viD4WrVqnXjjTfypwKuxFCjL126lC0arVu3fuWVV1xHzslV+8EHHxC+VqlSpVDSZJyFwqXGIiACIiACaSOA/4Y1I3w5d9xxhwudxubA8sD+wAphGPwWWGyEWDVv3tyN+tRTT+HhwM+BtyNtekRP0J9//om7BaPhsccec3ZSkyZNnn76aWxWLNdQ+uJjI/vD/vvv76Xp4tdPP/2UwD7SdBFHhUlBmzPOOIP4KiST8v6kk05q165d2JW4UFpFofH777+P4Xv00Ud/99136IP9hHsSj9dll11GcYXgGvIxvvfee/kYDx482H2MWQYlYy1/TnTs2DG4HH9LGWd+GjoXAREQAREoJwIkg8DOIAEEnjOGZIWOdTqsBNbs4tJ0sdaJMUG4FV4NWuLhwM+B4fL444+Xk67lOwxGGAZo3759iQZj5HXXXZecDqyXkfQ/lCIkg8A9hoHrpeli4RIXGoF9cWm6SJDGWic+Hi91bZzhEmrc6DcuboC2b99+2rRpuAw33njjUPq//vrrbdq0YSMnPkg6YuzinuRjzBVn7IaS5jWWceah0IkIiIAIiEB5ECDwvHv37qxXemm6iMghtp0Idy8EKk4PPBmEQ+HVOPHEE90KEd6OY445Bs8HZkRc4+z9lfBzXIZHHHHE/PnzmQUzZY5U1yYZ/TrrrBN8XmyeYAsFvhyKoLs0XSSDIOCMZWIC+0qTw7onpsYzzzzTtGlT1yZuya+0jll0vfjS7SabbPLoo4/iJtxpp51CTeTrr78+7LDD9t57748//th13G+//TgnFxq0Q4kq3ljGWXEmuiICIiACIpARAgSbs2bEfkx/MggyQbC7DU9YmUPi1SAB1bvvvtuhQwfXmO9UFuBYhsMXUmb3KDdYvHjx6aef3rZt2zfffNPpia3A7LAbGjVqFEpztk3QN+lkEBgcRFzdcMMNeOwY1wuWZ0E5lBoRbBy36QF7F/ckti8WcCht8WgSRoYbcvTo0a4jdjBV54k2a9GiRShRpTWWcVYaGV0XAREQARFIGwECp4YPH06w+U033eSSQRCE7pJBsLst1DBs25w8eTLBWPg86IhkVqOcZOclCiWtwhu7hFh8uw8dOtSlC2nQoAGsWGXbZZddQqnnJYNggdJ1TC4ZBFbL5Zdfjp/y2GOPdX5KJLMVw59mIpRiFd6YZfG4dCE9evRgpZj1cbasBlePD9sjjzzCh42S58QF0pG4PdyTyGeHZnA5ZbaUcVYmIjUQAREQARFIiYBLBsGKpPNvEVKGl4jv/rDJIPxKEMeNz4OCRdWrV+e688nhzMA74m8W8XO8huzpIyGWS7TL6i2eRcgcf/zxzioKqL/n30o9GYQ3YsOGDR9++GH37NzFuAStXssonxDRSP5Y9gJ7iXaJdJw0aRKOQPZYhNIcc5nQtOOOO27BggV05GN8yimnEF7G4wuV0yTIoDLOglBSGxEQAREQgWQIuMgwViH9ySDw67C7LWwyiOLD4/OgqA7+D4K03F2i2ciAgI/Ei2Yr3isiV1CbSLsDDzwQE9OpRBwe64nkcahZs2YoJYkMYwOBlwyC7sklgyhxUFKsYZ9hpWGr0cCLZhs4cGDE6z6xiZLiB7gk+dfldeMjh3uSvcC4AEucbGkXscawydymAddm9913J9jx/vvv32ijjUrrlcp1GWep0FNfERABERCBkgn8vXTpzft39e+p3GyzzVwyCNwYJfdJ6mrjxo1xFxGqRbZPJwAfiX8faFJSM9iJhFi4DIm+f/nll90wOM/Yu0oAE7lPQw2MnbHbbrvhRPz222/piLMtuWQQiQdFLOubGJGsdbocE+wDvfjii4kd9PaBJpZQ/nf5DPAxw2fm7QXu3bs3Xi5C8eL2AifWjbVLVjBZx2Q10+U0Kf55SywhubsyzpLjpl4iIAIiIAKlEnjjkj4tNqh36bgXXTYyQsvJ2kWerbDJIEodoNiNOE+Gl0ENr0nSxaeLDZLqBbQiixu+HDb0ObcTCbHI9EZCLDb9hZK+aNEitkEQ+E/4nevoJYMgZC2UqICNccixS4CHeOihh7ou2DpxGdQCispoM897ShyYGwj3JJ5UXH2l7QUuTZ9Ro0axUE7sv8tpUtxTW1rH1K/LOEudoSSIgAiIgAgUEZj+oVVq99+tA+b+F6sqSFr0Y486CqcL33ChkkEUiQvx08UAEbDlxQC5eCO8aF7tgRDi0t2U/LFsxiTXq5cQi7oI2DfU1Q6VEMslg8DC8/L4s52TTZ1YaWGTQSQxRbJsYLKQ+B7Pn+vuag+QyRaPYBIC09iFuEPyrfjjDlntxT2Jew/XV6iByIhB/lj+liBfhut41KqPsRfjGEpaEo1lnCUBTV1EQAREQASKEfhxsR18ou3Yxuw9vEDswNy1ao3p9w59+PHHwyaDKCY6xAW8I+yew1OCv8R1w4MSV7UzhLh0NKXEOF/zfNmTZszJQx++/kkoHzYhllcY3lXAdMkgsEdJBhFqA0GK02IuBA6St5bstYjCN8lcsBe9qp0pyg/b3duxS9kDt2OXvcCUpSJ5HqURQknDdCZ/LHllMaZdR7c7mIzHbndwKGlJN5ZxljQ6dRQBERABEVhF4J9/rN+tVn8jGzPcI/L8bXe+9dfyHU4/1btSnid4SvCX+PNOxZk15aMMy2HkjyX3Fd4mNyIWDAmx8DaFTYjlkkH4C8MnlwwiXRPH24cXEM8fCfFJi49YzBq8gJg1ZLJN1yhB5MTluvMUw5nnFAsihDaYmCw384DYreI2EBTPqxdQVOrNZJylzlASREAERCCPCTw71tba2K6+ZDWCM/9ny5ZXu/DsSpUr+CvGy9juL+zNty+ZbF1GsdU6p/vMS4hFnJaXEOuWW27BpRc2IZa3OJt6Moh0z9IAi0Hjd1DhESR+jky23oJg2gf1BLIXmM0Q/ioRlJ/yu/S8lmWeYMezLYONGm5xlsLw//vf/3BJehUpypSQ3gYV/H9OeicjaSIgAiIgAuVH4NPPrV5HO/Qgs1hxzNixzYE2a47dfZPVCpcMwvXOxL9erUOvsDeh9CeffDJ1BbxQ+rSPSw2DuIRYjIiTiR2OfOsHH674toakk0EEHzSJlsVDu9h5imuQ3Z0rVqxIQmCZXbz6qqQRcY0JhqPwFE47LxiuTCGuARYY5jKZkElu4q7gniSnCdmSXS3XgHLS20zGWXp5SpoIiIAI5AGBJUvtuHNtm5b28xtFs21sr0ywT8ba5s2KrkTop1fYm2App1ZcEop06UpCLPLHktmfhKVOJqkuyPFGpcuwCbFcMgh/YfjkkkGka2plynGbIgn4c5siqQNBujVyqZAjzSWhKFNCwAZkTsEcvPrqq729wLgnMadw1wWU4JqR+Je9IxSGp3qYu0JyEPaOUFusefPmoUSlvbGMs7QjlUAREAERyF0CK1fabXfb+uvZI3eunuStd9q/c22/TquvRPIMnwrbDAn/wsviFHTpW8lkiycmRZWxRUiI5bdFSIg1cuRIsuoThhVKePFkEKSrTS4ZRKhxU29MonzMHXxRXjqx4tZqKqOQc4T8sT179qScFHJcAjaGw0UXai8wi9rkj2WBmwJiLqfJ+uuvf8cdd7BjY5999klFw3T1lXGWLpKSIwIiIAK5TuDV163q5nbR2avnecxZ9vMvsStVqqy+GO0z0nSRrAtfi0vEj/flmmuuwRPjFT5KQn3WH0mTQboQt4pHQixkskxGPcpQ0ooXoXIrhrh2wiaDCDVuehvjIyQRPwn0ST7nJMet8yY3HJY0Gycxdl33uNIFwWWSr5iHddppp1Fsnl4sfOOeZNGZf0PlNAk+YhItZZwlAU1dREAERCDPCMydZzt0s857m80rnHmdPe2TT+3Ru2JetGw7SHOPr4Xsa9TkcRkoXMlwjAmvZHioOfGl7uXXJSEWZhmLbq7oZ0A5XjIIQp38ySCIrw+bDCLgiJluRsEGzCBMXjyIjFV8h0RYBdje4TZ2FC/6GVDU/PnzqfS15557zpgxw3XBT8Y5PjM8ZwGFlE8zGWflw1mjiIAIiEB2Eljxm53Tx5o3tRkvFE2gro0aY0vfiMWcZfPBd/yIESOmTp2KD8bN4+233yaPK9Wsf/zxx7AzY3Nft27dkEBCrE033TRU9+LJICgMjy+HZBBpr6gdSrHUG2MMYapS9xNvItKK5xYJPgT204ABA7CqWceknFSovG74R8kfixuSAmJuRKLKnn/+eSLMiDMLrkO5tZRxVm6oNZAIiIAIZBWBggK7f0Rs3+VdA1brffUA+2vRqh2aq69l9RnbNuMKew8bNoxoJALbnQcr4OwwPviyJ61DwPaumSsMXzwZBKm2XH7XUNKi2RgPYt++ffFTkvbCaVg8K29AzQllYz2aamAB29MMj91jjz3GovB1113ncpqwBxP3JBsIMKaDyynnljLOyhm4hhMBERCBbCAweapV3tZOO2G1rgceZwsX2TWXWphkEKu7R/jMiyv3akxR2PuSSy5hHx8JYzOkuJcMAk+bG4LC8Mklg8iQhukVS3p9jCTSlxA35iTH1bNK73BOGvtkMXx79er1/fffc4UHTd4yHG+4OUPlNMmEbollyjhLzEd3RUAERCCbCXw1y264zTofY5Wa8dVklZrHzrkya3aiWeEz2+0Qs5lFbdrYtPdt7IhYDYDcPbzq7F5GBhYWca4Q6sQGgvTO+6mnniqeDIJRvKHTO1x0pHXo0IHNASQBJvk+WhWvBJ8uVRcuXIgd5jYNOJlYadhq3tDpGihDcmScZQisxIqACIhARRMYfJ9ttaVdeZG9im/G1W+eGzvnypZb2F0PlKofZtxzDxbeHf64FUy3dm1LbZxbNzz31XbbbedmNn78eHJwUKRoyZIlqc/VJYNgF2eKySBS16SiJHjuK4qUO/cVSflJzQ9k0vSnrhU5TQhNY2F6+PDhLr8aTjvck4QDskkzdfnlI0HGWflw1ii5QIDc01QXznTVl1wgFXIOROoQShKyk5onJLB8RWxz5QVnrGq0ll1yjb3wirHjcszLsXNblfbinFNtp0OMeP8Sj4O7xALOfvvdjj+qxPu5fdFVAfICv6i6OGTIEL7v7777bld1MYnps8mAz3lakkEkMXrUuhD4hQlF4Bfp+J1u7BsgTT+ZbFl2TFpb8se2bNmyT58+LqcJ4W7sAyDcjS20ScuskI4yzioEuwbNMgIUiWO5gYJx/PlFpu8s0z7C6rpazocccghUMdEirGm2qXbeFUWbK3e2xQvs5qut637WtIkdtH/s/MeFZjvFpvT+89b76lLndspxVqN6qXdz/UblypXjtkz+/PPPZ599NhkiyGQbavakOR04cCC2HZ9z99dd0skgQo0b/cZsmcSc8m+ZfOmllwj1I5Mt6ftD6U+SXvJidO/efe7cua4j7kkMPjIMu42ioaRVeGMZZxX+CKRApAnw5xc7tykSR6k4p+jLL78caY2zRDlXy5mVI6+Ws8Cm7dF98ZUNH7JKWlP7+y2rt0G85A3r2d+TzTaJXb9/oBGXpqMUAiTWGjRoEMnGcOq4JhT57tSpE5lsPSOglK6Fl8eOHUuyBqpqssmAS16KtbDJIBKPktV3MapIzX/nnXe6ZGPYsiTux5YliX+QlQpnNG+//fYTJ050HFyKNcozuBRr2QhHxlk2PrXy0JkPNwv2HN4rqTxGjdIYBCtQEo4N2JSHI4gB1VzgwrPPPhslNbNPF1aFeAvz5uVft0IEZL7AWELKvslEU+Ou5xbqMXonKwAAOz1JREFU9XB/q1atZB25/mBRgoxu55XcRleLCBC8j0eHNP1UZ3LXeA/4l8+KGq7xkwB/8scedNBBbCxwN+KKE6zROr9/IYsvXkmXpp+U/cAgfT9J/IkSI5NtaWxYbiZ/rH+5meIEmHT+4gSl9Y36dWz5BAffTzpyj8CkSZPc5/LKK6/MvdmlZUbkPSL7kfd/rwtcIH1iYuH8oea6EJiSuGXe3vWvX8Cqdu3aLptUYiC33367A0tVxAQt/a+yBM1y/NbinwrI7RT7r1bBypWJJvvvvwVWvbDxL0sStdS9IgJkPmOB0hX2dp/JBg0a8EcsDp6iJrGfOIbZQOCMDNfMlfX0t9F5aQRYoNx3330dN/dvjx49SI0W137cuHHYx14z9hbgnmQxNK5ZVvzqf3e5c3nOvCerExGIESAdDssNbr+3I5LVgQvReahxtZwJ6CEPO38oE1yS7TnQowM5psnnXxbqs/eBVjnhG55qmLt3LWzs9Sr8XT9KJsBntXfv3nxucerwGabRDz/8cMIJJ3gpG3AG33PPPfhyBg8ejF+HBuSS5QpVoTp27FiyUF1dkwCrwOyQJQh18803d3dI64/zktB+Ev1zZfbs2S7FCfsJXIOuXbti0t1yyy38vbemsGz9LeH/utk6KektAskQIHk0uadZtnj00Uf5YwsRbdq0waOe1YELyYBIdx9qObNnnr9xvZB/Khiy7sDqA2sQ6R4t7+V9WmSctdisbBbbNC1s81lRr7L7qIVtuOGG99133/Tp06nS6HC4ZKf4ewhmP+uss4iC4jqeM/xnWHJnnHFGdCpqZ8vzw/xiwxCp/NnXic68n0nxj9WLHYb15iUHJiCYBBz8yq1smVoQPWWcBaGUs21IUZ2zcws5MRJz8z8567ysXdLVBS7wwsWMCC7JmXS0T3qzffCxsqIlQB566CFCym6++WZXCYf4XAohY/IS1Bh8CuIZnJV9UmRmbbFZ2b28Nh9/UXZjtViTABHo5LgnnWyTJk24w6ed3S1sD3StiDZj6wAr8q5W95pd9VsgAqxUksof65Z0smRHo8+CBQuI/HMvk/XWWw/3JHs1yBIcSFx2NSq+0um/wqdNR44RwLvO7mX3KWXfEN+UOTbBsNOZMWMGOY28/22TDlwgko+/p50cXOvsPQyrSY6199dpAQu72dnTzqpE2GmyV8ADiwcuQXe9u2Jwjji9MIzswZgDuIxj6PDCxsecVUZL3S6dAH/l4tTxIswaNWrEh7b05rqTDAF87SxluBcshtqZZ575008/JSMokn387y53bsUv+a9EchZSKkkCixYtIsrHxUl4tggnOIew2JIUms3dyAlJHiM/EBzm5D8MOyciVY844gg/UndONkWSH4aVlgPtv/3227iUjxQ85mLYqbnNbn6wJAFOIETvrhic6wcW2lt9+ydgVXjrsn6FjW+8vezGapGQAG9R/gIhNAq/TsKGupk8AV6q66yzDvvokxcRyZ7+d5c7l3EWyQeVbqV4WbAnzh8pSVYIlu29rz0XnY31lu6RIyoPIOTR8S83uMCFsOqyBtq3b1/2cnokmzZtyiqe9yvhwwS8U5wkrOQsbY9jLC7lI/nQcaGFnQ51ci644ALPFQFP/mgmgSf2dAJR/hdcgmY5fmv0C4X2Fi60Mo/uJxc2HpPvjt4yUQVpwIuFI0hLtUmOQK4S9r+7ZJwl99nIvl5ESvrNBbL8kRiGbUTMJLm8BtmHYE2NyVfkJSviW98FLpD2cM1WZf/22GOPYeN6dphL+e121BPBhpXm3SKCbejQoQROlS00m1vE7ZygsDE1huNSDJQ5Pz6ZbG1jg5tHz+UQL7MjDfwvuCDtc7PN518W2ls19ih7grZbYeNZc8purBYiIAKZIeB/d8k4ywzjKEllm7E/UhI/xDnnnMM2Ir+OfBd6eZnd1yF7XrDn/G1y5pxY3S5dunjf+uyfSi5wgY0C7du39+TgZr/iiiuoJeAHRRgKez/XXXddrxkh8MSl+dvkzDkLOv6dE8TtERzGKybsBKmKQzooj5irvscurYBy/C+4gF1ysBl/ZhTmObOCxCbXV7NWt1z111oO0tCURCAbCPjfXTLOsuGJJasjq0JxKRCpj0EamNLkYbFht/lXkbDqsO1Ka59111lYvPDCC/35tCiUyTafsBNxOY3cviFnQ1Bzk8qbpclxWdP87YlOK55NsbTu0b9ePJCRMsZkIQqrOZVwIOmZZRAjdxS0Q8nxv+BCdcy1xncMLbS6Dj0l0dQOPrGw2ZD7EjXTPREQgQwT8L+7ZJxlGHYFiccTRnr65FaFXOFY7wsSWw0LDzuvgqaSnmFZTCQjkbfdj9k1a9aMQplhpeO/GTBggMu44xDh43n99deDyJk6dSo5Kj2wxKgRqVZmvYEgkiuwDSWt4lI+EsXIQnlYlVz1UryPHh8yAOObDCuH9v4XXBLdc6cLC+jWtNDwGjWm5Hk983yR26xZGYUESu6vqyIgAmkj4H93yThLG9boCGJVqHXr1t6XHJYEGfz4Eg2l4XPPPefl2kAUdh7WnotRCyUnCo3JQkQuIg9IzZo1+/fvH3yZzJsCdfTimFAIMhQTQq/YYURcmqcM8WpErXlDZNdJXMpHAhlZHA8FhPmmnYn/BZddPNOv7YQ3imwvK+h3a4H/JcAycZ/rVt+dmJtL7elHKokikDEC/neXjLOMYa4IwawKUVLX++JnVYisfWFXhTzFsee8vMxOZtYVhmOp8fDDD/cDOf7440lg6M0x4AlpJDt16uTJYWGUXYRJexPT6CUKqH/amwUJZAwyaHFvIrVZUvQm+l9wQXTI8TbTPyywequNMGtdsHfPAtvCd6VRAW10iIAIVDQB/7tLxllFP400jU9tnD59+pBO1jMgWBUiX1/q4rHtvLzMTjj2H1Zg6pIzKgEDiET//mWyXXbZZdq0aWEHJcPh2Wef7S+6QspvMm+FlVO8fVriq4qLzfQVajmfd955/sBEitUkCGQsTZ8S4/Dmz59fWvvg1/0vuOC9crnl8hUFXY/3WWOuIPqqf3udXfD7H7k8d81NBLKHgP/dJeMse55bKZr+t3Llww880KBBA88sY6Xs8ccfL6V5kpeJ/sHa84bACrz88suxCJMUl8luLJNRFpP03J62nD/yyCNhszmQVmPIkCGk2PDkkIuEmiHp1Z14tVR2JqZXmcTSXCDjBhts4AGhIDGFMhP3Kn63xB2slHIq3jK5K/4XXHIScrbXnK8LnhgVy0/7/EsF336fs9PUxEQgOwn4310yzrLzGRZp/cG9Q3etWsP7siTGPPVVoSLZJfzE5ovL6TVixIiwRk8JctN36d1338VD5gHBc4b/LC69RZDRxo8fT4luT06dOnUGDhyYocSSGD3Fc3oR3xZEz3JrM3HixNQDGdG2eO43Cp+nN/eb/wVXbnw0kAiIgAikSMD/7pJxliLMiuvOH757HLa+Zz6YHXnkkWlZFUo8JeKBsP/82fDbtWtH5FDiXuVwl0gy4sn86SqINkuQ3qI0lSiv261bN48rVRNOPfXUxCnpSxMV6joRbHHZ8IlyI9YtlJBMNGb5tXv37h4QCJ900kkLFy4MO9ZHH33kr16auaoJ/hdcWCXVXgREQAQqioD/3SXjrKKeQgrjEiNy6bUuguSeVd+ZO1VZe+rQB1KQGLorVqC/jiRf2MceeyxRRKEFpaMD+y5vvPFG9mB6BsR2223HDs2wsn/99ddLLrmE1KmenD322OPDD8s1VjqujiSxbkS8xWUMDjuvpNsXD2TcddddkwhkXLx48RlnnOGP28tovVH/Cy7puaujCIiACJQzAf+7S8ZZOcNPebjHni6wKv7Y3lHHnbwyfNGhlPWICSBOiHz3nilDHvzrr7+eiKK0CA8ohFxlZCzzdCCTGfnMwi6T0X7YsGH169f35DRp0uSpp54KqEPam40dOzau1hbRb0mUlkpaMZaqWbD2BzJuuummSQQyovOgQYNSr14aaiL+F1yojmosAiIgAhVIwP/uknFWgQ8i5NCxLfE7+82ygpMuKFj6a0gpaW6OWUPMEFUjPbNms802I64ozcOUJI7M/uT398ZlmYzs/0kUF3/77bfbtm3ryalRo0a/fv3K2cQsPj/i24hyI9bNU4wYOCLhirdM+5UpU6awVO2NyxL21VdfnUR6i5dffnnrrbf25GCi3X777eVgYvpfcGmHI4EiIAIikCEC/neXjLMMQU6r2B8XF3Q/eQ2zbLN9CyhsHJmDBcGLLrrIXxaJ6KIZM2ZkSEHSW1AN079MRq1MKmaGHe6bb77p2bOnZz2wOHvMMcd89913YeVkrj2xbkS8EffmKUk8HFFxGRqRuffq1csft5dcIOOXX37JwqWnM0+KZU0WNzOkdpxY/wsu7pZ+FQEREIHIEvC/u2ScRfYxrVLs779j+969Asaxk/oFY16OptJfffVV165d/V/Jp59+enq/kvG7DB482J/eYquttnrppZfCAvn999+vueYanGSetjvttNM777wTVk75tCfujeg3T1Wi4oiN43/dNI6Op5AlaX+B9jZt2rz11lthh8Bz2bt3b7+Z3rFjR7YChJWTSnv/Cy4VOeorAiIgAuVJwP/uknFWnuRDjvXci2um9raC/oMKKii8LLjqr7zySosWLTxLgsUsoo7SkoRi3Lhxfsks+d12221JSH7iiSeIoPI0JLLqoYceilRCkBJpEwNHJJynNhFyxMmFja4rUfLTTz/NYrQnmUXqBx54IKxk2g8dOtRfvbRp06ajRo0qccSMXvS/4DI6UPYJ//6Hor/0ds4+5aWxCOQ6Af+7S8ZZJJ/2p58XNOxU9Bpdlcj78FMLFv8USV1LUAr/FtFFfv8WsUdEIJXQNNgl3G9+nxzLfPjkkkhvMX369N12282zQkile9lll0UzlW6JYPBvEQ/nd/gRLUfMXImNg1ycN2/ennvu6QHBJ3fxxRezSB2kr7/NpEmT4qqX3nDDDUlUL/XLTPrc/4JLWkhudpRxlpvPVbPKEQL+d5eMs4g91CVLC447dw2zzNoXfFiuq0LpIoJFVTwyjGikJORT5XOLLbZwNgTGRBLRbOTlOvnkk/3BW4cccsicOXOSUKbCu3z77bfExvkjw4icI34uCcWww7zNHAcddBAL02GFYN716NHDM+/Q6rjjjquopCpOef8LLux0cry9jLMcf8CaXnYT8L+7ZJxF5lmuXFkw6J41zbLqsVorWX6wp5KoI+/Lm2gkYpKS2FP5wgsvsPTGAlxYHhh2N998c+3atT0dttlmmwkTJoSVE7X2RMgRJ+dNCncaUXTE0oXVk822LBazZBy2I/s3+/bt669euvPOOydRvTTsuGW297/gymycXw1knOXX89Zss4yA/90l4ywaD+/V1wus2RqWWZ/rCv78MxrKpUELYo+IQPIsCSKTiE8KG9WEmRVWlTFjxlD/0RuXupB33XUX5ZLCyolme+LkiJbbeOONvQk2btx45MiRobTlKYRNb8G4VC+NK+T18MMPRyRuz/+CC4Ui9xvLOMv9Z6wZZjEB/7tLxllFP8i58wradFvDLNv36ILvFlS0Wukfnwik/v37+/P4E6VErFL6R1ol8dNPP+3cubNntVStWvXcc8/95ZdfMjRcBYolZo7IOeLnvMkSV0d0XYZUonpp+/btvbHwnF1xxRVJVC/NkHqI9b/gMjdKVkqWcZaVj01K5wsB/7tLxlmanvqXX8VyXmBXWdNVllaz2DlXviorJdWd9/sss9YFkyu+SGWaiJQshgqYxCT546WIWCJuqeTWSV3FAsMOwxrzDAisNGy1pIRlTSfi54ii86ZMdB0xdklUwEww4R9++OGEE07wP7vDDjssieqlCYZIyy3/Cy4tAnNHiIyz3HmWmkkOEvC/u2ScpeMB336vz8BatbnSn5kM8yvB8d9/BbZVrPv9Iwo4z4+DyKRddtnFsyTwvhC9lEQO+jharFeyasnapSeZNU1WNuOa5fCvxNIRUedNn0i7W265JYnl4DhEJVYvff311+OaReRX/wsuIipFRQ0ZZ1F5EtJDBEog4H93yTgrAVCIS8uWF2x/UJFltlbBJdcUvPBKASuV5Inl3CuCuePBBctXlCp21pxEd0vtlt03iE965JFHGjZs6FkSjRo1IpIp6bil4nYJ+wBSt0uyjnJxC5WNrqlYqM8++6y/emm9evXuvffesPGC5YnR/4Irz3GzYCwZZ1nwkKRi/hLwv7tknKX2OTjhvCLLbOcS8pBRdsl2Kmxw6kWpjZSbvYlVImLJv+OPeCaimkLNthxW9ELpE4XGaVnb/eSTTzp16uRZz+y0veCCC5YsWRKFCSbQwf+CS9AsH2/JOMvHp645Zw0B/7tLxlkKj43qloXLl00LqLNU4sF126SwGXFpOkoiQNwS0UueEUBUE7FNRDiV1HaNa+UcC7/G2NnwS/FdEeedd16QXRFULz3rrLP81UsPOOCAzz//PBsmrQ0BpT8lGWels9EdEahwAsWNs9Vllb0vSJ2UTaDruYVtHu5v1aqV3J7rDw4ovNXtvJLb5P1VEpg988wzxDBtt912wOD/kOHDh2+55ZYDBgxgXbJEPMXbUJGJukyUg6QoZIld8vBiy5YtSWDm5RNhxXPIkCGsct59992sS5YIhDZ33HGHvw3VS1988UUKmFLmocQuuigCIiACIpARAsXtNf+VCjcno6gAxZQK3Wa1Csgfm+Agq5ZVL2z8S9SXhBLMoxxuYTEQz0RUk/cpJ9qJmKe4oePyr1avXj25/KtxYnP4V2zc4pl4J06cGDdlLDnsOQ8+1UsHDhyYRPXSOLHl/KveXaUCl+esVDS6IQIVT8D/7nLn8px530eBTz7/srDp3gda5YQAq1Sx3bsWNvZ6BR4nrxq6opmzZs0itokIJ+Y+d+7c7t2777PPPjNnzuRX6gL16tVr1113fe+99xwZKhdREurqq6/GRMsrVqEmS9HMSy65hOpMXg0reBJPduihh0IYUbNnz+7Wrdt+++332Wef8SsP4rTTTuNBUM7BPYhQw6mxCIiACIhA6gQS2hapi89JCZ8WGWctNit7ftsUJcf/rKhX2X3yt0XdunUHDRpE3SfinBwFfDxkrCX7Bmudjz32GH/gcN3V/GYpkwXN/IUVZub169d/4IEHsGu96u94JXGVAbZVq1YUyHLCqF5KDtv77ruPQg5hxKutCIiACIhAOgnIOAtP85MiM2uLzcru7LX5+IuyG6vFKgJEOBHnRLQTBhkXWPEkOxq1IznHyBg2bBibOnGhiVZYAsTkEZnnGbWseAKWtUvkNGnS5KmnnnrjjTcwhcOKVXsREAEREIH0EpBxFp7nT0sL+9SuWXbnmjUK2/zya9mN1cJHoEuXLizAEfnkMv6zkfPCCy9kee6kk05i6c3XUKfhCLjl4Kuuusph5N9+/fp98cUXFGwIJ0itRUAEREAEMkNAX3LhuW4bc+fEjq+/cT8T/Tu3qM02Rb0Stda9NQgQ80Tk0/jx4/HrDB48+LbbbiPx/Rot9EtSBIjSu/baa0n86xxmFGnwJ5xLSqQ6iYAIiIAIpI3A6iqEaROZ84JaFplZX84ve65em1Zbld1YLUoi0LFjR0pwlnRH11IicNSqIyUR6iwCIiACIpABAvKchYfaosg4G/t52Z2fLQpQ83qV3UctREAEREAEREAE8peAjLPwz37zZoV9fn/TZseSEZR6zJpt9nbh3aZNSm2mGyIgAiIgAiIgAiJQREDGWRGJ4D+rVrU7hhY2v/TGRP0u6V94d8h9Rs4zHSIgAiIgAiIgAiJQFgEZZ2URKvH+WSebrUpgNvoBG12YIyq+4agx9vxDqy42s7NPib+r30VABERABERABESgJAIyzkqiUuY1UjlMcIaX2WHd7LqBtipZVGE/ikJefr0dfnDhrxMfKqOQQJnDqYEIiIAIiIAIiEDeEKhEFacEk61Vq1aCu/l+64MZ1nZfs5+KOLS2vVvZa9PNZhVdaWTTx1obZfUs4qGfIpBJAsuXL/fE693loeDk79lz19qiOSf/2CbVCr7139K5CIhAhRPwv7ucMvKcpfBQsLqWz7OuxxeJ+MReG7naMut1tv0+W5ZZERz9FAERqDACK1ascGMvsO8qTAkNLAIiEJiA8pwFRlViw5rr2gvDzYbb3Hn27gc2Z561bmFttrNNGpbYXBdFQAREQAREQAREIDEBGWeJ+QS+22wz4z8dIiACIhA9Av/WrXPGKq3qb77NtdFTTxqJgAjEEZBxFgdEv4qACIhArhH4b52171s1p702qZdrc9N8RCAXCSjmLBefquYkAiIgAiIgAiKQtQRknGXto5PiIiACIiACIiACuUhAxlkuPlXNSQREQAREQAREIGsJyDjL2kcnxUVABERABERABHKRgIyzXHyqmpMIiIAIiIAIiEDWEpBxlr5Ht/l+VqlS7L+lv6ZPqCSJgAiIgAiIgAjkFwEZZ/n1vDVbERABERABERCBiBOQcRbxByT1REAEREAEREAE8ouAjLP8et6arQiIgAiIgAiIQMQJyDiL+AOSeiIgAiIgAiIgAvlFQMZZfj1vzVYEREAEREAERCDiBGScRfwBST0REAEREAEREIH8IiDjLL+et2YrAiIgAiIgAiIQcQIyziL+gKSeCIiACIiACIhAfhGQcZZfz1uzFQERyHcCP71n80flOwTNXwSiTaBqtNWTdiIgAiIgAmkl8PYutug/22Qr2/UZq7tNWkVLmAiIQHoIyHOWHo6SIgIiIALRJ1Dpz8Uxy4zjuy/tqdb2zgn258/RV1saikC+EZBxlm9PXPMVARHIXwIF69SzPYZY9VUECsw+GWFP1rNPB1rByvyFopmLQPQIyDiL3jORRiIgAiKQKQKVrMW5dtQS2+5Uc6//P83evtieqWoLxmVqTMkVAREISUDGWUhgai4CIiAC2U6gWl3bZagd8Zk13r5wKr+YvbC/je9gy2Zl++SkvwjkAAEZZznwEDUFERABEUhEoNLfv7rblf74cXW7Oi3sgA/tgOetbtG1r6fYU1vatHPsn2VFl/RTBESgAgjIOKsA6BpSBERABMqJQMF/9vmdNZ7f2g1XefkXtpKFTN/RuJv1+Ns69Le1V10k9mzGXTayjn11nxlRaTpEQAQqgICMswqAriFFQAREoDwI/PCaja5ib55rRfZYwdob2X9/xw9duZq17mM9f7SWPa3Sqpu/m71+ho2ubIvejm+s30VABDJPQMZZ5hlrBBEQAREoZwIrvrYJnWxMJ/spNnCt2lYwaos/F7z38tT5Vq12ybqss6Ht/oQdNt0ablrYYLHZc7vba/vbb9+U3EVXRUAEMkNAxllmuEqqCIiACFQIgX9/s/cvspHNbM5rhePXMdvvaTv0q7Ub7LjWWmuVodQGbeygb2zfx80z4WaNsyeb2PQ+tvKPMvrqtgiIQJoIyDhLE0iJEQEREIEKJzB7uI2sadNvM5e2DEusYSNrcrI12iecas2OsiP+sHaXW7VV/f4xe3+Ajaxhcx8LJ0etRUAEkiIg4ywpbOokAiIgApEisHiaPV/TJp5ovxWptdUhdsAbtvB7+3iYPbGefXGXsTkg+FFlHdvhBjvyW9uyS2GnFWav9rIXGthP04OLUUsREIEkCMg4SwKauoiACIhAZAj8/oO9cYiN3sUWFtllG69rh061vZ61JR8XbrhkQXLSObHNAQtfD6f3uptYxxet+9tWv6jfgoU2ekd78wjzZ+UouqmfIiACaSFQadmyRPlsatWqlZZh8kHI0krN69pcZvrPDwurbey9yfJh6pqjCESCwPLlyz098uLdtfIvm3mjfXCtefsv1zXb5SHb/ASPg/3yiU05zL7zpZZtvrftMsxqbra6TaCzAps1zKaeamzkdAdrpm2vt23+Z2z21CECIpACAf+7y4mRcZYCzjW7flWpypYWWzX4bf436zYu2u60Zhv9JgIikDkC/hdc7htn856xqT2sMLmsWRWz7S+y7a+1qhhoxY5QjYv1Xn3h3+X2wZX28ZDCmDZukMC2/bPW+JDVbXQmAiIQkoD/3eW6yjgLibD05jLOSmejOyJQHgT8L7hcNs5++cjeOdy+n72aaRBnGOnNPulfhptttcSEZ8vn2NTjbe7k1Y023cY6PGl1W66+ojMREIHABPzvLtdJxllgeGU1PGWfzlMmvkqrd375pc5665XVXPdFQATSTMD/gstN4+zPn236hfbpI6tT99cz2/U127hjUJQEqL17pn35/Or2BKh1mGgb7rz6SsCzBeNt8n5GUU53kL229cnW5hZbW2+/Iib6KQLBCPjfXa6HNgQEIxeg1Y811vnMjP+ssqgG4KUmIiACwQkU/Gszb7En69nMIsusutmed9qhK0NYZgxXo4Ht9VxsuwA2mTvYRsBmgje6G3ZbqKNhZzv8X9v9NltnVTdKPbEtdOT69tlgK3CZPEKJU2MREIHVBGRGrGahMxEQARGIIoHvX7anq9nk/xVWYeK1vd3pdtRS2/psq5TUOxw/2cErrNNDVmSh2ZfP2ZMN7aNrSijulIBIpSrW8kLr+bO1PrGw7hN1ot66wEZVtR8mJOinWyIgAokJJPU/dmKRuisCIiACIpAWAsu+sld2trFdbEmRuCZt7YjPbZd7rRqJ/1M72NTZc4W17R3bTMDBls+p19pTaxu7B0Ida69vHR60I2bapq0K+/1sNmZfe3VPWx7bwK5DBEQgLAEZZ2GJqb0IiIAIZJ7AP8ts2ln25FY2/93CwQjl6jLW9n/f6mydtuHZ2rnjQOs519hS4I5fzcb1sBe3jOXgCHXUbWVdZtr+o80zGue+aU82t3cvsH9JX6tDBEQgBAEZZyFgqakIiIAIZJwAefy/uMeeqGMz7lmVnMdsbaL+B1iPf2zTAzMyes2mts9E6zbR2F7gDvKiPbOtTT7O2IIQ6mjS3Y74y9r3M1fDk9izDwfbE7ViOdJW72IIJVGNRSAfCcg4y8enrjmLgAhElMDCSfZsFZt0lpHTn4MtkK2OsZ4/2jaXWqWqmdW5wd6x7QV73GFsNeAgwJ/NB2xB+PRWYztC8KPyWrZtX+u50LY+vDAQjby1r51iz1W2H98JLkYtRSCfCcg4y+enr7mLgAhEhsCK+Taxsz2/ly0uUqlREzvsA9vtUVtnw6JLGf7J9oIW59hRS2IbDtyXAwH+b19iz1Sz718JN3b1+rbn03bou9Zgo8KOi8ye3dVe72a/fx9OlFqLQP4RkHGWf89cMxYBEYgUgZV/2PRL7anNbHYsUWLsqG3WeaR1nWcb7OAulOu/1erGNhyw7aBxm8JxSWY29oDY1oRlvjJQQXSqt5N1W2T7PGw1i1p/9YKN3MRmXGnUntIhAiJQCgEZZ6WA0WUREAERKAcCcx6xkTXs/Zvtn1WDUaZy5yvtiD+t6ZHlMHiiIdh2cMB06zLGvJyybE14ckubdraxWSHU0fxY6/m77XipuSKczHTaDfYc6dFYOtUhAiJQAgEZZyVA0SUREAERyDiBn96zMRvYhOPM28u45UHW83vb/jqrwhaAaBybHhTbiMB2BKcR1YNn3G0j69hX94YzrapUt7YD7Ih5tnnnwolt2X9VSF00piktRCBiBGScReyBSB0REIGcJ/DHQpt0uI1uZz8UFT+qX9m6v2Mdx1iNhpGbPRsR2I7ApoSWR60O8H/9TBtd2Ra9GU7bmk2s0zg7+A3bvJO1ujhcX7UWgXwiIOMsn5625ioCIlCxBKg+/vF1NrKBfTGqcE2vhtnew+yQf22j9hWrWhmjsylh98djGxTYpuAONi48t6dN3M9++6aMvnG3N97TOk2wym6NM+6efhUBEYgRkHGmz4EIiIAIlAuB+aNj+fenXBXLxc9BXv42F9hRy22Lk7JmgY8NCmxTYLMCWxbcMXu8PdnEpl9mbGvQIQIikCYCMs7SBFJiREAERKA0AtQUf6mlvXKYkX/fHc32sCPn2E6DrKq3j7HoVvR/slnhiD9s5ytWB/i/f1NsWwObG3SIgAikg0CGsxqmQ0XJEAEREIFUCfww0b57MSZkq9Ot9lYlS/txss0fFbvV/Hhbf7vVbby+mx1plAwv7fjwCvv3D2tymG20a3yTtdezJZ8XXtyAdP+vWoN94ttk1+9V1rHtr7ctz7Rpp9tXq8CyrYHNDZ9ebB1etHo7Jj8byoniYvx5iv00xtgVSjGoDbrZBu1jYGtvkbxY9RSBrCIg4yyrHpeUFQERSI7Awtfsg0GxrvX3SmScuTYbtFnDOPP6zhlkPX43Nh6WeMzoH1uvrN6gBOMMU6b9o/ZWL9tpcCzLK7lec+Oo0cg6jrVW79g7uxo5Zjl++NFG72RbHW7t7jTy0IY9Pr/TppxbmFXE9f3JYlaajbH3+liHu23rM8OKVHsRyEYCufKOyEb20lkERCC7CLAo+V7vJFVudowdvdRanpc7lpkHYqMOdsh/tvcDxuYGDpKXffGMjdzYPr7e/nPZ27ympZ/8u9xe3dPeXGWZEdDWro91edGOmRur9c45V5BEVasJHVVGvXSIupM7BGSc5c6z1ExEQAQyTuDje23x1CRHqcYSXa4elWyLk2ObG3Y4P7bRgQMn4pS+9tRaNv/ZQHN+/yKb+2asZcNNrfti26G/bdrFqMhOrXfOu/9oDTeO3Z3zRqyagg4RyHUCMs5y/QlrfiIgAmkkgFvojfZGRgwdxQmwuaHd7XbkbGO7gzvwNb5yqL20jS39tHjz1VeWfWmf3B/7lYC8A+fYOvVW33JnJPI48Btbf9UvH99txKXpEIGcJiDjLKcfryYnAiKQLgLrmm2yKiCdxLEfXJEuqTkop1Zz23eSHTQ+Zmm549tPbfr5Rb+U9POdY43aAxxtHik1/xl50doMj7Wh5dTjYyc6RCB3Ccg4S9Oz/XdF5V8T/mmYpnEkRgREoMII7PFyYfKID2+1Xz6qMDWyYuCG+9ph/9rutxslNFnobDe0VK3//Mnmvxe7y6pv06NLbcaNZr0K86t9PdX+WpKope6JQJYTkHGW+gMssNkP2chalZfOdbKqsg9chwiIQO4RwCfU7qbYtHDevLG9FazMvSmmc0aVqljL863nL9b5eavVrFTJv35ReKv+gWXslkBg/f0KG3u9SpWrGyKQxQSUSiO1h0do8GT2kMc88i3r2+LmVlCjYaUmXVITqt4iIAIZI7B4Sqmil3xY6i3vBhUh5/Szhb8ZxYs+6W/b9vXu6KRkAuR4a9yt5Fvu6q9FGeBqBkhjVpMcdeNi/egV8YJXieaseyJQBgEZZ2UAKvX27wvs3TPtSxLwFB7X/29P2/nBRH8gFrXUTxEQgQoj8P6AlIYmRdme79ozrQyv2btXGWlpa2+ZkkB19vYK1GxeNgyvzdKZZTdWCxHIWgIyzsI/upV/xf5i/qDf6kyJsZTfE6xBp/Cy1EMERKB8CdQ1W7uUEX8zI819mUfdlrbjlTbt+ph9NmlvO+jbrKmMWebUKqTBP+ywWHWsVavs8auxL2PV8c/SshurhQhkLQEZZyEf3ddP2dQjY0VF3EGq8J2G2NZnlxEqUdRcP0VABCqYQPvnS11lm3mzTQ6WQ2u7q23O9Uby+gXf22e3W8sLK3hSWT187ZaF6q+YV/Y8fvu6sE3tFmU3VgsRyFoC2hAQ+NH9MsPGNrPxRZYZ5LY9xY5aYi3OlWUWGKIaikBOEKhU1fZ83yqtmsu03rZifk7MqoImUWfrwoFXzClbg+VFbeoUmXRl91ELEcg+AjLOAjwzdnpPPtae2cG+L/qjrfH2dsRn1v5+q8YaiQ4REIH8I1Cvre1wQWza5KN9K2HAe/6xCTfjukU+sF/fL7vjsumFbeqyM0CHCOQsARlnCR9twb/GSsfIDW3mo7GCcRwYYwc8bwd8aHWKXigJBeimCIhAzhJoM8DWWzW5bz62WcNydpqZnhgJSlxRzu8+t+WzE422bJZ9PyvWgHgSKjvpEIHcJSDjrPRn++1L9nS1WAzKX6varGXWob/1+LvUgJXSJemOCIhADhKosrbt9Xbh4uY7p8T2B+hIggBrxDvdFevHH8AzrkokYEbfwj+S291p5DzTIQK5S0DGWUnPlkJvr+xoLx1oLgc1kSUte1rPRda6T6mlRUoSo2siIAI5TmCjXa31ybE5/mkyzpJ/1ludUVjr6csnSi2UTnLvr56MDUHhza3OTH4s9RSBbCAg42zNp/TPrzb1THtya5tfFNnQsJEdNt12f8Kqb7RmU/0mAiIgAuzXHlxYU0gwkiZA9rhdJ8aqPOF9HHeofXLjGqXlyV6Ez2zcYbG7tNn1Ne3BSpq0OmYLAaXSKHpSBf/Zl/fau2fbH0VXapvt/Lg1O6rod/0UAREQgWIEqq5re46zF/YrdkMXwhBosLd1n24T2hr5y9653KZdbvUqW50D7JcXjTxosSIsZuub7T3dNmgTRq7aikBWEpDnbNVjW/iGja5ik4oss2qU6b3cjvhDlllWfqiltAiUM4GGna3FkeU8Zg4Oh9V12HLb+vBYlmCcZJTF++rFWDI5LDOqp7c+yQ79Q5ZZDj53TakkApWWLfMSqpZwv1atACmbS+iXPZdIezjtFJs9cbXGW3axdvfZupusvqIzERCBbCCwfPlyT83cf3d5U83Jk+Vz7af3bcVcq9sqZpDVaJSTs9SkRMAR8L+73JU8Xtb89zebcY3NuHV1GG999mO+bUT46hABERABEahAArWaqU5xBeLX0BVOIF+NszkjbOoJq+vokWVnl/ttC3ZduZzfFf5cpIAIiIAIiIAIiECeEsg/42zxuzals/3wa+EDZ+/PtudZm+utaq4v4ObpJ1zTFgEREAEREIEsI5BPxtkfC2ObMb8YvfoRNdvVdhlh5KfWIQIiIAIiIAIiIALRIJAfxtl/f8cS53xwTawKnjvYkr3rOGOPlQ4REAEREAEREAERiBKBPDDO5o+yKYdb0TJmbEv2TrdZi/NU/SNKn0PpIgIiIAIiIAIiUEggp42zXz6xKT3suy8L50qs/zYnWttbbW38ZjpEQAREIDUCZK5e+YeRhFaHCIiACKSVQI4aZ3/+bNN726cPF1bJBdmmrazDk7GUOTpEQAREIEUCbPeecEJMxo6XWdsbUxSm7iIgAiIQRyDnjLOClfbZ7fb+xbE6xO6oY9Z+tDXpXvS7foqACIiACIiACIhAdAmEMc5+mGjfvRibylanW+2tSp7Tj5ONGC+O5sfb+tuV3Cbg1d+/t69H2tIZ9svrtuT7WCw/4WLVzep2snq7Wb2drUEnq7zWGsIK/rXR1WLlPtzBzbb9bJtL45sV3ddPERABERABERABEYgagTDG2cLX7INBsQnU3yuRcebaUHAjFePsq/vtndPsrzVx/WbGfz9NLKy21PNzq7P1Gi0qVbVGp9pP98dSyW51uLW706qT9V+HCIiACIiACIiACGQNgTDGWflM6q8l9tre9s2M2GgUIG95um3Y3jbYMeb9Wj7bln5uC561795enRQjTqu2N9uymdZmiNXbMe6OfhUBERABERABERCB6BOInnH2/vmFlhlbKvf91Oq2XA2x9hbW6ABr1dvIW7bwdVunJK9YtbrW+Z3VXXQmAiIgAiIgAiIgAllFIGLG2S8z7NNHYgBrmnVfblX5UdKBF63hfiXd0DUREAEREAEREAERyG4ClaOl/uRuhckvdhhcqmUWLY2ljQiIgAiIgAiIgAikk0CUjLM/F9uCb2OTw1+29RnpnKVkiYAIiIAIiIAIiECWEIiScfbr54XQNi6WIyNLaEpNERABERABERABEUiRQFIxZ4unlDrqkg9LvVXmjV+L6izVXDNBRpkd1UAEREAEREAEREAEcoVAUsbZ+wMyMv1fPysUW2uLjMiXUBEQAREQAREQARGIPIGkjLO6ZmuXMjOSxK4o5VaZl/8uSu2/FhWXdIiACIiACIiACIhAPhJIyjhr/7w17lYyrZk32+RLS75V5tVaRSWhls8qs60aiIAIiIAIiIAIiEBOEojShoA6LQoRe+ubOYlckxIBERABERABERCB0glEyjgr8pwtfa50hXVHBERABERABERABHKZQJSMM/YBUEyTY5HZgldzmbrmJgIiIAIiIAIiIAKlECgv42zhG/ZRP3u9mz2/tr28nU05xbysZp5mVda2HfoU/ja1sxXWCvBu60QEREAEREAEREAEcp9AeRln07raB1fbig+sehv78WP7eJg92dIWjIsHvO1VsfIAHIvN3jraVv4Z38D7/bfv7J+l3m86EQEREAEREAEREIHcIFBextnOL9pxf9hB31nnKXbcStvhnJhf7N394yFWWcc6jCy8+NlIe7a6LXrb/vt7dbP//omZdK/ubo9tasvnrb6uMxEQAREQAREQARHICQJJpdJIYuYb77m6U6XK1upS+/BOW7b62uqzpkfaQevbpM6xuz+bPbe7YUCuZ1ajlS37NHYRq06HCIiACPy/vbuPkaOs4wD+Ow7otdy1pT1ouSIFFFpKY6GipEgD0vAWtVYMvtHYxAhF+UMjCcYYMTHxjcQYY+Qf41+NCYkiJfElCi0QqUIhegGOAq0U6Tut7d0eVCjQOnO3XK+XXdjp3ezrZ7Jpd5955nn5TLP5dmZnhgABAgSaVKBaR87G8J0w9Mv/M68dU1z82HN13FiIhSujY6jg8FBK29YXA0PJ7OSIeSti+YMxY1HpzZUSIECAAAECBBpWIMuRs4t/EMnr3ZeFd0Tyes+l9zvRlmSsb5SteGJXfHRN+jq4K/qfjgNPRXIArfPsOGVunLog2ieX3dAKAgQIECBAgEAjC2QJZ+Oc597H40BvvPZyvPjDtKUbNkb3h9+7ySlnRPLqSS7etBAgQIAAAQIEml+giuHs2bviud8XRT+wLN467mdwNv9eMUMCBAgQIECgZQWq+JuzK+6Nmw/FTVvjirtj77q4/6ro/V7Lups4AQIECBAgQKCkQBXDWdJ/ch1A8rux+V+NT2yN5Hf9G78fA8+VHJZCAgQIECBAgEBrClQ3nI0YJxFt9uL0phh7Hh4p84YAAQIECBAgQKBG4SyBb08OnUW0DT9N044gQIAAAQIECBBIBWoUzpKHL+14LO1/5iXpnxYCBAgQIECAAIEhgWpdrfmnhTH3yzFraUyaEfueiI1fiOSZTPM+5Uay/h0SIECAAAECBEYLVCuc7e2Lbbcf7bg94qLb4kM/OVriHQECBAgQIECAQES1wtmqI+m9/gvPx/92pxdsTp8fJ03nT4AAgYYUmHpBLPxSOvLTL2vI8Rs0AQL1LdBWKJR8/Hhx1F1dXfU9fqMjQIBAUWBwcHDEwnfXCIU3BAjUucDo767hodbogoA6dzI8AgQIECBAgECNBISzGsHrlgABAgQIECBQSkA4K6WijAABAgQIECBQIwHhrEbwuiVAgAABAgQIlBIQzkqpKCNAgAABAgQI1EhAOKsRvG4JECBAgAABAqUEqnWfs1J9KyNAgEDjCRzcEVvvif7e2P9QHNiRPuykI2JyxPRl0X15dF8aZyyLE4aeHZzMbde62P7HdI7zVsfUeaUn+8qG+M+96ar3r/LQlNJESgm0mIBw1mI73HQJEBiPwAu/ir/fEm8c28RrEclr37rYsi5d8flNMW1+scbu9fHPn6XvZ135buFsuM7MxcJZ0c1fBFpbQDhr7f1v9gQIVCjwxoFYf1W83JtWPyliweo4bUnMvCQ9SDa4Jfo3xc77Yvuj6YE0CwECBMYnIJyNz8/WBAi0iMCTXy8msxkRV/fF9AVH5z31vJhzfVz4zTh8KHY/FB2zjq7yjgABAtkFhLPsZrYgQKDVBPb3Rt+adNKdEZ8ejBOTv0otyVG0nmtLrVBGgACBDAKu1syApSoBAi0qsGF5HBma+sU/L5vMWpTGtAkQmHgB4WziTbVIgEBTCby+N3ZuS2eUHC+bf2tTTc1kCBCoSwHhrC53i0ERIFA/AgObimOZPeoeGfUzPCMhQKDpBPzmrOl2qQkRIDCxAgPPF9vrfOcGGcfR/t5/lN3owL/KrrKCAIGWFBDOWnK3mzQBApULDDxbrNt1XuUbja355I/HlvhMgACBMgLCWRkYxQQIEBgWOLSvKHHytOMnmR4xqczWyQ1sXy2zSjEBAi0pIJy15G43aQIEKhfoeuexS4ObK99obM0l98dZy8cWDn9+5q7Y8K3Sq5QSINCSAi4IaMndbtIECFQuMO2CYt2R85uVb6smAQIEsgsIZ9nNbEGAQEsJTHvnyFn/2paat8kSIFArAac1ayWvXwIEGkQguQ4geZjmmxF7InY+ED1XV2ncR96OwuZIHk6QvE5oj1POjrNWRMdpVepdNwQI1E5AOKudvZ4JEGgIgfZJcfG3Y+OP0sE+dk3ccDiirRoDX399bHkg7Sjpbfj5BO23xJKfpg/xtBAg0NQCTms29e41OQIEJkTgg3emjwdIlr0Rf/tivP162VZf2x5v9pddm2lF9+VxxS/jpq1x81vxmSfiotvi7YgNt0d/X6ZmVCZAoOEEhLOG22UGTIBA1QXaO+Kye4q9PntP3Dc59jwahw8dHcfhN2PnX+KBpfGb98XgS0fLx/Nu0Z0x/2vReXa0tUf3JXHpLyK5lUdyCG33w+Np1bYECNS/gNOa9b+PjJAAgToQOOdz8ckZ8cg1UYj4b8TapZH83/bUiCkXRqEvLRw+85jjSNti9ooYWBtT5uTYiaYJEKgDAUfO6mAnGAIBAg0hkFwKcGMhFq6MjqHhJr89S1Latr4YGEpmJ0fMWxHLH4wZi3KZzZG34pW1addnfjyX9jVKgEDdCLQVCsn/+MouXV1dZddZQYAAgXoSGBwcHBlO7t9dB3dF/9Nx4KlIDqAlZx5PmRunLoj2ySMDmOA3r74Uj6+O7X+NpWvi3JUT3LjmCBCoqcDo767hgTitWdMdonMCBBpUYMoZkbx6rsl3+P9eE898JQ4eiiR2zp4Rn90dk2fl26PWCRCoAwGnNetgJxgCAQIESgqcNDU6r4yuuenZzF3748+zY/cjJSsqJECgmQSc1mymvWkuBFpaYPSpgdxPa1ZZOvnB2eZfx8O3pt0uXx+zP1bl/nVHgEB+AqO/u4Z7ceQsP20tEyBAYIIE2k6M81fH4tvTa0KfWDVBjWqGAIE6FRDO6nTHGBYBAgTGCsy5Li3Zv21suc8ECDSXgHDWXPvTbAgQaGKBQ8lNOyImNfEMTY0AgVRAOPPvgAABAg0hcCT6vpsO9PTrG2K4BkmAwHELuJXGcdPZkAABAnkK/OHc6L42eq6LzrmR3Ofs6Tti++b0sNlH7s6zV20TIFB7AVdr1n4fGAEBAhMiMPqKp2a4WvO3bbH/WJg558SS38XMxceW+kSAQGMLjP7uGp6JI2eNvUeNngCBphW48Ui8vi8KL8SrL8ak7ph6fnSd27STNTECBEYJCGejMLwlQIBAXQl0dEfyOv2yuhqUwRAgkLeACwLyFtY+AQIECBAgQCCDgHCWAUtVAgQIECBAgEDeAsJZ3sLaJ0CAAAECBAhkEBDOMmCpSoAAAQIECBDIW0A4y1tY+wQIECBAgACBDALCWQYsVQkQIECAAAECeQsIZ3kLa58AAQIECBAgkEFAOMuApSoBAgQIECBAIG8B4SxvYe0TIECAAAECBDIICGcZsFQlQIAAAQIECOQtIJzlLax9AgQIECBAgEAGAeEsA5aqBAgQIECAAIG8BYSzvIW1T4AAAQIECBDIICCcZcBSlQABAgQIECCQt4Bwlrew9gkQIECAAAECGQSEswxYqhIgQIAAAQIE8hYQzvIW1j4BAgQIECBAIIOAcJYBS1UCBAgQIECAQN4CwlnewtonQIAAAQIECGQQEM4yYKlKgAABAgQIEMhbQDjLW1j7BAgQIECAAIEMAsJZBixVCRAgQIAAAQJ5CwhneQtrnwABAgQIECCQQUA4y4ClKgECBAgQIEAgbwHhLG9h7RMgQIAAAQIEMggIZxmwVCVAgAABAgQI5C0gnOUtrH0CBAgQIECAQAYB4SwDlqoECBAgQIAAgbwFhLO8hbVPgAABAgQIEMggIJxlwFKVAAECBAgQIJC3QFuhUMi7D+0TIECAAAECBAhUKODIWYVQqhEgQIAAAQIEqiEgnFVDWR8ECBAgQIAAgQoFhLMKoVQjQIAAAQIECFRDQDirhrI+CBAgQIAAAQIVCghnFUKpRoAAAQIECBCohoBwVg1lfRAgQIAAAQIEKhQQziqEUo0AAQIECBAgUA2B/wPCN1ow4sSOFQAAAABJRU5ErkJggg==" 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" 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" + } + }, + "cell_type": "markdown", + "id": "7f18a74d", + "metadata": {}, + "source": [ + "# Fragmenting Compounds\n", + "\n", + "\n", + "## Background\n", + "\n", + "A [fragment](https://www.frontiersin.org/articles/10.3389/fmolb.2020.00180/full#:~:text=Fragment%2Dbased%20drug%20discovery%20(FBDD,in%20target%2Dbased%20drug%20discovery.) is essentially a small molecule that has a low molecular weight and small size, often representing a small part of a bigger, drug-like compound. These small compounds are of great importance in drug discovery, especially in the early stages. Fragments are used to identify small chemical and functional groups that bind, even if weakly, to the target of interest and thus serve as useful starting points in a [medicinal chemistry](https://en.wikipedia.org/wiki/Medicinal_chemistry) campaign. Once identified, fragments act as building blocks and are subsequently chemically modified (addition/removal of specific chemical groups) to improve the overall interaction of the newly generated compounds with the target of interest. \n", + "\n", + "Generally speaking, fragments will follow the [rule of three](https://www.sciencedirect.com/science/article/abs/pii/S1359644603028319?via%3Dihub). However, this is not always the case. Different researchers will have their own definitions of what is deemed a fragment:\n", + "\n", + "1. **Molecular weight less than 300 Da**\n", + "2. **ClogP value less than 3**\n", + " 1. ClogP is a well established measure of a compound’s hydrophilicity which is important for absorption, permeation, and other drug-related physical properties.\n", + "3. **Less than 3 hydrogen donor and acceptor groups.**\n", + "\n", + "Existing molecules can also be split into smaller fragments, a good visual is shown below of the fragmentation process: \n", + "\n", + "![image.png](attachment:f4220217-9021-45e0-8c56-a33cc7f65ce3.png) \n", + "\n", + "[***Source***](https://www.frontiersin.org/articles/10.3389/fchem.2018.00229/full)\n", + "\n", + "## Fragment-Based Approaches for Compound Optimization\n", + "\n", + "Standard fragment-based approaches for compound optimization are: \n", + "\n", + "1. **Fragment growing** - adding chemical groups to the fragment to improve properties.\n", + "2. **Fragment merging/scaffold hopping** - combining fragments that have an overlapped binding site.\n", + "3. **Fragment linking** - linking two or more fragments together to drastically improve [binding affinities](https://www.malvernpanalytical.com/en/products/measurement-type/binding-affinity#:~:text=What%20is%20Binding%20Affinity%3F,(e.g.%20drug%20or%20inhibitor).).\n", + "\n", + "You can read more about these methods in detail [here](https://www.frontiersin.org/articles/10.3389/fmolb.2020.00180/full). \n", + "\n", + "## Fragment Generation\n", + "\n", + "![image.png](attachment:ddd64e95-9256-46e4-9c25-2560deadf01c.png)\n", + "\n", + "***[Source](https://www.researchgate.net/figure/A-schematic-overview-of-a-molecular-fragmentation-process-For-a-single-step_fig2_353714355)***\n", + "\n", + "Fragments are essentially generated by breaking specified bonds in a larger molecule. There are multiple ways approaches that one can take to fragment a molecule. The methods covered below include; RECAP, BRICS, FraggleSim and AnyBreak. \n", + "\n", + "1. **RECAP - R**etrosynthetic **C**ombinatorial **A**nalysis **P**rocedure \n", + " 1. Alkyl groups smaller than five carbons and cyclic bonds are left intact while compounds are dissected based on 11 pre-specified bond types\n", + "2. **BRICS -** **B**reaking **R**etrosynthetically **I**nteresting **C**hemical **S**ubstructures\n", + " 1. In BRICS, compounds are dissected based on 16 bond types while considering the chemical environment and surrounding substructures. \n", + " 2. Both RECAP and BRICS are examples of systematic fragmentation\n", + "3. **FraggleSim**\n", + " 1. RDKit uses the Fraggle similarity algorithm developed by Jameed Hussain and Gavin Harper of GSK. Read more about the details of the algorithm [here](https://raw.github.com/rdkit/UGM_2013/master/Presentations/Hussain.Fraggle.pdf) and [here](https://www.rdkit.org/docs/source/rdkit.Chem.Fraggle.FraggleSim.html).\n", + "4. **AnyBreak**\n", + " 1. This method uses BRICS first and fallback to generating all possible fragmentation if it doesn't work. \n", + "\n", + "**Note:** It’s challenging to point to one method and refer to it as the status quo. The method you should use depends on what exactly you are trying to do with the fragments, the types of molecules you’re working with etc. Generally speaking, it is ideal to fragment a molecule in a way that is synthesizable in the lab, and each of the methods listed above have slight variations in their approach. There’s no point fragmenting a molecule at certain bonds if these bonds have never been broken before in a lab setting, it’s not realistic. \n", + "\n", + "Once molecules are fragmented, the next step is typically a matched molecular pair analysis (MMPA). This analysis compares the chemical structure of two molecules that only differ by a **single chemical transformation** (i.e. changing one functional group). MMP’s are useful to analyze a large collection of compounds because the minimal structural differences make it much easier to interpret any observable changes in physical or biological properties. We will not cover MMPA’s in this tutorial. See below for a visualization of a matched molecular pair: \n", + "\n", + "![image.png](attachment:053aeef2-86e6-4191-bd4a-f82359a8efc4.png)\n", + "\n", + "[Source](https://en.wikipedia.org/wiki/Matched_molecular_pair_analysis#:~:text=Matched%20molecular%20pair%20analysis%20(MMPA,matched%20molecular%20pairs%20(MMP).)\n", + "\n", + "**Note:** Sometimes the term fragment is used synonymously with scaffolds. However, scaffolds are better defined as key core structures of a compound, often critical and essential for binding, whereas a fragment may only partially match with a “core structure”. \n", + "\n", + "## Tutorial\n", + "\n", + "Now let’s walkthrough how you could do this in RDKit and then compare it with Datamol. Starting from a cluster of molecules, this tutorial will cover the following:\n", + "\n", + "1. Generate list of all fragments in different ways as described above\n", + " 1. RECAP, BRICS, FraggleSim\n", + "2. Show how to return the results as a hierarchy of nodes instead of just a visualized set of fragments allowing for more flexibility in the manipulation of the results\n", + "3. Fragment molecules on specific bonds suitable for an MMP analysis\n", + "4. Briefly exploring other manipulations\n", + " 1. Assembling - assemble fragments to create new molecules. Limit the number of fragments you’d work with because it’s computationally intensive.\n", + " 2. Decomposition - break a molecule down to get non-overlapping fragments and how they are linked.\n", + "\n", + "## RDKit Example" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a5e049ff", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'mol1_f' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m smiles \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCCCOCc1cc(c2ncccc2)ccc1\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 10\u001b[0m mol \u001b[38;5;241m=\u001b[39m Chem\u001b[38;5;241m.\u001b[39mMolFromSmiles(smiles)\n\u001b[0;32m---> 12\u001b[0m mol1_f \u001b[38;5;241m=\u001b[39m Chem\u001b[38;5;241m.\u001b[39mGetMolFrags(\u001b[43mmol1_f\u001b[49m, asMols\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 13\u001b[0m MolsToGridImage(mol1_f)\n", + "\u001b[0;31mNameError\u001b[0m: name 'mol1_f' is not defined" + ] + } + ], + "source": [ + "from rdkit import Chem\n", + "from rdkit.Chem.Draw import IPythonConsole, MolsToGridImage\n", + "from rdkit.Chem import BRICS\n", + "from rdkit.Chem import Recap\n", + "from rdkit.Chem import rdMMPA\n", + "\n", + "from rdkit.Chem.Fraggle import FraggleSim\n", + "\n", + "smiles = \"CCCOCc1cc(c2ncccc2)ccc1\"\n", + "mol = Chem.MolFromSmiles(smiles)\n", + "\n", + "mol1_f = Chem.GetMolFrags(mol1_f, asMols=True)\n", + "MolsToGridImage(mol1_f)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a393217f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Decompose compounds based on BRICS algorithm. \n", + "# Calling dm.fragment.brics runs this algorithm, as well as fixes/sanitizes fragments in one line of code.\n", + "\n", + "brics_frags = BRICS.BRICSDecompose(mol, returnMols=True, singlePass=True)\n", + "brics_frags = list(brics_frags)\n", + "MolsToGridImage(brics_frags)\n", + "\n", + "# Recap, FraggleSim and rdMMPA can be run in a similar manner as the BRICS algorithm above." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "fe058c37", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import datamol as dm\n", + "\n", + "smiles = \"CCCOCc1cc(c2ncccc2)ccc1\"\n", + "mol = dm.to_mol(smiles)\n", + "mol" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7c2059f8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "RDKit WARNING: [10:26:25] WARNING: not removing hydrogen atom without neighbors\n", + "[10:26:25] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:26:25] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:26:25] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:26:25] WARNING: not removing hydrogen atom without neighbors\n", + "[10:26:25] WARNING: not removing hydrogen atom without neighbors\n", + "[10:26:25] WARNING: not removing hydrogen atom without neighbors\n", + "[10:26:25] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:26:25] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:26:25] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:26:25] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:26:25] WARNING: not removing hydrogen atom without neighbors\n", + "[10:26:25] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:26:25] WARNING: not removing hydrogen atom without neighbors\n", + "[10:26:25] WARNING: not removing hydrogen atom without neighbors\n", + "[10:26:25] WARNING: not removing hydrogen atom without neighbors\n", + "[10:26:25] WARNING: not removing hydrogen atom without neighbors\n", + "[10:26:25] WARNING: not removing hydrogen atom without neighbors\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# BRICS\n", + "frags = dm.fragment.brics(mol)\n", + "dm.to_image(frags, n_cols=3)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "99027b1a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "RDKit WARNING: [10:27:02] WARNING: not removing hydrogen atom without neighbors\n", + "[10:27:02] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:27:02] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:27:02] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:27:02] WARNING: not removing hydrogen atom without neighbors\n", + "[10:27:02] WARNING: not removing hydrogen atom without neighbors\n", + "[10:27:02] WARNING: not removing hydrogen atom without neighbors\n", + "[10:27:02] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:27:02] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:27:02] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:27:02] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:27:02] WARNING: not removing hydrogen atom without neighbors\n", + "[10:27:02] WARNING: not removing hydrogen atom without neighbors\n", + "[10:27:02] WARNING: not removing hydrogen atom without neighbors\n", + "[10:27:02] WARNING: not removing hydrogen atom without neighbors\n", + "[10:27:02] WARNING: not removing hydrogen atom without neighbors\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# FraggleSims\n", + "frags = dm.fragment.frag(mol)\n", + "dm.viz.to_image(frags, n_cols=6)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "89850686", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "RDKit WARNING: [10:27:33] WARNING: not removing hydrogen atom without neighbors\n", + "[10:27:33] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:27:33] WARNING: not removing hydrogen atom without neighbors\n", + "[10:27:33] WARNING: not removing hydrogen atom without neighbors\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Recap\n", + "frags = dm.fragment.recap(mol)\n", + "dm.viz.to_image(frags, n_cols=3)" + ] + }, + { + "cell_type": "markdown", + "id": "54bceb67", + "metadata": {}, + "source": [ + "What you can also do is assemble some new molecules based off a list of fragments. This is how fragments are used as building blocks for larger, more optimized molecules. By having an understanding of the properties of the underlying fragments, you can essentially run a “mix and match” process to generate optimal molecules. \n", + "\n", + "Assembling molecules from fragments is computationally expensive. Make sure you use the parameters: \n", + "\n", + "- ***frags***\n", + "- **max_n_mols**\n", + "\n", + "To limit the number of fragments to work with and the number of molecules to be assembled. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e9551e69", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "RDKit WARNING: [10:27:40] WARNING: not removing hydrogen atom without neighbors\n", + "[10:27:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:27:40] WARNING: not removing hydrogen atom without neighbors\n", + "[10:27:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:27:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:27:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:27:40] WARNING: not removing hydrogen atom without neighbors\n", + "[10:27:40] WARNING: not removing hydrogen atom without neighbors\n", + "[10:27:40] WARNING: not removing hydrogen atom without neighbors\n", + "[10:27:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:27:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:27:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:27:40] WARNING: not removing hydrogen atom without neighbors\n", + "[10:27:40] WARNING: not removing hydrogen atom without neighbors\n", + "[10:27:40] WARNING: not removing hydrogen atom without neighbors\n", + "[10:27:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:27:40] WARNING: not removing hydrogen atom without neighbors\n", + "[10:27:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit ERROR: [10:27:40] Explicit valence for atom # 2 C, 5, is greater than permitted\n", + "[10:27:40] Explicit valence for atom # 2 C, 5, is greater than permitted\n", + "RDKit ERROR: [10:27:40] Explicit valence for atom # 3 C, 5, is greater than permitted\n", + "RDKit ERROR: [10:27:40] Explicit valence for atom # 2 C, 5, is greater than permitted\n", + "RDKit ERROR: [10:27:40] Explicit valence for atom # 3 C, 5, is greater than permitted\n", + "[10:27:40] Explicit valence for atom # 3 C, 5, is greater than permitted\n", + "[10:27:40] Explicit valence for atom # 2 C, 5, is greater than permitted\n", + "[10:27:40] Explicit valence for atom # 3 C, 5, is greater than permitted\n", + "RDKit ERROR: [10:27:40] Explicit valence for atom # 5 C, 5, is greater than permitted\n", + "[10:27:40] Explicit valence for atom # 5 C, 5, is greater than permitted\n", + "RDKit ERROR: [10:27:40] Explicit valence for atom # 5 C, 5, is greater than permitted\n", + "[10:27:40] Explicit valence for atom # 5 C, 5, is greater than permitted\n", + "RDKit ERROR: [10:27:41] Explicit valence for atom # 2 C, 5, is greater than permitted\n", + "[10:27:41] Explicit valence for atom # 2 C, 5, is greater than permitted\n", + "RDKit ERROR: [10:27:41] Explicit valence for atom # 2 C, 5, is greater than permitted\n", + "[10:27:41] Explicit valence for atom # 2 C, 5, is greater than permitted\n", + "RDKit ERROR: [10:27:41] Explicit valence for atom # 17 C, 5, is greater than permitted\n", + "[10:27:41] Explicit valence for atom # 17 C, 5, is greater than permitted\n", + "RDKit ERROR: [10:27:41] Explicit valence for atom # 17 C, 5, is greater than permitted\n", + "[10:27:41] Explicit valence for atom # 17 C, 5, is greater than permitted\n", + "RDKit ERROR: [10:27:41] Explicit valence for atom # 3 C, 5, is greater than permitted\n", + "[10:27:41] Explicit valence for atom # 3 C, 5, is greater than permitted\n", + "RDKit ERROR: [10:27:41] Explicit valence for atom # 3 C, 5, is greater than permitted\n", + "[10:27:41] Explicit valence for atom # 3 C, 5, is greater than permitted\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + 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+ "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Assembling new molecules based on a list of fragments\n", + "# Get the fragment set of a molecule\n", + "smiles = \"CCCOCc1cc(c2ncccc2)ccc1\"\n", + "mol = dm.to_mol(smiles)\n", + "frags = dm.fragment.brics(mol)\n", + "\n", + "# Limit the number of fragments to work with because assembling is computationally intensive.\n", + "frags = frags[:3]\n", + "\n", + "# Assemble 8 molecules from the list of fragments\n", + "mols = list(dm.fragment.assemble_fragment_order(frags, max_n_mols=8))\n", + "\n", + "dm.viz.to_image(mols, n_cols=3)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "93f77ef0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "RDKit WARNING: [10:28:03] WARNING: not removing hydrogen atom without neighbors\n", + "[10:28:03] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:28:03] WARNING: not removing hydrogen atom without neighbors\n", + "[10:28:03] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:28:03] WARNING: not removing hydrogen atom without neighbors\n", + "[10:28:03] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:28:03] WARNING: not removing hydrogen atom without neighbors\n", + "[10:28:03] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:28:03] WARNING: not removing hydrogen atom without neighbors\n", + "[10:28:03] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:28:03] WARNING: not removing hydrogen atom without neighbors\n", + "[10:28:03] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:28:03] WARNING: not removing hydrogen atom without neighbors\n", + "[10:28:03] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [10:28:03] WARNING: not removing hydrogen atom without neighbors\n", + "[10:28:03] WARNING: not removing hydrogen atom without neighbors\n" + ] + }, + { + "data": { + "text/plain": [ + "(['CCC', 'O', 'C', 'c1ccncc1', 'c1ccccc1'],\n", + " {'C',\n", + " 'CCC',\n", + " 'CCCOCc1cccc(-c2ccccn2)c1',\n", + " 'Cc1cccc(-c2ccccn2)c1',\n", + " 'O',\n", + " 'OCc1cccc(-c2ccccn2)c1',\n", + " 'c1ccc(-c2ccccn2)cc1',\n", + " 'c1ccccc1',\n", + " 'c1ccncc1'},\n", + " )" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Decomposition\n", + "# It's also possible to break a molecule based on a set of chemical transformation and gets the non-overlapping fragments and how they are linked\n", + "\n", + "dm.fragment.break_mol(mol, randomize=False, mode=\"brics\", returnTree=True) \n", + "# returns fragments, fragments + intermediate decomposition, decomposition tree" + ] + }, + { + "cell_type": "markdown", + "id": "4a2dae4e", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "- [https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.6b00596](https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.6b00596)\n", + "- RDKit Cook Book - Creating fragments - [https://www.rdkit.org/docs/Cookbook.html#create-fragments](https://www.rdkit.org/docs/Cookbook.html#create-fragments)" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/tutorials/new/Fuzzyscaffolds.ipynb b/docs/tutorials/new/Fuzzyscaffolds.ipynb new file mode 100644 index 00000000..e21e9ad8 --- /dev/null +++ b/docs/tutorials/new/Fuzzyscaffolds.ipynb @@ -0,0 +1,3451 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fa974713", + "metadata": {}, + "source": [ + "# Fuzzy Scaffolds\n", + "\n", + "Fuzzy scaffolding is a concept useful for scaffold decoration and constrained scaffolding. If you want finer control over the generation of your scaffolds, you can use the fuzzy scaffold function to **enforce specific groups** that need to appear in the core as a sort of pharmacophore requirement.\n", + "\n", + "**Note:** A pharmacophore is essentially “[a part of a molecular structure that is responsible for a particular biological or pharmacological interaction that it undergoes](https://link.springer.com/referenceworkentry/10.1007/978-3-642-16483-5_4502)”. \n", + "\n", + "You can also force R groups to be included as well, allowing for flexibility to reconstruct specified positions (attachment points) in the scaffold. Overall, it allows you to build a highly specific [molecular series to be used for MMPA](https://pubs.acs.org/doi/10.1021/jm500022q#:~:text=A%20matched%20molecular%20series%20is,groups%20at%20the%20same%20position.). \n", + "\n", + "## Understanding Key Parameters\n", + "\n", + "- **enforce_subs -** this lets you specify what substructure(s) you want to enforce on the scaffold\n", + "- **n_atom_cuttoff** - the minimum number of atoms a core should have. The smaller the number, the smaller the new scaffolds will be or the lesser number of new scaffolds will be generated, vice versa is true.\n", + "- **ignore_non_ring -** Some scaffolds might be a simple aliphatic chain, in other words, a molecule that only contains straight/branched chains with no rings. Most of the time, you want to ***ignore these scaffolds*** as they typically don’t translate well in a drug like context.\n", + "- **mcs_params -** This is quite a niche parameter. If two molecules in your dataset have a different Murcko scaffold but the same Minimum Common scaffold, toggling this argument will categorize these molecules into the same bucket using a [maximum common substructure algorithm](https://www.rdkit.org/docs/GettingStartedInPython.html#maximum-common-substructure).\n", + "\n", + "## Datamol Example" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "de75702a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neiRDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "ghbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neiRDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "ghbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom witRDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "hout neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not remoRDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "ving hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: nRDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "ot removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighborRDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "s\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:4RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "0] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen aRDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "tom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removRDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "ing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummRDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "y atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom witRDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "h dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neigRDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "hbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removRDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "ing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removinRDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "g hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogenRDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + " atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING:RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + " not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighboRDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "rs\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighborRDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "s\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", + "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n" + ] + }, + { + "data": { + 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"\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import datamol as dm\n", + "\n", + "# Get some mols\n", + "data = dm.data.freesolv()\n", + "smiles = data[\"smiles\"].iloc[:].tolist()\n", + "mols = [dm.to_mol(s) for s in smiles]\n", + "\n", + "scaffolds, scf2infos, scf2groups = dm.scaffold.fuzzy_scaffolding(mols)\n", + "\n", + "sfs = [dm.to_mol(s) for s in list(scaffolds)]\n", + "dm.viz.to_image(sfs, n_cols=3)" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/tutorials/new/Generatescaffold.ipynb b/docs/tutorials/new/Generatescaffold.ipynb new file mode 100644 index 00000000..f90159cf --- /dev/null +++ b/docs/tutorials/new/Generatescaffold.ipynb @@ -0,0 +1,598 @@ +{ + "cells": [ + { + "attachments": { + "81a46677-723c-42db-b6a3-281143cf3ae0.png": { + "image/png": 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+ } + }, + "cell_type": "markdown", + "id": "2fc73a86", + "metadata": {}, + "source": [ + "# Generate Scaffold\n", + "\n", + "## Introduction to Scaffolds\n", + "\n", + "A scaffold is best defined as a **molecular core** of a compound where R groups are attached via attachment points. Scaffolds are important in drug discovery as they help us uncover structure-activity relationships ([SAR](https://info.collaborativedrug.com/tofu-content-what-is-sar)) and often are found to be essential for the bioactivity of a given class of compounds. The general idea behind this approach is finding relationships between the structure of a compound and its properties such as biological activity and/or physicochemical properties. \n", + "\n", + "There are multiple ways to define a scaffold and this can make it hard to compare the results of different, independent studies that involve scaffolds. If you’re interested, you can read more about scaffolding [here](https://datagrok.ai/help/domains/chem/functions/murcko-scaffolds) and [here](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3328312/). Most of the time, we rely on the Murcko scaffold which is also known as the [Bemis-Murcko](https://pubs.acs.org/doi/10.1021/jm9602928) framework. \n", + "\n", + "From [Chemaxon](https://docs.chemaxon.com/display/docs/bemis-murcko-clustering.md): “Bemis and Murcko outlined a popular method for deriving scaffolds from molecules by removing side chain atoms. A molecular framework can be interpreted as a graph containing nodes and edges representing atom and bond types, respectively. Removing atom and bond labels or agglomerating nodes by chemotype yields a hierarchy of reduced graphs, or molecular equivalence classes, that represent sets of related molecules. Likewise, a framework can be further decomposed into individual rings (or the core ring assembly) using chemically intuitive rules: the rings can individually or jointly be considered as scaffolds derived from the original compound.” \n", + "\n", + "Scaffolds are generally useful in two main ways: \n", + "\n", + "1. Identifying core structures that have preferential activity against some specific target classes. This can then serve as a “building block” to further optimize active compounds on certain properties through the modification of R groups that are attached to the scaffold (sometimes referred to as scaffold decorations).\n", + "2. Scaffold hopping - finding structurally distinct compounds that have the same activity\n", + "\n", + "Scaffold hopping is particularly useful in [ligand](https://en.wikipedia.org/wiki/Ligand_(biochemistry))-based virtual screening methods where the information of known active compounds is used for hit identification and optimization rather than the available structural data for the target protein. In this approach, you start with a search template (i.e. the scaffold of the known active compound with all the decorations), keep the decorations the same and replace the scaffold itself with a similar molecular structure\n", + "\n", + "Below is an image showing a network of possible scaffolds for a given molecule A: \n", + "\n", + "![image.png](attachment:81a46677-723c-42db-b6a3-281143cf3ae0.png)\n", + "\n", + "If you’re interested in learning more about scaffolds and how we explore scaffolds computationally, read this [paper](https://pubs.acs.org/doi/10.1021/acs.jmedchem.5b01746). \n", + "\n", + "## Tutorial\n", + "\n", + "So, what does this look like in practice? This tutorial will show you some of the basics of using scaffolds in drug discovery. \n", + "\n", + "1. Load an example dataset/list of molecules\n", + "2. Identify the scaffolds\n", + "3. This will then enable you to create a chemical series which can be used in a MMPA (see the [fragmentation](https://www.notion.so/Fragmenting-Compounds-8c861697ae6c44f3991cb215fd93e393) tutorial)\n", + " 1. A molecular series refers to a set of two or more molecules with the same scaffold but different R groups at the same position, read more [here](https://pubs.acs.org/doi/10.1021/jm500022q#:~:text=A%20matched%20molecular%20series%20is,groups%20at%20the%20same%20position.). Once a molecular series is generated, it enables scientists to focus on studying molecular properties and how changes in the structure are associated in the changes of these values (e.g. SAR studies).\n", + "\n", + "## RDKit Example" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4d9006c8", + "metadata": {}, + "outputs": [], + "source": [ + "from rdkit import Chem\n", + "from rdkit.Chem.Scaffolds import MurckoScaffold\n", + "from rdkit.Chem.Draw import IPythonConsole, MolsToGridImage\n", + "\n", + "# Load a list of molecules\n", + "smiles_list = [\"CCOC1=CC=CC=C1C(=O)OCC(=O)NC1=CC=CC=C1\",\n", + " \"NC(=O)C1=C(NC(=O)COC2=CC=CC=C2C(F)(F)F)SC=C1\",\n", + " \"CC(C)NC(=O)CSCC1=CC=CC=C1Br\",\n", + " \"CC1=CC=C(C(=O)NC(C)C)C=C1NC(=O)C1=CC=CO1\",\n", + " \"O=C(CN1CCCCCC1=O)NCC1=CC=C(N2C=CN=C2)C(F)=C1\"\n", + " ]\n", + "mol_list = [Chem.MolFromSmiles(smi) for smi in smiles_list]\n", + "scaffolds = [MurckoScaffold.GetScaffoldForMol(mol) for mol in mol_list]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d5b3010f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + 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+ "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Extracting Murcko scaffolds from list of compounds\n", + "scaffolds = [dm.to_scaffold_murcko(mol) for mol in mol_list]\n", + "dm.to_image(scaffolds, n_cols=3)" + ] + }, + { + "cell_type": "markdown", + "id": "1e167bb7", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "- What is SAR? [https://info.collaborativedrug.com/tofu-content-what-is-sar](https://info.collaborativedrug.com/tofu-content-what-is-sar)\n", + "- [https://datagrok.ai/help/domains/chem/functions/murcko-scaffolds](https://datagrok.ai/help/domains/chem/functions/murcko-scaffolds)\n", + "- [http://practicalcheminformatics.blogspot.com/2021/10/exploratory-data-analysis-with.html](http://practicalcheminformatics.blogspot.com/2021/10/exploratory-data-analysis-with.html)" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/tutorials/new/Generatingconformers.ipynb b/docs/tutorials/new/Generatingconformers.ipynb new file mode 100644 index 00000000..c2c06af4 --- /dev/null +++ b/docs/tutorials/new/Generatingconformers.ipynb @@ -0,0 +1,612 @@ +{ + "cells": [ + { + "attachments": { + "3411d6e3-7efa-43fe-8b8a-669d68efdf0b.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "ad8338f0", + "metadata": {}, + "source": [ + "# Generating Conformers\n", + "\n", + "A drug-like molecule can exist in a variety of diverse 3D shapes depending on the number of rotatable bonds, bond order, torsion, and in general, its degree of freedom. Each individual 3D spatial arrangement of a molecule is defined as ***conformer*** and each conformer may have different properties (e.g. relative energy). This is why the [sampling of the conformational space](https://pubs.acs.org/doi/full/10.1021/acs.jcim.7b00221), often referred to as conformational search, is a key step to understand the 3D properties of a given compound. You must factor in all the possible conformers and their respective properties in order to achieve the best representation of a molecule. It is a necessary step in any [virtual screening](https://en.wikipedia.org/wiki/Virtual_screening#:~:text=Virtual%20screening%20(VS)%20is%20a,a%20protein%20receptor%20or%20enzyme.) campaign.\n", + "\n", + "**Note:** You can see a good visualization on how relative energy of conformers changes based on manual manipulation of bond angles [here](https://www.sas.upenn.edu/~kimg/mcephome/chem502/ethbutconform/ethbutmm2.html#:~:text=The%20highest%20energy%20conformer%2C%20the,energy%20of%203.5803%20kcal%2F%20mol.). \n", + "\n", + "A common term that you will see throughout this example is ***RMSD*** which stands for root-mean-square deviation. RMSD is widely used as a similarity measure when analyzing conformations: the smaller the RMSD between two conformers, the more similar in 3D spatial arrangement they are. Once conformers are generated, they are usually pruned on RMSD, meaning, structures that are redundant and essentially correspond to the same conformation are removed from the list. \n", + "\n", + "**Note:** RMSD is not the only measure of conformer similarity, and it does have its limitations. If you’re interested in learning more about all the various ways in which chemical structural similarity can be measured, read more [here.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6068280/) \n", + "\n", + "### How are conformers generated?\n", + "\n", + "The current default RDKit method used to generate conformers leverages various versions of experimental-torsion distance geometry with additional basic knowledge ([ETKDG](https://pubs.acs.org/doi/full/10.1021/acs.jcim.5b00654)) created by Riniker and Landrum. From the RDKit book the default algorithm followed is:\n", + "\n", + "1. The molecule’s distance bounds matrix is calculated based on the connection table and a set of rules.\n", + "2. The bounds matrix is smoothed using a triangle-bounds smoothing algorithm.\n", + "3. A random distance matrix that satisfies the bound's matrix is generated.\n", + "4. This distance matrix is embedded in 3D dimensions (producing coordinates for each atom).\n", + "5. The resulting coordinates are cleaned up somewhat using the “distance geometry force field”, based on distance constraints from the bounds matrix. \n", + "\n", + "The first 5 steps describe the “ETDG” approach. The additional “K” in ETKDG just defines further constraints from chemical knowledge such as “aromatic rings are to be flat or bonds connected to triple bonds are to be collinear”. These additional constraints introduce a certain level of “chemical awareness” that helps generate correct conformers which are chemically and physically valid. Read more [here](https://www.blopig.com/blog/2016/06/advances-in-conformer-generation-etkdg-and-etdg/), [here](https://greglandrum.github.io/rdkit-blog/conformers/exploration/2021/01/31/looking-at-random-coordinate-embedding.html) and [here](https://greglandrum.github.io/rdkit-blog/conformers/exploration/2021/02/22/etkdg-and-distance-constraints.html). \n", + "\n", + "![image.png](attachment:3411d6e3-7efa-43fe-8b8a-669d68efdf0b.png)\n", + "\n", + "***[Source](https://pubs.acs.org/doi/10.1021/acs.jcim.5b00654)***\n", + "\n", + "## Tutorial\n", + "\n", + "Now let’s start with a tutorial on how you would go about generating conformers via RDKit.\n", + "\n", + "## RDKit Example\n", + "\n", + "Below is an example of how you would go about generating conformers in RDKit." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4e45a284", + "metadata": {}, + "outputs": [], + "source": [ + "from rdkit import Chem\n", + "from rdkit.Chem import AllChem\n", + "from rdkit.Chem import rdDistGeom\n", + "from rdkit.Chem import rdMolAlign\n", + "from rdkit.Chem import rdMolDescriptors\n", + "from rdkit.Chem import rdMolTransforms\n", + "from rdkit.Chem import rdForceFieldHelpers\n", + "\n", + "from rdkit.Chem import PyMol\n", + "import copy\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4b9cefae", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m = Chem.MolFromSmiles('O=C(C)Oc1ccccc1C(=O)O')\n", + "m" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e7b4d603", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m2" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1209acc4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rotatable_bonds = Chem.rdMolDescriptors.CalcNumRotatableBonds(m2)\n", + "rotatable_bonds" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "07d0df61", + "metadata": {}, + "outputs": [], + "source": [ + "# Setup the parameters for the embedding\n", + "params = getattr(rdDistGeom, \"ETDG\")()\n", + "params.randomSeed = 0\n", + "params.enforceChirality = True\n", + "params.useRandomCoords = True\n", + "params.numThreads = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c7c224c9", + "metadata": {}, + "outputs": [], + "source": [ + "# EMbed conformers\n", + "confs = rdDistGeom.EmbedMultipleConfs(m2, numConfs=50, params=params)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8453b480", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "50" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(confs)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "642925b0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0,\n", + " 1,\n", + " 2,\n", + " 3,\n", + " 4,\n", + " 5,\n", + " 6,\n", + " 7,\n", + " 8,\n", + " 9,\n", + " 10,\n", + " 11,\n", + " 12,\n", + " 13,\n", + " 14,\n", + " 15,\n", + " 16,\n", + " 17,\n", + " 18,\n", + " 19,\n", + " 20,\n", + " 21,\n", + " 22,\n", + " 23,\n", + " 24,\n", + " 25,\n", + " 26,\n", + " 27,\n", + " 28,\n", + " 29,\n", + " 30,\n", + " 31,\n", + " 32,\n", + " 33,\n", + " 34,\n", + " 35,\n", + " 36,\n", + " 37,\n", + " 38,\n", + " 39,\n", + " 40,\n", + " 41,\n", + " 42,\n", + " 43,\n", + " 44,\n", + " 45,\n", + " 46,\n", + " 47,\n", + " 48,\n", + " 49]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Minimize energy\n", + "energy_iterations = 200\n", + "results = rdForceFieldHelpers.UFFOptimizeMoleculeConfs(m2, maxIters=energy_iterations)\n", + "energies = [energy for _, energy in results]\n", + "energies = []\n", + "for conf in m2.GetConformers():\n", + " ff = rdForceFieldHelpers.UFFGetMoleculeForceField(m2, confId=conf.GetId())\n", + " energies.append(ff.CalcEnergy())\n", + "energies = np.array(energies)\n", + "# Add the energy as a property to each conformers\n", + "[\n", + " conf.SetDoubleProp(\"rdkit_uff_energy\", energy)\n", + " for energy, conf in zip(energies, m2.GetConformers())\n", + "]\n", + "\n", + "# Now we reorder conformers according to their energies,\n", + "# so the lowest energies conformers are first.\n", + "mol_clone = copy.deepcopy(m2)\n", + "ordered_conformers = [\n", + " conf for _, conf in sorted(zip(energies, mol_clone.GetConformers()), key=lambda x: x[0])\n", + "]\n", + "m2.RemoveAllConformers()\n", + "[m2.AddConformer(conf, assignId=True) for conf in ordered_conformers]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "74756772", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Align conformers to each others\n", + "rdMolAlign.AlignMolConformers(m2)\n", + "m2" + ] + }, + { + "cell_type": "markdown", + "id": "4a5e643a", + "metadata": {}, + "source": [ + "As a beginner, this can be a bit overwhelming and complicated to get the hang of. Let’s see how this would look in Datamol\n", + "\n", + "## Datamol Example" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "aa5b82da", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "import datamol as dm" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9f55f956", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "smiles = \"O=C(C)Oc1ccccc1C(=O)O\"\n", + "mol = dm.to_mol(smiles)\n", + "mol" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "c03e90f5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# generate conformers\n", + "mol = dm.conformers.generate(mol, align_conformers=True)\n", + "mol" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "912fdfe2", + "metadata": {}, + "outputs": [], + "source": [ + "# Get all conformers as a list\n", + "conformers = mol.GetConformers()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "a1b296b0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "50" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(conformers)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "81fb899c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 2.03942419, -1.45922972, -0.5317635 ],\n", + " [ 1.99263631, -1.56446468, 0.71918407],\n", + " [ 3.17475723, -2.13427287, 1.41002923],\n", + " [ 0.83776239, -1.13909896, 1.35783965],\n", + " [-0.24741278, -0.60913374, 0.65486606],\n", + " [-0.31728327, 0.74698269, 0.41866521],\n", + " [-1.37604602, 1.30106187, -0.27254643],\n", + " [-2.40163014, 0.50559754, -0.74968685],\n", + " [-2.35444025, -0.85475256, -0.52596736],\n", + " [-1.27294042, -1.39641191, 0.17657748],\n", + " [-1.21427441, -2.8252044 , 0.4168274 ],\n", + " [-0.25085537, -3.31954322, 1.04442977],\n", + " [-2.22026164, -3.68264236, -0.04010149]])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get the 3D atom positions of the first conformer\n", + "positions = mol.GetConformer(0).GetPositions()\n", + "positions" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "9864a4c2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'rdkit_uff_energy': 35.402383024447225}\n" + ] + } + ], + "source": [ + "# If minimization has been enabled (default to True)\n", + "# you can access the computed energy.\n", + "conf = mol.GetConformer(0)\n", + "props = conf.GetPropsAsDict()\n", + "print(props)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "056e862c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.00000000e+00, 1.01201288e+00, 3.80585511e-02, ...,\n", + " 1.00248204e+00, 6.38152829e-02, 9.30034182e-01],\n", + " [1.01201288e+00, 4.67577303e-08, 1.01828888e+00, ...,\n", + " 7.17687634e-02, 1.01744973e+00, 4.35281059e-01],\n", + " [3.80585511e-02, 1.01828888e+00, 4.67577303e-08, ...,\n", + " 1.01013936e+00, 7.97864852e-02, 9.43285047e-01],\n", + " ...,\n", + " [1.00248204e+00, 7.17687634e-02, 1.01013936e+00, ...,\n", + " 0.00000000e+00, 1.00439835e+00, 4.19279897e-01],\n", + " [6.38152831e-02, 1.01744973e+00, 7.97864852e-02, ...,\n", + " 1.00439835e+00, 4.67577303e-08, 9.36046765e-01],\n", + " [9.30034182e-01, 4.35281059e-01, 9.43285047e-01, ...,\n", + " 4.19279897e-01, 9.36046765e-01, 0.00000000e+00]])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Compute RMSD\n", + "rmsd = dm.conformers.rmsd(mol)\n", + "rmsd" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "5b1878d2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(50, 50)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rmsd.shape" + ] + }, + { + "cell_type": "markdown", + "id": "1a2d80ef", + "metadata": {}, + "source": [ + "**In essentially one line of code, you can generate a list of conformers.** What’s important to understand are some of the key parameters that are factored into this process. In general, sticking with the defaults in Datamol will suffice in most cases, but if you want to make specific modifications, you can. If you’re interested in learning more about all the algorithms underlying conformer generation, read [this](https://pubs.acs.org/doi/10.1021/acs.jcim.7b00221). \n", + "\n", + " A few parameters to highlight: \n", + "\n", + "- **n_confs** - Specifying the number of conformers to generate. This is based on the number of rotatable bonds and, by default, this is set to 200 if there are more than 8 rotatable bonds and 50 if there are less than 8. Theoretically, there are an unlimited number of conformers that can be derived from a single rotatable bond, however, in practice, not all the conformer structures make sense since only “stable” conformers are relevant in this context. This is why the defaults are set in place. Hypothetically, if you only have 2 rotatable bonds and you set n_confs to 2,000,000, not only will this be computationally expensive but a lot of the conformers generated will start to have non-relevant structures that are not useful.\n", + "- **add_hs** - By default, hydrogen atoms are added before embedding because it is critical to generating high quality 3D conformations.\n", + "- **minimize_energy** - Minimizing energy releases the strain of the generated conformation to the closest local minima enabling you to find a more relevant conformation. In other words, ***finding the conformer that is most likely to exist***. There are multiple force fields that you can apply.\n", + "- **method -** Within the ETKDG method, there are various versions that can be selected to generate conformers.\n", + "- **energy_iterations -** This options allows you to specify how many iterations of conformer generation you want to go through if you have enabled energy minimization. In general, the more iterations you specify, the more accurate the conformers. However, there is a trade off between the number of iterations and computation speed. Running through 1000 iterations will be significantly more expensive computationally as opposed to 100 iterations.\n", + "- **rms_cutoff** is the max RMSD value for which two conformers are considered to be the same.\n", + "\n", + "The full table of parameters along with their definitions is shown below:" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/tutorials/new/Preprocessing.ipynb b/docs/tutorials/new/Preprocessing.ipynb new file mode 100644 index 00000000..c9ad7173 --- /dev/null +++ b/docs/tutorials/new/Preprocessing.ipynb @@ -0,0 +1,555 @@ +{ + "cells": [ + { + "attachments": { + "8e26de78-ec19-4302-8b3e-1cb0e36c99b5.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "0a462b7a", + "metadata": {}, + "source": [ + "# Pre-processing Molecules\n", + "\n", + "You’ve probably heard the adage “garbage in, garbage out” before in reference to the importance of data quality when it comes to AI/ML. The same holds true in the field of drug discovery. Given the scarcity and often non-consistent quality of available data for drug discovery, an initial clean up is almost always required to ensure the use of high quality data in the generation of your models. If you don’t do this, the use of lesser quality data would definitely impact the accuracy of your models in any downstream task. Pre-processing of data and molecules is extremely important, let’s dive in!\n", + "\n", + "## Representing Molecules\n", + "\n", + "There are many ways in which molecules can be represented. In other words, how can we effectively express the complexity of a molecule in a way that machines can understand? Here are some existing methods: \n", + "\n", + "- [Molfile](https://en.wikipedia.org/wiki/Chemical_table_file) - A table that holds information about the atoms, bonds, connectivity and coordinates of a molecule\n", + "- **SMILES** - stands for **S**implified **M**olecular **I**nput **L**ine-**E**ntry **S**ystem and the name essentially describes it. It’s a line notation for encoding molecular structures where atoms are represented by their standard abbreviation as a chemical element (i.e. C for carbon, N for nitrogen etc.). Multiple symbols are then used to define elements with charges, bonds, rings, aromaticity, stereochemistry, and much more. For more detail, read [here](https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system#Terminology).\n", + " - As an example, CCO, OCC and C(O)C all refer to ethanol. Having a number of equally valid SMILES strings for a given molecule can be an issue, therefore, canonicalization algorithms can be used to generate canonical SMILES to produce unique and consistent SMILES strings.\n", + " - Although SMILES are commonly used, they are not perfect. In a generative model using SMILES as inputs and outputs, there are often invalid SMILES strings that are produced (i.e. the SMILE string corresponds to an invalid molecule that violates basic chemical rules).\n", + "- **SELFIES** - stands for **SELF**-referenc**I**ng **E**mbedded **S**trings, it is another string-based representation for molecules that is generally more suitable for ML models and exhibits more robustness (i.e. more SELFIE strings corresponds to a valid molecule). Read more [here](https://aspuru.substack.com/p/molecular-graph-representations-and).\n", + "- **InChi** - another string-based method of representing chemical structures developed by IUPAC. Read more [here](https://iupac.org/100/stories/what-on-earth-is-inchi/). On the other hand, [InChi key](https://dev.drugbank.com/guides/terms/inchi-key) is a newer version of InChi that is only useful to identify molecules, however, it is impossible to reconstruct a molecule from an InChi key.\n", + "\n", + "See below for a graphic that summarizes some of the methods discussed in the section above: \n", + "\n", + "![image.png](attachment:8e26de78-ec19-4302-8b3e-1cb0e36c99b5.png)\n", + "\n", + "***[Source](https://www.researchgate.net/publication/344906202_Chemoinformatics-based_enumeration_of_chemical_libraries_a_tutorial)***\n", + "\n", + "**Note:** it’s important to understand that all forms of molecular representation have their pro’s and con’s. It’s less of a “one-size-fits-all” and more about picking and choosing specific methods to represent a molecule given your specific use case. \n", + "\n", + "## Sanitize and Standardize\n", + "\n", + "***Molecular sanitization*** is the process of ensuring that the molecules in your dataset ***are realistic***. You can read more about the sanitization procedure as applied in the RDKit [here](https://www.rdkit.org/docs/RDKit_Book.html#molecular-sanitization). In Datamol, there are a few extra steps as well, sanitization is done under the following procedure: \n", + "\n", + "1. Adjusting for nitrogen aromaticity since faulty valence for nitrogen in aromatic rings is [currently](https://github.com/rdkit/rdkit/issues/2011) an issue in RDKit through the Sanifix algorithm. \n", + "2. An extra conversion is done from mol → smiles → mol to ensure that the molecules are valid SMILES.\n", + "3. Charge neutralization - this is NOT charge removal, it attempts to correct valence issues arising from incorrect charges being placed on atoms.\n", + "\n", + "Users can control the application of the sanifix algorithim or charge neutralization, users can toggle the respective parameters ***sanifix*** and ***charge_neutral*** to be TRUE/FALSE. \n", + "\n", + "The process of **standardization** is used to generate ***canonical SMILES.*** It is currently done using the following procedure which can be controlled by the user through the described parameters below:\n", + "\n", + "- ***disconnect_metals -*** metal disconnection\n", + " - Depending on the source of the database, some compounds may be reported in salt form or associated to metallic ions (e.g. the sodium salt of a carboxylic compound). In most cases, these counter-ions are not relevant so the use of this function is required before further utilization of the dataset.)\n", + " - More details [here](https://molvs.readthedocs.io/en/latest/guide/standardize.html#disconnect-metals)\n", + "- ***normalize -*** ion (charge) and functional groups normalization\n", + " - It corrects drawing errors and standardizes functional groups in the molecule as well as ensuring the overall proper charge of the compound\n", + " - More details [here](https://molvs.readthedocs.io/en/latest/guide/standardize.html#apply-normalization-rules)\n", + "- ***reionize -*** reionization of the molecule (protonation following the acidity order)\n", + " - If one or more acidic functionalities are present in the molecule, this option ensures the correct neutral/ionized state for such functional groups. Molecules are uncharged by adding and/or removing hydrogens. For zwitterions, hydrogens are moved to eliminate charges where possible. However, in cases where there is a positive charge that is not neutralizable, an attempt is made to also preserve the corresponding negative charge\n", + " - Read more [here](https://molvs.readthedocs.io/en/latest/guide/standardize.html#reionize-acids)\n", + "- ***uncharge* -** charge removal\n", + " - This option neutralize the molecule by reversing the protonation state of protonated and deprotonated groups, if present (e.g. a carboxylate is re-protonated to the corresponding carboxylic acid).\n", + " - In cases where there is a positive charge that is not neutralizable, an attempt is made to also preserve the corresponding negative charge to ensure a net zero charge.\n", + "- ***stereo -*** stereochemistry proper reassignment if missing.\n", + " - Stereochemical information is corrected and/or added if missing using built-in RDKit functionality to force a clean recalculation of stereochemistry\n", + "\n", + "The actual processes for sanitization and standardization described can get a bit too detailed, with lots of chemistry terminology. We recommend just sticking with the defaults already set in Datamol. It’s enough just to understand the importance of why we sanitize and standardize our datasets as a key step in the pre-processing, as you continue spending time in the AI/ML for drug discovery field, you will get more familiar with the details. \n", + "\n", + "## Tutorial\n", + "\n", + "In this tutorial, let’s walk through how to load a dataset and then apply the described pre-processing pipeline which will take a list of molecules and: \n", + "\n", + "- Convert to a mol.\n", + "- Fix common errors in the mol.\n", + "- Sanitize the mol.\n", + "- Standardize the mol.\n", + "- Generate a standardized SMILES.\n", + "- Generate SELFIES.\n", + "- Generate InChi and InChi key.\n", + "- Save the results as CSV or SDF file formats.\n", + "\n", + "From here, we will generate a table where it can more easily visualized. The option of parallelization will also be shown: \n", + "\n", + "**Note:** parallelizing the preprocessing will only be faster if your dataset is very large. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2a3c8bf5", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import datamol as dm\n", + "\n", + "dm.disable_rdkit_log()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "fc621492", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(642, 4)\n" + ] + }, + { + "data": { + "text/html": [ + "
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iupacsmilesexptcalc
04-methoxy-N,N-dimethyl-benzamideCN(C)C(=O)c1ccc(cc1)OC-11.01-9.625
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32-ethylpyrazineCCc1cnccn1-5.45-5.809
4heptan-1-olCCCCCCCO-4.21-2.917
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" + ], + "text/plain": [ + " iupac smiles expt calc\n", + "0 4-methoxy-N,N-dimethyl-benzamide CN(C)C(=O)c1ccc(cc1)OC -11.01 -9.625\n", + "1 methanesulfonyl chloride CS(=O)(=O)Cl -4.87 -6.219\n", + "2 3-methylbut-1-ene CC(C)C=C 1.83 2.452\n", + "3 2-ethylpyrazine CCc1cnccn1 -5.45 -5.809\n", + "4 heptan-1-ol CCCCCCCO -4.21 -2.917" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load a dataset\n", + "data = dm.data.freesolv()\n", + "print(data.shape)\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f7f710f1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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iupacsmilesexptcalcstandard_smilesselfiesinchiinchikey
04-methoxy-N,N-dimethyl-benzamideCN(C)C(=O)c1ccc(cc1)OC-11.01-9.625COc1ccc(C(=O)N(C)C)cc1[C][O][C][=C][C][=C][Branch1][#Branch2][C][=Br...InChI=1S/C10H13NO2/c1-11(2)10(12)8-4-6-9(13-3)...OCGXPFSUJVHRHA-UHFFFAOYSA-N
1methanesulfonyl chlorideCS(=O)(=O)Cl-4.87-6.219CS(=O)(=O)Cl[C][S][=Branch1][C][=O][=Branch1][C][=O][Cl]InChI=1S/CH3ClO2S/c1-5(2,3)4/h1H3QARBMVPHQWIHKH-UHFFFAOYSA-N
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32-ethylpyrazineCCc1cnccn1-5.45-5.809CCc1cnccn1[C][C][C][=C][N][=C][C][=N][Ring1][=Branch1]InChI=1S/C6H8N2/c1-2-6-5-7-3-4-8-6/h3-5H,2H2,1H3KVFIJIWMDBAGDP-UHFFFAOYSA-N
4heptan-1-olCCCCCCCO-4.21-2.917CCCCCCCO[C][C][C][C][C][C][C][O]InChI=1S/C7H16O/c1-2-3-4-5-6-7-8/h8H,2-7H2,1H3BBMCTIGTTCKYKF-UHFFFAOYSA-N
\n", + "
" + ], + "text/plain": [ + " iupac smiles expt calc \\\n", + "0 4-methoxy-N,N-dimethyl-benzamide CN(C)C(=O)c1ccc(cc1)OC -11.01 -9.625 \n", + "1 methanesulfonyl chloride CS(=O)(=O)Cl -4.87 -6.219 \n", + "2 3-methylbut-1-ene CC(C)C=C 1.83 2.452 \n", + "3 2-ethylpyrazine CCc1cnccn1 -5.45 -5.809 \n", + "4 heptan-1-ol CCCCCCCO -4.21 -2.917 \n", + "\n", + " standard_smiles selfies \\\n", + "0 COc1ccc(C(=O)N(C)C)cc1 [C][O][C][=C][C][=C][Branch1][#Branch2][C][=Br... \n", + "1 CS(=O)(=O)Cl [C][S][=Branch1][C][=O][=Branch1][C][=O][Cl] \n", + "2 C=CC(C)C [C][=C][C][Branch1][C][C][C] \n", + "3 CCc1cnccn1 [C][C][C][=C][N][=C][C][=N][Ring1][=Branch1] \n", + "4 CCCCCCCO [C][C][C][C][C][C][C][O] \n", + "\n", + " inchi \\\n", + "0 InChI=1S/C10H13NO2/c1-11(2)10(12)8-4-6-9(13-3)... \n", + "1 InChI=1S/CH3ClO2S/c1-5(2,3)4/h1H3 \n", + "2 InChI=1S/C5H10/c1-4-5(2)3/h4-5H,1H2,2-3H3 \n", + "3 InChI=1S/C6H8N2/c1-2-6-5-7-3-4-8-6/h3-5H,2H2,1H3 \n", + "4 InChI=1S/C7H16O/c1-2-3-4-5-6-7-8/h8H,2-7H2,1H3 \n", + "\n", + " inchikey \n", + "0 OCGXPFSUJVHRHA-UHFFFAOYSA-N \n", + "1 QARBMVPHQWIHKH-UHFFFAOYSA-N \n", + "2 YHQXBTXEYZIYOV-UHFFFAOYSA-N \n", + "3 KVFIJIWMDBAGDP-UHFFFAOYSA-N \n", + "4 BBMCTIGTTCKYKF-UHFFFAOYSA-N " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "smiles_column = \"smiles\"\n", + "\n", + "def _preprocess(row):\n", + " mol = dm.to_mol(row[smiles_column], ordered=True)\n", + " mol = dm.fix_mol(mol)\n", + " mol = dm.sanitize_mol(mol, sanifix=True, charge_neutral=False)\n", + " mol = dm.standardize_mol(mol, disconnect_metals=False, normalize=True, reionize=True, uncharge=False, stereo=True)\n", + "\n", + " row[\"standard_smiles\"] = dm.standardize_smiles(dm.to_smiles(mol))\n", + " row[\"selfies\"] = dm.to_selfies(mol)\n", + " row[\"inchi\"] = dm.to_inchi(mol)\n", + " row[\"inchikey\"] = dm.to_inchikey(mol)\n", + " return row\n", + "\n", + "data_clean = data.apply(_preprocess, axis=1) \n", + "data_clean.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "66298eaf", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9669398e2f064c57b28c7d2d847479aa", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/642 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
iupacsmilesexptcalcstandard_smilesselfiesinchiinchikey
04-methoxy-N,N-dimethyl-benzamideCN(C)C(=O)c1ccc(cc1)OC-11.01-9.625COc1ccc(C(=O)N(C)C)cc1[C][O][C][=C][C][=C][Branch1][#Branch2][C][=Br...InChI=1S/C10H13NO2/c1-11(2)10(12)8-4-6-9(13-3)...OCGXPFSUJVHRHA-UHFFFAOYSA-N
1methanesulfonyl chlorideCS(=O)(=O)Cl-4.87-6.219CS(=O)(=O)Cl[C][S][=Branch1][C][=O][=Branch1][C][=O][Cl]InChI=1S/CH3ClO2S/c1-5(2,3)4/h1H3QARBMVPHQWIHKH-UHFFFAOYSA-N
23-methylbut-1-eneCC(C)C=C1.832.452C=CC(C)C[C][=C][C][Branch1][C][C][C]InChI=1S/C5H10/c1-4-5(2)3/h4-5H,1H2,2-3H3YHQXBTXEYZIYOV-UHFFFAOYSA-N
32-ethylpyrazineCCc1cnccn1-5.45-5.809CCc1cnccn1[C][C][C][=C][N][=C][C][=N][Ring1][=Branch1]InChI=1S/C6H8N2/c1-2-6-5-7-3-4-8-6/h3-5H,2H2,1H3KVFIJIWMDBAGDP-UHFFFAOYSA-N
4heptan-1-olCCCCCCCO-4.21-2.917CCCCCCCO[C][C][C][C][C][C][C][O]InChI=1S/C7H16O/c1-2-3-4-5-6-7-8/h8H,2-7H2,1H3BBMCTIGTTCKYKF-UHFFFAOYSA-N
\n", + "" + ], + "text/plain": [ + " iupac smiles expt calc \\\n", + "0 4-methoxy-N,N-dimethyl-benzamide CN(C)C(=O)c1ccc(cc1)OC -11.01 -9.625 \n", + "1 methanesulfonyl chloride CS(=O)(=O)Cl -4.87 -6.219 \n", + "2 3-methylbut-1-ene CC(C)C=C 1.83 2.452 \n", + "3 2-ethylpyrazine CCc1cnccn1 -5.45 -5.809 \n", + "4 heptan-1-ol CCCCCCCO -4.21 -2.917 \n", + "\n", + " standard_smiles selfies \\\n", + "0 COc1ccc(C(=O)N(C)C)cc1 [C][O][C][=C][C][=C][Branch1][#Branch2][C][=Br... \n", + "1 CS(=O)(=O)Cl [C][S][=Branch1][C][=O][=Branch1][C][=O][Cl] \n", + "2 C=CC(C)C [C][=C][C][Branch1][C][C][C] \n", + "3 CCc1cnccn1 [C][C][C][=C][N][=C][C][=N][Ring1][=Branch1] \n", + "4 CCCCCCCO [C][C][C][C][C][C][C][O] \n", + "\n", + " inchi \\\n", + "0 InChI=1S/C10H13NO2/c1-11(2)10(12)8-4-6-9(13-3)... \n", + "1 InChI=1S/CH3ClO2S/c1-5(2,3)4/h1H3 \n", + "2 InChI=1S/C5H10/c1-4-5(2)3/h4-5H,1H2,2-3H3 \n", + "3 InChI=1S/C6H8N2/c1-2-6-5-7-3-4-8-6/h3-5H,2H2,1H3 \n", + "4 InChI=1S/C7H16O/c1-2-3-4-5-6-7-8/h8H,2-7H2,1H3 \n", + "\n", + " inchikey \n", + "0 OCGXPFSUJVHRHA-UHFFFAOYSA-N \n", + "1 QARBMVPHQWIHKH-UHFFFAOYSA-N \n", + "2 YHQXBTXEYZIYOV-UHFFFAOYSA-N \n", + "3 KVFIJIWMDBAGDP-UHFFFAOYSA-N \n", + "4 BBMCTIGTTCKYKF-UHFFFAOYSA-N " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "smiles_column = \"smiles\"\n", + "\n", + "def _preprocess(i, row):\n", + "\n", + " dm.disable_rdkit_log()\n", + "\n", + " mol = dm.to_mol(row[smiles_column], ordered=True)\n", + " mol = dm.fix_mol(mol)\n", + " mol = dm.sanitize_mol(mol, sanifix=True, charge_neutral=False)\n", + " mol = dm.standardize_mol(mol, disconnect_metals=False, normalize=True, reionize=True, uncharge=False, stereo=True)\n", + "\n", + " row[\"standard_smiles\"] = dm.standardize_smiles(dm.to_smiles(mol))\n", + " row[\"selfies\"] = dm.to_selfies(mol)\n", + " row[\"inchi\"] = dm.to_inchi(mol)\n", + " row[\"inchikey\"] = dm.to_inchikey(mol)\n", + " return row\n", + "\n", + "data_clean = dm.parallelized(_preprocess, data.iterrows(), arg_type='args', progress=True, total=len(data))\n", + "data_clean = pd.DataFrame(data_clean)\n", + "data_clean.head()" + ] + }, + { + "cell_type": "markdown", + "id": "bc123d3a", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "- [https://depth-first.com/articles/2020/07/27/a-guide-to-molecular-standardization/](https://depth-first.com/articles/2020/07/27/a-guide-to-molecular-standardization/)\n", + "- Wikipedia - [https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system#Terminology](https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system#Terminology)" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/mkdocs.yml b/mkdocs.yml index f6ffcbfe..d0b6f3ec 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -26,8 +26,6 @@ nav: - datamol.viz: api/datamol.viz.md - datamol.fragment: api/datamol.fragment.md - datamol.scaffold: api/datamol.scaffold.md - - datamol.reactions: api/datamol.reactions.md - - datamol.actions: api/datamol.actions.md - datamol.molar: api/datamol.molar.md - datamol.align: api/datamol.align.md - datamol.utils: api/datamol.utils.md @@ -89,6 +87,7 @@ plugins: - mkdocs-jupyter: execute: false # kernel_name: python3 + ignore: ["tutorials/new/*.ipynb"] - mike: version_selector: true diff --git a/news/tutos.rst b/news/tutos.rst new file mode 100644 index 00000000..7c344d3d --- /dev/null +++ b/news/tutos.rst @@ -0,0 +1,24 @@ +**Added:** + +* + +**Changed:** + +* Revamped all the datamol tutorials and add new tutorials. +* Improve documentation for `dm.standardize_mol()` + +**Deprecated:** + +* + +**Removed:** + +* Remove unused and unmaintained `dm.actions` and `dm.reactions` module. + +**Fixed:** + +* + +**Security:** + +* diff --git a/pyproject.toml b/pyproject.toml index b34158f7..23473fc3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ exclude = 'typings/' [tool.pytest.ini_options] minversion = "6.0" -addopts = "--verbose --cov=datamol --cov-report xml --cov-report term --cov-report html" +addopts = "--verbose --cov=datamol" testpaths = ["tests"] filterwarnings = ["ignore::DeprecationWarning:rdkit.*:"] diff --git a/tests/test_actions.py b/tests/test_actions.py deleted file mode 100644 index bd91d025..00000000 --- a/tests/test_actions.py +++ /dev/null @@ -1,78 +0,0 @@ -from re import L -import datamol as dm - - -def test_pick_atom_idx(): - smiles = "OC1=CC2CCCCC2[N:1]=C1" - mol = dm.to_mol(smiles) - - assert isinstance(dm.actions.pick_atom_idx(mol), int) - assert dm.actions.pick_atom_idx(mol) <= mol.GetNumAtoms() - - -def test_all_bond_remove(): - - smiles = "OC1=CC2CCCCC2[N:1]=C1" - mol = dm.to_mol(smiles) - - mols = dm.actions.all_bond_remove(mol) - assert isinstance(mols, list) - - -def test_add_remove_bond(): - mol = dm.to_mol("CC(=O)OC1=CC=CC=C1C(=O)O") - - tmpmol = dm.actions.remove_bond_between(mol, mol.GetAtomWithIdx(0), mol.GetAtomWithIdx(1)) - assert dm.to_inchikey(tmpmol) == "LWXRIQYZZCXYDI-UHFFFAOYSA-N" - - tmpmol = dm.actions.remove_bond_between(mol, 0, 1) - assert dm.to_inchikey(tmpmol) == "LWXRIQYZZCXYDI-UHFFFAOYSA-N" - - tmpmol = dm.actions.remove_bond_between(mol, 0, 1, sanitize=False) - assert dm.to_inchikey(tmpmol) == "LWXRIQYZZCXYDI-UHFFFAOYSA-N" - - tmpmol = dm.actions.remove_bond_between( - mol, mol.GetAtomWithIdx(3), mol.GetAtomWithIdx(4), sanitize=False - ) - tmpmol = dm.actions.add_bond_between(tmpmol, 3, 4, dm.SINGLE_BOND, sanitize=True) - assert dm.to_inchikey(tmpmol) == "BSYNRYMUTXBXSQ-UHFFFAOYSA-N" - - tmpmol = dm.actions.remove_bond_between( - mol, mol.GetAtomWithIdx(3), mol.GetAtomWithIdx(4), sanitize=False - ) - tmpmol = dm.actions.add_bond_between( - tmpmol, tmpmol.GetAtomWithIdx(3), tmpmol.GetAtomWithIdx(4), dm.SINGLE_BOND, sanitize=True - ) - assert dm.to_inchikey(tmpmol) == "BSYNRYMUTXBXSQ-UHFFFAOYSA-N" - - -def test_update_bond(): - - mol = dm.to_mol("CC(=O)OC1=CC=CC=C1C(=O)O") - - with dm.without_rdkit_log(): - assert dm.actions.update_bond(mol, 0, dm.DOUBLE_BOND) is None - - new_mol = dm.actions.update_bond( - mol, mol.GetBondBetweenAtoms(0, 1), dm.DOUBLE_BOND, sanitize=False - ) - assert new_mol is not None - assert dm.to_inchikey(new_mol) == "YMTXMCUZWXKNSB-UHFFFAOYSA-N" - - new_mol = dm.actions.update_bond(mol, 0, dm.DOUBLE_BOND, sanitize=False) - assert new_mol is not None - assert dm.to_inchikey(new_mol) == "YMTXMCUZWXKNSB-UHFFFAOYSA-N" - - -def test_all_atom_join(): - - mol = dm.to_mol("CC(=O)OC1=CC=CC=C1C(=O)O") - tmpmol = dm.actions.remove_bond_between(mol, 7, 8, sanitize=False) - mols = dm.actions.all_atom_join(tmpmol, 7, 8) - assert dm.same_mol(mols[0], mol) - - mols = dm.actions.all_atom_join(mol, 7, 8) - assert dm.to_inchikey(mols[0]) == "HGYLTEABMPLEAQ-UHFFFAOYSA-N" - - mols = dm.actions.all_atom_join(mol, 7, 6) - assert dm.to_inchikey(mols[0]) == "WSZJSUJBYYBSDX-UHFFFAOYSA-N" diff --git a/tests/test_reactions.py b/tests/test_reactions.py deleted file mode 100644 index d2f3be78..00000000 --- a/tests/test_reactions.py +++ /dev/null @@ -1,4 +0,0 @@ -def test_me(): - - # TODO - pass From 00532f9bb8ffd1ebf11d946363f924174739c972 Mon Sep 17 00:00:00 2001 From: Hadrien Mary Date: Sun, 4 Sep 2022 09:53:01 -0400 Subject: [PATCH 02/15] WIP --- datamol/align.py | 2 + datamol/descriptors/descriptors.py | 3 - docs/tutorials/Cluster_Molecules.ipynb | 270 --- docs/tutorials/Clustering.ipynb | 1066 +++++++++++ docs/tutorials/Filesystem.ipynb | 562 +----- docs/tutorials/Fragment.ipynb | 1314 +++++++++++++ docs/tutorials/Fragment_and_Scaffold.ipynb | 355 ---- docs/tutorials/Preprocessing_Molecules.ipynb | 559 ------ docs/tutorials/The_Basics.ipynb | 2 +- docs/tutorials/Visualization.ipynb | 741 +++----- docs/tutorials/new/Clustering.ipynb | 1658 ----------------- docs/tutorials/new/Fragment.ipynb | 1442 -------------- ...yscaffolds.ipynb => Fuzzy_Scaffolds.ipynb} | 0 ...scaffold.ipynb => Generate_Scaffold.ipynb} | 0 ...mers.ipynb => Generating_Conformers.ipynb} | 0 mkdocs.yml | 5 +- news/tutos.rst | 1 + 17 files changed, 2631 insertions(+), 5349 deletions(-) delete mode 100644 docs/tutorials/Cluster_Molecules.ipynb create mode 100644 docs/tutorials/Clustering.ipynb create mode 100644 docs/tutorials/Fragment.ipynb delete mode 100644 docs/tutorials/Fragment_and_Scaffold.ipynb delete mode 100644 docs/tutorials/Preprocessing_Molecules.ipynb delete mode 100644 docs/tutorials/new/Clustering.ipynb delete mode 100644 docs/tutorials/new/Fragment.ipynb rename docs/tutorials/new/{Fuzzyscaffolds.ipynb => Fuzzy_Scaffolds.ipynb} (100%) rename docs/tutorials/new/{Generatescaffold.ipynb => Generate_Scaffold.ipynb} (100%) rename docs/tutorials/new/{Generatingconformers.ipynb => Generating_Conformers.ipynb} (100%) diff --git a/datamol/align.py b/datamol/align.py index 9c1a52fb..2e8199f3 100644 --- a/datamol/align.py +++ b/datamol/align.py @@ -146,6 +146,7 @@ def auto_align_many( Args: mols: A list of molecules to auto align. partition_method: Partition method to use: + - 'scaffold': Cluster molecules by Murcko scaffold. - 'strip-scaffold': Cluster molecules by Murcko scaffold, but remove all atoms not in the core. @@ -155,6 +156,7 @@ def auto_align_many( generic but keeping the bond order informations. - 'cluster': Cluster the molecules using Butina frm RDKit with `dm.cluster_mols`. Cautious as the method 'cluster' is very sensitive to the cutoff. + copy: Whether to copy the molecules before aligning them. cluster_cutoff: Optional cluster cutoff. allow_r_groups: Optional, if True, terminal dummy atoms in the diff --git a/datamol/descriptors/descriptors.py b/datamol/descriptors/descriptors.py index b6962677..b6d7ffbc 100644 --- a/datamol/descriptors/descriptors.py +++ b/datamol/descriptors/descriptors.py @@ -1,6 +1,3 @@ -from typing import List -from typing import Set - import sys import os diff --git a/docs/tutorials/Cluster_Molecules.ipynb b/docs/tutorials/Cluster_Molecules.ipynb deleted file mode 100644 index 63b5fcf8..00000000 --- a/docs/tutorials/Cluster_Molecules.ipynb +++ /dev/null @@ -1,270 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import operator\n", - "import pandas as pd\n", - "\n", - "import datamol as dm" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Cluster a list of molecules" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Get some mols\n", - "data = dm.data.freesolv()\n", - "smiles = data[\"smiles\"].iloc[:].tolist()\n", - "mols = [dm.to_mol(s) for s in smiles]\n", - "\n", - "# Cluster the mols\n", - "clusters, mol_clusters = dm.cluster_mols(mols, cutoff=0.7)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Cluster #1\n", - "dm.viz.to_image(mol_clusters[0], mol_size=(100, 100), n_cols=6, max_mols=18)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Cluster #2\n", - "dm.viz.to_image(mol_clusters[1], mol_size=(100, 100), n_cols=6, max_mols=18)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Cluster #3\n", - "dm.viz.to_image(mol_clusters[2], mol_size=(100, 100), n_cols=6, max_mols=18)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Cluster #4\n", - "dm.viz.to_image(mol_clusters[3], mol_size=(100, 100), n_cols=6, max_mols=18)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "### Pick diverse molecules from a list (based on their fingerprint)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Get some mols\n", - "data = dm.data.freesolv()\n", - "smiles = data[\"smiles\"].iloc[:].tolist()\n", - "mols = [dm.to_mol(s) for s in smiles]\n", - "\n", - "indices, picks = dm.pick_diverse(mols, npick=18)\n", - "dm.viz.to_image(picks, mol_size=(100, 100), n_cols=6)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "### Pick centroids from a set of molecules" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "data = dm.data.freesolv()\n", - "smiles = data[\"smiles\"].iloc[:].tolist()\n", - "mols = [dm.to_mol(s) for s in smiles]\n", - "\n", - "indices, centroids = dm.pick_centroids(mols, npick=18, threshold=0.7, method=\"sphere\", n_jobs=-1)\n", - "dm.viz.to_image(centroids, mol_size=(100, 100), n_cols=6)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Assign molecules to the existing centroids for clustering" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# remove centroids for comparison\n", - "query = [m for i, m in enumerate(mols) if i not in indices]\n", - "cluster_map, cluster_list = dm.assign_to_centroids(query, centroids, n_jobs=-1)\n", - "centroid_0 = centroids[0]\n", - "cluster_0 = cluster_list[0][:17]\n", - "legends = ['Centroid'] + [f\"Mol_{i}\" for i in range(len(cluster_0))]\n", - "dm.viz.to_image([centroid_0]+cluster_0, legends=legends, mol_size=(100, 100), n_cols=6)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:datamol]", - "language": "python", - "name": "conda-env-datamol-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/tutorials/Clustering.ipynb b/docs/tutorials/Clustering.ipynb new file mode 100644 index 00000000..5a2f176e --- /dev/null +++ b/docs/tutorials/Clustering.ipynb @@ -0,0 +1,1066 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "85f43cfe", + "metadata": {}, + "source": [ + "# Clustering Molecules\n", + "\n", + "💡 [Clustering](https://en.wikipedia.org/wiki/Cluster_analysis) - the act of *grouping a set of objects in such a way that objects in the same group (called a **cluster**) are more similar (in some sense) to each other than to those in other groups (clusters).*\n", + "\n", + "One of the largest challenges in early-stage drug discovery is narrowing down the massive chemical space of approximately **10 to the power of 60 molecules (10^60)** to a list of molecules that have the desired properties for a specific target of interest. This is where computational approaches comes in, taking a large library of small molecules and reduce its size by filtering in/out molecules based on similarity, patterns, predicted physicochemical properties, specific rules, etc. This selection process allows scientists to focus on compounds with the highest chance of success before experimental testing in a lab, saving time and money.\n", + "\n", + "Clustering molecules is an extremely useful process where you can easily manipulate and subdivide large datasets to group compounds into smaller clusters with similar properties. Comparing molecules and their similarities can then be used to discover new molecules with optimal properties and desired biological activity. \n", + "\n", + "### How are compounds clustered?\n", + "\n", + "Compounds can be clustered via multiple clustering algorithms. There are also multiple ways to measure similarity between compounds, and theoretically, any [molecular descriptor](https://pubs.acs.org/doi/abs/10.1021/jm401411z) can be used. ***The current common approach for structural clustering is the [Butina](https://pubs.acs.org/doi/abs/10.1021/ci9803381) algorithm which can use multiple similarity measures. In Datamol, the measure set as the default is the [Tanimoto similarity](http://www.biotech.fyicenter.com/1000134_What_Is_Tanimoto_coefficient.html#:~:text=Tanimoto%20coefficient%20is%20a%20metric,union%20of%20the%20two%20sets.) index, measured on a scale between 0 (not similar) to 1 (most similar)***. After clustering molecules, you can also identify **centroids**. These are essentially the molecules in the middle of the cluster and are frequently used to **represent** **the cluster as a whole**. \n", + "\n", + "For a more detailed breakdown of clustering methods and their uses in computational chemistry, read [here](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.630&rep=rep1&type=pdf).\n", + "\n", + "**Note:** centroids are highlighted here only as an example. Centroid identification is not linked to clustering itself, and there are algorithms commonly utilized that have nothing to do with centroids (i.e. [hierarchical clustering](https://chemaxon.com/presentation/hierarchical-clustering-of-chemical-structures-by-maximum-common-substructures)).\n", + "\n", + "## Molecular Fingerprints\n", + "\n", + "In order for us to perform machine learning techniques or statistical analyses on molecules, we must represent molecules as mathematical objects (i.e vectors). Molecular fingerprints essentially encode the structural characteristics of molecules in the form of vectors enabling us to subsequently leverage statistical techniques to uncover new insights. \n", + "\n", + "The most common fingerprint used today is ECFP4 (extended connectivity fingerprints), also known as the Morgan fingerprint. [Here](https://towardsdatascience.com/a-practical-introduction-to-the-use-of-molecular-fingerprints-in-drug-discovery-7f15021be2b1) is a practical blog that explains what and how to use ECFP4. \n", + "\n", + "## Tutorial\n", + "\n", + "This tutorial will walk you through the following:\n", + "\n", + "1. Loading an example dataset\n", + "2. Calculate fingerprints\n", + "3. Then generate distance matrix\n", + "4. Cluster with the Butina algorithm \n", + "5. Pick diverse molecules from a list\n", + " 1. Why is this useful? \n", + " 1. Resource limitations generally prevent you from experimentally testing as many compounds as you want/are available. Therefore, you want to be able to collect as much information as possible through diversity. By selecting diverse molecules (i.e. one representative example from each chemical series in a list), you can quickly gain information around the effect of structural changes on in vitro activity while exploring a larger chemical space in fewer “shots”.\n", + "6. Pick centroids from a set of molecules\n", + "\n", + "First let’s see what this process would look like on RDKit: \n", + "\n", + "## RDKit Example" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e69926c7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([352, 2, 10, 56, 61, 85, 92, 176, 262, 408]),\n", + " [,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import operator\n", + "\n", + "import datamol as dm\n", + "import numpy as np\n", + "\n", + "from rdkit import Chem\n", + "from rdkit.Chem import DataStructs\n", + "from rdkit.ML.Cluster import Butina\n", + "from rdkit.SimDivFilters.rdSimDivPickers import MaxMinPicker\n", + "\n", + "### Clustering compounds\n", + "\n", + "# Get some mols\n", + "data = dm.data.freesolv()\n", + "smiles = data[\"smiles\"].iloc[:].tolist()\n", + "mols = [Chem.MolFromSmiles(s) for s in smiles]\n", + "\n", + "# Create fingerprints\n", + "fps = [Chem.RDKFingerprint(x) for x in mols]\n", + "\n", + "# Calculate distance matrix\n", + "dists = []\n", + "n_mols = len(mols)\n", + "\n", + "for i in range(1, n_mols):\n", + " dist = DataStructs.cDataStructs.BulkTanimotoSimilarity(fps[i], fps[:i], returnDistance=True)\n", + " dists.extend([x for x in dist])\n", + "\n", + "cutoff = 0.2\n", + "\n", + "# now cluster the data\n", + "cluster_indices = Butina.ClusterData(dists, n_mols, cutoff, isDistData=True)\n", + "cluster_mols = [operator.itemgetter(*cluster)(mols) for cluster in cluster_indices]\n", + "\n", + "# Make single mol cluster a list\n", + "cluster_mols = [[c] if isinstance(c, Chem.rdchem.Mol) else c for c in cluster_mols]\n", + "\n", + "### Pick diverse compounds\n", + "\n", + "# Get some mols\n", + "data = dm.data.freesolv()\n", + "smiles = data[\"smiles\"].iloc[:].tolist()\n", + "mols = [Chem.MolFromSmiles(s) for s in smiles]\n", + "\n", + "# Calculate fingerprints\n", + "fps = [Chem.RDKFingerprint(x) for x in mols]\n", + "\n", + "\n", + "def distij(i, j, features=fps):\n", + " return 1.0 - DataStructs.cDataStructs.TanimotoSimilarity(fps[i], fps[j])\n", + "\n", + "\n", + "npick = 10\n", + "seed = 0\n", + "\n", + "picker = MaxMinPicker()\n", + "initial_picks = []\n", + "picked_inds = picker.LazyPick(distij, len(mols), npick, firstPicks=initial_picks, seed=seed)\n", + "picked_inds = np.array(picked_inds)\n", + "picked_mols = [mols[x] for x in picked_inds]\n", + "\n", + "picked_inds, picked_mols" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0a5e407f-5d0a-436d-bc09-c37ee4ad853b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dm.to_image(picked_mols, mol_size=(150, 100))" + ] + }, + { + "cell_type": "markdown", + "id": "fa727157", + "metadata": {}, + "source": [ + "## Datamol Example\n", + "\n", + "**Note:** Datamol abstracts away the explicit steps 2 (calculating fingerprints) and 3 (generating a distance matrix) of the tutorial" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b87a7afe", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import datamol as dm\n", + "\n", + "# Load example dataset\n", + "data = dm.data.freesolv()\n", + "smiles = data[\"smiles\"].iloc[:].tolist()\n", + "mols = [dm.to_mol(s) for s in smiles]\n", + "\n", + "# Cluster the mols\n", + "clusters, mol_clusters = dm.cluster_mols(mols, cutoff=0.7)\n", + "\n", + "# Cluster #1\n", + "dm.to_image(mol_clusters[0], mol_size=(150, 100), n_cols=6, max_mols=18)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "71b09c2e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Cluster #2\n", + "dm.to_image(mol_clusters[1], mol_size=(150, 100), n_cols=6, max_mols=18)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "8579620c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Pick a diverse set of molecules\n", + "indices, picks = dm.pick_diverse(mols, npick=10)\n", + "dm.to_image(picks, mol_size=(150, 100))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "50c4fb2c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", 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+ "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Pick centroids from a set of molecules\n", + "indices, centroids = dm.pick_centroids(mols, npick=18, threshold=0.7, method=\"sphere\", n_jobs=-1)\n", + "dm.to_image(centroids, mol_size=(150, 100), n_cols=6)" + ] + }, + { + "cell_type": "markdown", + "id": "a233a779-6ce1-4e8c-ade5-86eaa627f039", + "metadata": { + "tags": [] + }, + "source": [ + "**Note**: Datamol provides one method (Butina using Tanimoto/ECFP for distances computations) for clustering molecules. In practice, an infinite number of methods exists and the user should build them as needed. Please feel free to contribute to Datamol if you wish to add any specific methods that are useful! \n", + "\n", + "## Understanding key parameters\n", + "\n", + "- Determining an appropriate threshold for cutoff\n", + " - Butina uses distances (which is 1 - distance) and the cutoff is dependent on the distance metric used. As mentioned earlier, Datamol uses Tanimoto with ECFP fingerprint. Therefore the distance cutoff is 1 - Tanimoto.\n", + " - Generally speaking, if you have a very small distance cutoff, compounds must be extremely similar (i.e. high Tanimoto score) in order to be grouped into one cluster. Therefore, with a small distance cutoff, you’ll get more clusters with fewer compounds per cluster. Vice versa is true.\n", + "\n", + "**Note:** This is an extremely general overview, in reality, the output greatly depends on both the size and diversity of the dataset being used. There is no “default” cutoff that is set in Datamol and instead each user should set cutoffs according to their specific dataset and use case. \n", + "\n", + "You can also see a more detailed definition of the methods, arguments and their returns, [here](https://github.com/datamol-org/datamol/blob/main/datamol/cluster.py#L173). \n", + "\n", + "## References\n", + "\n", + "- Macs in Chemistry - [https://www.macinchem.org/reviews/clustering/clustering.php](https://www.macinchem.org/reviews/clustering/clustering.php)\n", + "- TeachOpenCADD - [https://projects.volkamerlab.org/teachopencadd/talktorials/T005_compound_clustering.html#Picking-diverse-compounds](https://projects.volkamerlab.org/teachopencadd/talktorials/T005_compound_clustering.html#Picking-diverse-compounds)\n", + "- [https://jcheminf.biomedcentral.com/articles/10.1186/s13321-020-00445-4](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-020-00445-4)\n", + "- [https://towardsdatascience.com/a-practical-introduction-to-the-use-of-molecular-fingerprints-in-drug-discovery-7f15021be2b1](https://towardsdatascience.com/a-practical-introduction-to-the-use-of-molecular-fingerprints-in-drug-discovery-7f15021be2b1)" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python [conda env:datamol]", + "language": "python", + "name": "conda-env-datamol-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/tutorials/Filesystem.ipynb b/docs/tutorials/Filesystem.ipynb index abe9f259..2eb89f01 100644 --- a/docs/tutorials/Filesystem.ipynb +++ b/docs/tutorials/Filesystem.ipynb @@ -40,7 +40,7 @@ }, { "cell_type": "code", - 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" 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" 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" + } + }, + "cell_type": "markdown", + "id": "7f18a74d", + "metadata": {}, + "source": [ + "# Fragmenting Compounds\n", + "\n", + "\n", + "## Background\n", + "\n", + "A [fragment](https://www.frontiersin.org/articles/10.3389/fmolb.2020.00180/full#:~:text=Fragment%2Dbased%20drug%20discovery%20(FBDD,in%20target%2Dbased%20drug%20discovery.) is essentially a small molecule that has a low molecular weight and small size, often representing a small part of a bigger, drug-like compound. These small compounds are of great importance in drug discovery, especially in the early stages. Fragments are used to identify small chemical and functional groups that bind, even if weakly, to the target of interest and thus serve as useful starting points in a [medicinal chemistry](https://en.wikipedia.org/wiki/Medicinal_chemistry) campaign. Once identified, fragments act as building blocks and are subsequently chemically modified (addition/removal of specific chemical groups) to improve the overall interaction of the newly generated compounds with the target of interest. \n", + "\n", + "Generally speaking, fragments will follow the [rule of three](https://www.sciencedirect.com/science/article/abs/pii/S1359644603028319?via%3Dihub). However, this is not always the case. Different researchers will have their own definitions of what is deemed a fragment:\n", + "\n", + "1. **Molecular weight less than 300 Da**\n", + "2. **ClogP value less than 3**\n", + " 1. ClogP is a well established measure of a compound’s hydrophilicity which is important for absorption, permeation, and other drug-related physical properties.\n", + "3. **Less than 3 hydrogen donor and acceptor groups.**\n", + "\n", + "Existing molecules can also be split into smaller fragments, a good visual is shown below of the fragmentation process: \n", + "\n", + "![16717480-e193-4f3b-b39a-650801293024.png](attachment:16717480-e193-4f3b-b39a-650801293024.png)\n", + "\n", + "[***Source***](https://www.frontiersin.org/articles/10.3389/fchem.2018.00229/full)\n", + "\n", + "## Fragment-Based Approaches for Compound Optimization\n", + "\n", + "Standard fragment-based approaches for compound optimization are: \n", + "\n", + "1. **Fragment growing** - adding chemical groups to the fragment to improve properties.\n", + "2. **Fragment merging/scaffold hopping** - combining fragments that have an overlapped binding site.\n", + "3. **Fragment linking** - linking two or more fragments together to drastically improve [binding affinities](https://www.malvernpanalytical.com/en/products/measurement-type/binding-affinity#:~:text=What%20is%20Binding%20Affinity%3F,(e.g.%20drug%20or%20inhibitor).).\n", + "\n", + "You can read more about these methods in detail [here](https://www.frontiersin.org/articles/10.3389/fmolb.2020.00180/full). \n", + "\n", + "## Fragment Generation\n", + "\n", + "![image.png](attachment:ddd64e95-9256-46e4-9c25-2560deadf01c.png)\n", + "\n", + "***[Source](https://www.researchgate.net/figure/A-schematic-overview-of-a-molecular-fragmentation-process-For-a-single-step_fig2_353714355)***\n", + "\n", + "Fragments are essentially generated by breaking specified bonds in a larger molecule. There are multiple ways approaches that one can take to fragment a molecule. The methods covered below include; RECAP, BRICS, FraggleSim and AnyBreak. \n", + "\n", + "1. **RECAP - R**etrosynthetic **C**ombinatorial **A**nalysis **P**rocedure \n", + " 1. Alkyl groups smaller than five carbons and cyclic bonds are left intact while compounds are dissected based on 11 pre-specified bond types\n", + "2. **BRICS -** **B**reaking **R**etrosynthetically **I**nteresting **C**hemical **S**ubstructures\n", + " 1. In BRICS, compounds are dissected based on 16 bond types while considering the chemical environment and surrounding substructures. \n", + " 2. Both RECAP and BRICS are examples of systematic fragmentation\n", + "3. **FraggleSim**\n", + " 1. RDKit uses the Fraggle similarity algorithm developed by Jameed Hussain and Gavin Harper of GSK. Read more about the details of the algorithm [here](https://raw.github.com/rdkit/UGM_2013/master/Presentations/Hussain.Fraggle.pdf) and [here](https://www.rdkit.org/docs/source/rdkit.Chem.Fraggle.FraggleSim.html).\n", + "4. **AnyBreak**\n", + " 1. This method uses BRICS first and fallback to generating all possible fragmentation if it doesn't work. \n", + "\n", + "**Note:** It’s challenging to point to one method and refer to it as the status quo. The method you should use depends on what exactly you are trying to do with the fragments, the types of molecules you’re working with etc. Generally speaking, it is ideal to fragment a molecule in a way that is synthesizable in the lab, and each of the methods listed above have slight variations in their approach. There’s no point fragmenting a molecule at certain bonds if these bonds have never been broken before in a lab setting, it’s not realistic. \n", + "\n", + "Once molecules are fragmented, the next step is typically a matched molecular pair analysis (MMPA). This analysis compares the chemical structure of two molecules that only differ by a **single chemical transformation** (i.e. changing one functional group). MMP’s are useful to analyze a large collection of compounds because the minimal structural differences make it much easier to interpret any observable changes in physical or biological properties. We will not cover MMPA’s in this tutorial. See below for a visualization of a matched molecular pair: \n", + "\n", + "![image.png](attachment:053aeef2-86e6-4191-bd4a-f82359a8efc4.png)\n", + "\n", + "[Source](https://en.wikipedia.org/wiki/Matched_molecular_pair_analysis#:~:text=Matched%20molecular%20pair%20analysis%20(MMPA,matched%20molecular%20pairs%20(MMP).)\n", + "\n", + "**Note:** Sometimes the term fragment is used synonymously with scaffolds. However, scaffolds are better defined as key core structures of a compound, often critical and essential for binding, whereas a fragment may only partially match with a “core structure”. \n", + "\n", + "## Tutorial\n", + "\n", + "Now let’s walkthrough how you could do this in RDKit and then compare it with Datamol. Starting from a cluster of molecules, this tutorial will cover the following:\n", + "\n", + "1. Generate list of all fragments in different ways as described above\n", + " 1. RECAP, BRICS, FraggleSim\n", + "2. Show how to return the results as a hierarchy of nodes instead of just a visualized set of fragments allowing for more flexibility in the manipulation of the results\n", + "3. Fragment molecules on specific bonds suitable for an MMP analysis\n", + "4. Briefly exploring other manipulations\n", + " 1. Assembling - assemble fragments to create new molecules. Limit the number of fragments you’d work with because it’s computationally intensive.\n", + " 2. Decomposition - break a molecule down to get non-overlapping fragments and how they are linked.\n", + "\n", + "## RDKit Example" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "af6f446c-052b-4d77-8ad4-89af2500f2fc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from rdkit import Chem\n", + "from rdkit.Chem.Draw import IPythonConsole, MolsToGridImage\n", + "from rdkit.Chem import BRICS\n", + "from rdkit.Chem import Recap\n", + "from rdkit.Chem import rdMMPA\n", + "\n", + "from rdkit.Chem.Fraggle import FraggleSim\n", + "\n", + "smiles = \"CCCOCc1cc(c2ncccc2)ccc1\"\n", + "mol = Chem.MolFromSmiles(smiles)\n", + "\n", + "mol" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "a393217f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Decompose compounds based on BRICS algorithm.\n", + "# Calling dm.fragment.brics runs this algorithm, as well as fixes/sanitizes fragments in one line of code.\n", + "\n", + "brics_frags = BRICS.BRICSDecompose(mol, returnMols=True, singlePass=True)\n", + "brics_frags = list(brics_frags)\n", + "MolsToGridImage(brics_frags)\n", + "\n", + "# Recap, FraggleSim and rdMMPA can be run in a similar manner as the BRICS algorithm above." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "fe058c37", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import datamol as dm\n", + "\n", + "smiles = \"CCCOCc1cc(c2ncccc2)ccc1\"\n", + "mol = dm.to_mol(smiles)\n", + "mol" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "7c2059f8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# BRICS\n", + "with dm.without_rdkit_log():\n", + " frags = dm.fragment.brics(mol)\n", + "\n", + "dm.to_image(frags, mol_size=(250, 150))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "99027b1a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# FraggleSims\n", + "with dm.without_rdkit_log():\n", + " frags = dm.fragment.frag(mol)\n", + "\n", + "dm.to_image(frags, mol_size=(250, 150))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "89850686", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Recap\n", + "with dm.without_rdkit_log():\n", + " frags = dm.fragment.recap(mol)\n", + "\n", + "dm.to_image(frags, mol_size=(250, 150))" + ] + }, + { + "cell_type": "markdown", + "id": "54bceb67", + "metadata": {}, + "source": [ + "What you can also do is assemble some new molecules based off a list of fragments. This is how fragments are used as building blocks for larger, more optimized molecules. By having an understanding of the properties of the underlying fragments, you can essentially run a “mix and match” process to generate optimal molecules. \n", + "\n", + "Assembling molecules from fragments is computationally expensive. Make sure you use the parameters: \n", + "\n", + "- ***frags***\n", + "- **max_n_mols**\n", + "\n", + "To limit the number of fragments to work with and the number of molecules to be assembled. " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "e9551e69", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + 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"\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Assembling new molecules based on a list of fragments\n", + "# Get the fragment set of a molecule\n", + "smiles = \"CCCOCc1cc(c2ncccc2)ccc1\"\n", + "mol = dm.to_mol(smiles)\n", + "\n", + "with dm.without_rdkit_log():\n", + " frags = dm.fragment.brics(mol)\n", + "\n", + "# Limit the number of fragments to work with because assembling is computationally intensive.\n", + "frags = frags[:3]\n", + "\n", + "# Assemble 8 molecules from the list of fragments\n", + "with dm.without_rdkit_log():\n", + " mols = list(dm.fragment.assemble_fragment_order(frags, max_n_mols=8))\n", + "\n", + "dm.to_image(mols, mol_size=(250, 150))" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "93f77ef0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(['CCC', 'O', 'C', 'c1ccncc1', 'c1ccccc1'],\n", + " {'C',\n", + " 'CCC',\n", + " 'CCCOCc1cccc(-c2ccccn2)c1',\n", + " 'Cc1cccc(-c2ccccn2)c1',\n", + " 'O',\n", + " 'OCc1cccc(-c2ccccn2)c1',\n", + " 'c1ccc(-c2ccccn2)cc1',\n", + " 'c1ccccc1',\n", + " 'c1ccncc1'},\n", + " )" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Decomposition\n", + "# It's also possible to break a molecule based on a set of chemical transformation and gets the non-overlapping fragments and how they are linked\n", + "\n", + "with dm.without_rdkit_log():\n", + " results = dm.fragment.break_mol(mol, randomize=False, mode=\"brics\", returnTree=True)\n", + "\n", + "results" + ] + }, + { + "cell_type": "markdown", + "id": "4a2dae4e", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "- [https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.6b00596](https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.6b00596)\n", + "- RDKit Cook Book - Creating fragments - [https://www.rdkit.org/docs/Cookbook.html#create-fragments](https://www.rdkit.org/docs/Cookbook.html#create-fragments)" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python [conda env:datamol]", + "language": "python", + "name": "conda-env-datamol-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/tutorials/Fragment_and_Scaffold.ipynb b/docs/tutorials/Fragment_and_Scaffold.ipynb deleted file mode 100644 index 2e040070..00000000 --- a/docs/tutorials/Fragment_and_Scaffold.ipynb +++ /dev/null @@ -1,355 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import datamol as dm\n", - "dm.disable_rdkit_log()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fragmentation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The fragmentation methods implemented in `datamol` will return the fragment set coverage of a molecule, as opposed to a break down of the molecules into non-overlapping blocks.\n", - "\n", - "In the following, let's fragment a molecule using multiple methods." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ], - "image/png": 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\n" - }, - "metadata": {}, - "execution_count": 3 - } - ], - "source": [ - "smiles = \"CCCOCc1cc(c2ncccc2)ccc1\"\n", - "mol = dm.to_mol(smiles)\n", - "mol" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### BRICS" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 4 - } - ], - "source": [ - "frags = dm.fragment.brics(mol)\n", - "dm.viz.to_image(frags, n_cols=6)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### FraggleSim" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 5 - } - ], - "source": [ - "frags = dm.fragment.frag(mol)\n", - "dm.viz.to_image(frags, n_cols=6)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Recap" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 6 - } - ], - "source": [ - "frags = dm.fragment.recap(mol)\n", - "dm.viz.to_image(frags, n_cols=6)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Any break\n", - "\n", - "This method uses BRICS first and fallback to generating all possible fragmentation if it doesn't work." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 7 - } - ], - "source": [ - "frags = dm.fragment.anybreak(mol)\n", - "dm.viz.to_image(frags, n_cols=6)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Scaffold\n", - "\n", - "Get the scaffolds and attachment points from a list of molecules to allow creating molecular series." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "['c1cc2c([*:4])c([*:3])c([*:2])cc2cc1[*:5]',\n", - " 'C(=C1c2ccccc2CCc2ccccc21)[*:1]',\n", - " 'c1c([*:7])c2c(c([*:8])c1[*:9])Cc1c(c([*:1])c([*:2])c([*:3])c1[*:4])C2',\n", - " 'CCc1cc([*:5])cc2cc([*:2])c([*:3])c([*:4])c12']" - ] - }, - "metadata": {}, - "execution_count": 8 - } - ], - "source": [ - "# Get some mols\n", - "data = dm.data.freesolv()\n", - "smiles = data[\"smiles\"].iloc[:].tolist()\n", - "mols = [dm.to_mol(s) for s in smiles]\n", - "\n", - "scaffolds, scf2infos, scf2groups = dm.scaffold.fuzzy_scaffolding(mols)\n", - "list(scaffolds)[:4]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "image/png": 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Nt2zZ0qdPnxefksAVF1N4ONnaUsOGNGECnT1LderQoEFUVEREpK5Oa9bQqVOUkUFDh9Ljx4r6EqBGWrdunYaGRlBQ0PXr14lo8eLFYrE4MjJy3759fIcG1UiTJk169uxZUFDA+i/zwtWVfvuNXr3ia3zg008//VSvXr2///77+PHjihvl8uXLERER2traCxYsIKL4+PidO3eqqKh4e3ujgxTI0tPTMzY2VkQrgRpJQP9NMTExmzdvVlFR2bhxozJfNVq0aDFjxgzM6H8W+vTp88svv7Rp0yYqKqpbt24V2ZX377/ps2dTo0Y0fDiFhlJxMVlZUXAwJSXRzp00ejS1aEFE9M039PvvtHs3nThB3bvTxYsK/1qgxjAxMWEnn9lrSN26db29vYmItYSuxAVv3fr/Q6337lFyMr18+fZhWho9fy6fsEH5WI84HmcKatem5ctp3jy+xgc+6erqzp8/n4i8vb0VVEuvpKRk+/btROTq6sqqxSxevLioqMjOzq5jx46KGBE+X0+fPo2MjERfsYritcapUg0ePJiInJ2dlT90ZmZmw4YNiSgkJET5o8OnSktL+/bbb4lIQ0Nj9+7d7/2cxMREPz+/Tp06NW8+jIgj4kxNOU9P7unT8q6cnc2NGMERcWpq3ObNiogdaqbs7GwjIyMiCg4O5jiutLSU9dj19PSsxNV0dTnpvzM15bZu5aTdCg4c4Nzd5REx8CEvL69169Zff/31w4cPlTlucTH37Bnn4sKdP89xHGdjw7Vrx124wH3/Pff4scJHLykpyc7O3rJlS15eHnvm999/t7GxcXBwuHXrlsKHBxklJSXffvtts2bNAgMjFXH9TZs2EdHo0aMLCws5jrt48aJYLG7VqlVycrIihoPPGis4dOTIEb4D+Tyo8ZiLVlxJSUkVD0VEREScPHlSR0eHnSFUMjbutGnT5s6d+/XXX1fl0LOqqqqurq4cY4N31atX7/Tp066urv7+/g4ODnfu3Fm1ahVbVc7JyTl48GBwcPD58+dZ7ZmGDZMWLcoaPVqnU6ePX1lbmw4fJi8v8vKimTMpOpr8/Unt8/gtBD5pa2uvWLFi0qRJ7u7uI0eO1NbW9vb2tra2Tk1NffPmTZlPFolUOe6DrxJaWvTll3ThAj16RK1aKThuUC5NTc0RI0b4+vru2LFj9erVyhk0LY3GjaO4OLKyevuMnx+ZmtKOHbR7Nx0+TMuXk4uLAlvVZ2VlBQQE3Lt3r1GjRsOHD8/JyUlNTQ0JCXn8+LGrq+uJEycUNTC8Q1VV1dt703ff6c6d23jo0P+vOisXb9688fLyIiJHR8datWoVFxevWrWKiObOndugQQN5jgQgQHxnpB/36NGjrfI4pS4SiVauXMnXV1FSUtKiRYs6depU8ato2bLlnj17+PoqhCYgIEBNTY2IxowZExER4eTkpK2tzb4RGhoa1tbWISEhlWvFu3cvp6HBEXEDB3IZGfKMOSoqysXFhc0+pKamyvPSwCuJRNKtWzciWrRo0d69e9u1a/ehVwkTE2u2av3eP0uXcmZm3PXr3IABHPffCmHfvty8edy8edzw4Vgh/LxduXKFiBo2bFhcXKyE4W7e5L78kiPiGjXiTp/msrLePh8by714wTk5vf2p69mTu39fUTEkJyd7enoGBwc/efIkPDz83r177PmsrKx+/fopalT4MBsbjohzdJTzZX/88Uci6t+/P3u4YcOG+vXrT5w4sXLvwlDjYYXwk3wGaxOurq4nT57U0tKqXbt2pS+Sl5dXWFiotJap7youLs7Pzy8oKKhbt65aFZaEkpKS7O3t1dXVbW1t5RgevJeTk1OTJk3Gjx9/8ODB0NBQIhKJRN98842jo+OYMWMqsVS7YQMNHkxt2tB331GTJmRjQ6dPU69eFBbGNWtWpXOtjx492r17d3BwcHx8PBGpqamVlJQcPXo0LCzM1NS0KleGakIkEvn5+VlYWAQFBSUkJGhra+vr67/3M3V06nzgb4iI2AaFrl3JxISkHQq6dqUJE4iIIiIoOVnOkYMy9ezZs23btg8ePDh9+vTQoUMVOta+fTRlCuXl0ddfU2goicX//1fs7HRAAI0cSc7OFBlJZmY0dy4tW0a1asln9NLS0itXruzevfvUqVOjRo36/vvviaigoKBevXrsE3755ZexY8fKZzD4FGvX0okTFBxMP/xAMk0Bq+T+/fuBgYHSbl6pqaleXl4ZGRm2trbq6uryGQNAyPjOSD/i2LFjRKSjo/Pq1auqXCcqKkpFRaVWrVqPHj2SV2yfZNmyZURkZmZWUlJSlets27aNiJo0aZKbmyuv2KB8hw8fJiJdXV1vb++n5Z8R/Mh1OCJOT487ffrtMwkJXOfOXL16qWZm3c6zwzefKD09PSgoyNLSUlonqXHjxm5ubpcuXWKrSXXr1g0LC6t0zFDdhIaGtmjRgoh++eWXSl/EzIzjOC4tjevaFWcIayC2WXT06NGKG6KkhHNz40QijohzcuIKC8v75MxMzsWFU1HhiLiOHbmoqKqOfv/+fU9Pz/bt24vFYrFY3LRp05kzZ5b5nG3btrm4uJSWllZ1MKgUDw+OiDM35+T1HbCysiIiFxcX9tDJyYmIBrB9DgDvgxXCT1KtE8KioqLWrVsT0fr166t+tQkTJhDRqFGjqn6pT5WYmMg2i1bupl9WaWmpubk5ES1btkweocHH7dmzh4hsbGyqeJ38fM7eniPiVFU5H5+3T2ZmcpMnuxNR7dq1f//99wpeqqSkJCIiwsHBQXoeVUdHx8HBISIiQiKRsM8pKChgU+aqqqo+0vHgM8fOzJiamlZllxRLCDmO27EDCWENlJSUpK6uXqtWrZSUFEVcPy2Ns7J6Wxyr4i8tZ89yzZpxRJyhYaqnp3dBQcGnjpuZmRkcHDx8+HDxf7755pvNmzfL7o2PiIi4ffv2jBkzJk6ceOvWrdu3b0tfEkGZsrM5IyOOiNu1Sw5XO3ToEBHVq1fv9evXHMfdunVLVVVVTU3t7t27crg61FBICD9Jtd4yumnTpocPH7K2DdIn582b924RhQ8xMzObPXs2+3j16tWHDx8+fPjwmTNnrKSH35XCzc0tNzfX1ta2b9++7JmcnBwXF5eKX2H48OEjR44kItY2o3fv3qtWrZowYULTpk0VETDIunv3LhF16NChitfR0KDgYOrQgRYuJHd3evKENm8mHR0KDFxuYMCtXr160qRJ//7774YNG8ppm3Pv3r3g4ODff/89JSWFiFRUVCwtLR0cHEaPHl3mhGrt2rV37drVvn17Dw8Pd3f3uLi4LVu2KG5rzYULFzp16qSnp0dE2dnZrBYcEc2YMYM9CVWXnJzMEsL169dX5Vv5119vP5g0iTQ06J9/aOXKt88MH06DB1c1TuCXoaHhwIEDjx07tnfvXuk7oLxER0fPmmXy11/aDRvSgQOfsCGwf3+6c4c8POjGDVcvrz2hoft27NjRvXv3j/5DiUQSERFx4cKFffv2sWYGurq6NjY248aNe7fTwBdffFFYWCgWi4mI7TBq37492tMpn7Y2rVpFEybQP/+Qg0OVLlVUVOTm5kZE3t7eX3zxBRG5urqWlpbOmTOnnKPUwK+srKw3b94YGxuzh5mZmf/880/jxo3btGnDa1wKFx8fX69ePR0dHekzhYWF169fb9eunXQ3ezXFd0b6QSkpKew+8sSJE7LPN27cuOJf3dChQ2X/7cqVK4nI1NRUOaftmcjISJFIpKGhIbvbkN3NV9zixYtlr8kOENrb2yvtqxCyYcOGEdGBAwc4jissLKz6D88ff7ytKOPktO/Nmzfsye3bt9eqVYuIBg8enJmZWeafJCQk+Pn5mZmZSX8kTE1NfXx8ytlKnfFfsZoDBw5oaWkRUa9evRS0YnD48GFnZ+d169axH/L79+9PnDjx9u3bt2/fVubvWo03ceJEIho5cqS8LpiUxGlpcSIRd/GivC4J1cLBgweJqEOHDvK9bEhISJ06dYyNB/brV5yQUMmLXLlypW3btkSkoqLi5OSUnZ39oc98/vy5j49Ps2bNiGjQoEGNGjUaO3ZsSEiItL0EVGcSCRcWxi1b9nbX6KNHXOWOL5S5cwsJCSGievXqpaWlyTVekJv79+8HBAR4e3ufOXOG47j4+HgrK6utW7fa2dmxzknKofwVwlOnTi1dunTHjh0PHjyQPrlixQoLC4uIiAilhVE5Iq66tkp3dnYODAwcMmTI8ePHZZ8PCQnJycmp4EWaNm1qaWkpfVhUVNS+ffvY2Fh/f39WrkrROI7r0aPH9evXFy1axHpJMwUFBXv37q34dTp37ty5c2fpw4SEhDZt2uTn5//111+95HVkGz6gWbNm8fHxMTExrVu3/uOPPyZOnDhjxoz169dX5ZrXrpGnZ1hExKiWLVuGh4e3bNmSiP7++28bG5uUlJQOHTqEhYUZGxvn5+cfO3Zs165dJ0+eLC0tJSKxWGxrazthwoSvvvqqnOtv2LBh3bp1R48eZRuMb926NXz48ISEhObNm4eHh7O7MTlKTEz8448/JBKJm5vbxYsXOY67evXqpEmTGjRogLl5ebl582bXrl3ZLin2AyMXy5aRpyd16kRRUaSqKq+rAs+Ki4uNjIxev369cOFCKyurdu3aGRgYVOWCHMf5+vouXLiQ4zg7O7vt27dXpX9SQUGBj4/PqlWrioqKTExMAgMD+/fvL/3b3Nzc0NDQ33777dKlS+wWpUWLFs7Ozvb29mLZwjVQ7V27RpaWtGYNTZtGERF0/Dj5+X3aFZKTk1u1apWVlXX69GkrK6uCgoK2bdvGx8dv27bN2dlZMVGDHKxfvz4tLW3p0qUlJSVqampZWVlffPHFgwcPVqxYsXv3buXEMGLEiLCwsCNHjowYMUI5IxYVFXl5eRkYGLi6uubk5Ghraz948MDHx6dFixY9e/aUzUeqIz6z0Q/7999/VVVV1dXVY2Ji5HtlNm8q3YmuaL///jsRNWrUqJxJ0MpZtGgREX311Vc4NK9QWVlZbIGXVQNauHAhVbYbeBkvXrzo0qULEenr6589e5Y9GRcXx7K1+vXrDxo0SHrLpaWlZW9vf+rUqYoUJSopKenTpw8RaWtrS+fGXrx4wVqZ161b99ixY1WPn+O40tLSa9euzZ8/v23btk+ePMnIyJBIJL6+vlFRUc7OzosXLx48eHBiYqJcxhI4iUTSu3dvInJzc5PvlfPyOGNjjogLDJTvhYFPeXl5TZo0kd1Qo6enZ25u7uDg4OPjExYW9vjx44q/d2RmZrI7KjU1NTkeSL59+zabsRKJRE5OTpmZmVFRUU5OTnXr1mUxa2ho2Nrayh6Nhs/L1aucoyPXpQuXlMSdOcPNns0dPswFB7//z/79r4PfsX379hEjRvTo0YMtubD6fO3atcPek+pMIpFcu3bt7t272dnZ8+fPl+6PO3TokJeXl3JiOHfunIGBgb6+fp8+fZ49e6aEEXNzc728vE6dOnX16lWO42xtbUtKSkaOHBkfH+/t7V39VwiraULIbmfnzp2riIuXqVWlONnZ2UZGRkSkiCXy3NxcdoCw4pVIoBJYU6/OnTuzh9bW1vTf9tGqy8nJGTVqFLvNkhaNzMrKGjRoUJ06dUQikYqKioWFRUBAQJa0vVfFFBYWsu2FIpFImr7m5+fb2dmRPMrMxMbGrlq1qkuXLqy0g5GR0Xt3ZezcubMqxTBBav/+vN69pxgZGb27nbjq9u3jiLgGDbj0dLlfG3ggkUhYuwUjI6Pvv/++e/fu7+2RU7du3a5duzo6Ovr4+Bw5ciQ2Nva9800xMTHs2M8XX3whnbqSl6KiomXLlrHd8ux4GHvV6t2792+//Sb3iVRQsqtXuR9+4MLDOXv7twkh61r53j+mplfLWb0YO3bsokWL5FWfD5QvLi7Oysrq9evXSUlJCi34HxsbyzaLEhHrV6etrb1y5cpKFLKqIIlEEhoa2rlzZ7FYLHuYy9/f38PDIz4+ft68eXv37q3m6zfVMSHcv38/ERkYGKQr5vbk3r17ampqampq0dHRiri+lLu7OxH16NFDQbObwcHBRGRoaKiIe0RgAgMDicjxvw677IS0HBeuS0tL2aojEYWEhLAnz507R0TNmzev4rSWn58fq08zfvx4dupGIpH4+PiwbZxOTk6fWqkyIyODVfkzMjJiqaC5ufny5cufPHki/ZwbN27cuHHj5cuXHMd5eHgo88BATZWbyzVuzOr1Keo3vU8fjoj76ScFXR6Uavny5Szfk63B+ObNm0uXLgUEBLi4uFhaWpqYmLx7z62urm5iYmJtbe3m5hYUFBQVFRUaGsqSyU6dOsn+msvX3bt369evLxaLDQwM3Nzc+OoOBXLHEkKO40aM4JYs4WbP5lxdOXv79/9xcYmz/4DBgweLRKLatWu3bdtWof1UQEHYiZj4+HiO42bMmNGkSZP58+fL/RRoTk6Op6cnSwLr1Knj6ekZGxvr8F9RoyZNmgQFBcl3RI7jbt++LS19PHDgwGvXrkn/ys/Pb/78+fPnz+/fv7+dnZ3iMlK5qHYJYX5+PrvnDggIUNwo7ABh//79FTfE48ePNTQ0RCKR7A+HfEkkEnaA0B114hWGFYP19fXl3tk+Kkf79u0bOHBg4X/NvDZv3kxEU6dOrfqVT5w4wapd9ezZMykpiT25f/9+VmbG0tJSWtWmHAUFBcePH2dVbdmrXuvWrefNm3ft2rUykx0SiWTRokXR0dETJ060s7NbuXIlNvZU3eLFHBH31Vdy6+j1rn//5VRVuVq1OHlv0gdlO3nypKqqqoqKSnh4ePmfmZ6eHhUVFRQU5ObmZm1tbWJi8m59Y1bazc7OTqF1XEpLSxs0aEBEspUYoAaQJoRPn3IGBtzs2ZW/FLutt7GxUdBSASjIzJkz9+3b16RJk++++87JyWnhwoXu7u6NGzcWi8Xt2rULCgqSyw2VRCIJCgpq2LAh22Lg4OAgW3Lvzz//lFYk7tev3+3bt6s+IsdxSUlJ8+fPb9SokVgs7tSpU3BwsOwa4KBBg6QfY8toZcirgXv50tLS2O4UxfXsZlsBJ0yYoKDrMzdu3FBRUalVqxamVBWkX79+RHTy5Enune2jisPOyvv5+cnlatHR0WySpXHjxjdu3GBPXr16lb10tmjRopybsKioKBcXFwMDAzY127hxY1blLzc3Vy6xwUc9f/62EOhffyl2oKlTOSLO2hqHtT5jDx48YAt6ZfaE+/r6btmy5c8//0xOTi7nn2dnZ//zzz9BQUHu7u4jRoxo1KiRurp6q1atFBHqgAEDxo8fz27abty4QURffvmlIgYCHt28yXl7v/1440ZuzZrKXyoxMVFbW5uIWOFK+IwUFhZm/IftaIuNjbWzs2Pzy7179/7zzz+rcv3r16/37NmT5Xtdu3a9cuXKu59TWloaFBTEJp5UVFQcHBzKfzEsX1FR0a+//tqqVSuxWNy0adNFixaVf64nPz+/Kq2DlaN6JYRybOD+UX5+fmxXniLWcNmWP21t7RcvXsj94mVMmDCBiEaNGqXogYSJleZLSEjg3tk+qjgWFhZEdO7cOXldMDU19ZtvvqH/LTOTmJjIKjrIVrVhHj9+7OXl1aJFC+lCgbm5+a5du2QbQINyjB3LEXF2dgofKCmJ69nzTZs237HpD/jsvHnzhpWfHTNmjOzSvUQikT1DWPHqMhkZGVpaWioqKnIvyZCamqqioqKpqckWHll3zR/YWhLUFNeucUZG8mlMz7C90KgoU2OcOXOmR48eLC10dHRkG0o/SWJiooODAzsF06hRo6CgIOlL38OHD+Pi4sp8/ps3b9zc3NihZT09PR8fH+nOrIo7depU9+7dWdhTpkypRNjVU/VKCO3t7YnI1tZWCWMVFxe3b99euhtQjkpKStja9MqVK+V75fdKSkpiewJPnz6thOEE5dWrV+xVg73EzJo1SxE/MGVIJBK2Tasq01fvKigocHR0pP8tM1Omqk1GRkZQUJClpaW0V4SRkZGLi8vNmzflGAlU3OXLnEjEaWpyynnHWbNmDRG1adOm+s9lQhklJSWDBw9mWxjKLOAXFRWtWbNmypQpPXr0YK8tZWhra3fp0sXBwWHVqlWHDx+WrQz83XffEZG3dJVHTvbs2UNE0i1V3377LckcooYaQCLhLCw4Is7DQ27XLCgoYNOUW7duldtFgVcFBQUbN25s0aKFWCxu1aqVl5dXBUtJ5eXl+fj4sIrEmpqabm5u0jW67OxsdpLwQ+fCYmJihgwZwl79WrVqVfG66w8ePBg0aJC+vr6pqWnVFzarm2qUEF65cuXdBu4KdfbsWSKqW7cuK4AhL+wAmImJSX5+vhwvW44ybVtBXiIiIoiod+/e7KHs9lHFefbsGREZGhoq4uLSMjOTJ09mE2OyVW3YtBm7QXR0dIyIiKjmRbFqttJSrksXjohTVplurrCwsHXr1kS0YcMGJQ0JcjJnzhz2uvH8+fPyP/Oj1WV+/vln6SefOXOGiJo1aybf0misBvK6des4jsvNza1du7aqqir6jNcku3ZxRJyhISffmncHDhwgJXYOA+Vgh/FYaVCxWBwQEFD+vQdr1Mxer6ytraX1rkpLSwMDA6X7QqdOnVrOAmBExP+xd+dxNWf/H8Df97aHiKgrlGTJjFGyDKHha5ddGGQZY/8KWbJ8TRgzsmeZoWyTLfsSDYoZVHZliUoKo7RIVFpv957fH8fc6ReZ1OfeW93X89Ef995u55x03ft5n+X9DmrevDlvpFu3bg8fPvxEj69evZoyZYqWlhYRmZiY7Nu3T6nn2tSivASEcrm8bdu2RLRkyRJV9tuvXz8imjBhglANpqWlmZiYENHx48eFavNf5eXl8Z1Cv/zyi8o61QS8+vzUqVP53cLbR5UnICCAlJnx6NixY3xjdufOnRULQWvXrjUyMtLV1S1dlQtQBh8fRsTq12eqPLB5+vRpviqO7cEVyJ49e4hIR0fn0qVLpfjx169fh4SE+Pj4uLm59ezZ89SpU4pvyWQyCwsLIipdy8XhBRIfPHjAGDt79iwRtW3bVsD2Qb2ysliDBoyI/fab8I13796diGbNmiV806BW169fb9euHY/Q2rZte+3atQ+fExYWxs+/8N0Qly9f/uiPt2vX7qM/XkR+fr6XlxffUa+jo+Pq6vr27dsiz5FKpd7e3vzCXltbe9KkSZX1w7G8BISKAu7v3r1TZb9PnjzR09MTi8U3b94UpEGelLJLly6CtFZyx48fx7SZ4L777jsi+vXXX9kH20eVx9PTU9mfdvfu3bOwsPjf//6neIS/frp37668TuFzPXnCBg5kBw+qut9evXrxeRC5XC4tRkFBAZNKi/1CGXEVun37toGBASktNfeSJUtI0ARpERERRGRmZsbfS93c3IosS0JFt3gxI2L29kpJjBwREcErhxWuqgKVg1wuP3z4MJ+EEolEzs7OihN6qamprq6ufI2uVq1aXl5eijW6Fy9eKE4S1qtXr/BJwpL4RMsXLlzgh8v4ND2fw6qsykVAqNQC7v9q7ty5RNShQ4eyX+g/evRIR0dHS0tLqJy2n6VHjx5E5OrqqvquKyu+an3lyhX2wfZR5Rk9ejQR7dixQ6m9vHr1qvALnmf3nT9/vlI7hSLy81m7dv/ctbNjR4+y5s0ZnxbbvZtt26aGUSnex3gl1Y8Si8XFlpcmYj4+LDWVjR7NevdmXbuylSsRIipJYmKiubk5Ec2YMUNJXcTFxYnF4ipVqghVOWUn9QAAIABJREFU8JbvvFBk5+LXW8KuQIIaPX/+PjFycLCyuuCJuLt166asDkCtsrKyPDw89PX1icjQ0HDJkiVr1qz56DpeVlaWp6cnTz9raGjo7u5ewiOIH7pz506nTp0Ua4/79+93dnbmdxs3bqwJJ5zLRUCo7ALun5aRkSGRSIjIz8+vjE3xmfVp06YJMrDP9fDhQz5tdv/+fbUMoJKRyWT8XYafbOEXMSr447Zs2ZKIrl+/ruyOCuNvfHsEzAcHJZCXx5o3/+eutTU7cIDZ2bF58xhjbNs2JlDlkc82c+ZMImrSpIlWMXR1dZmWVrFfO3awkSMZf0eVydiIEWpY6NQAOTk5fItUp06dSpErr+S++eYbAWepePIbPv+bmJgoEomqVKlSzks2Q8kNGcKImIuLErt4/fp1zZo1iajk6UCgwomLixsyZEjhici+fftGR0crnuDv78/XEvlJwrLnH5HL5X5+fvXr1ycinq6mevXqa9asUeq7a/mh/oDw7du31apVU2oB93+1fft2vhuwTRnww6nqPXszbdo0UkldBE3w5MkTvv2A3y28fVR5pFKpnp6eSCRS8Sk+GxsbIgoPD1dlp5CXxyws2MWL778aNmQHDrCVK5mjI7t/X50BYVpaGr/eCg0NLWUThWvKXb7MRo8WYlzw/0yYMIGILC0tU1JSlNqRr68vETk4OJS9qby8vKpVq4pEIp7Ljbfs5ORU9pahPAgOfqGnx6pUYUo+a/9+itba2hpTCZXbn3/+yTdzFi4bfufOnY4dO/JQsFWrVlcErdKblZXVt29fIrKxsUlKShKw5XJOu7gdQSojEolatWoVFhbWrFkzxYMnT5786quvPsx+piRff/21tra2SCS6detWWdoxMTExMTHhJe9V48SJE3Z2dopsS3Z2drq6unyJCcrowYMHRKTYPl7krpLExMTk5eVZWVnx2SnVyMvLi4mJ0dbWLvx/EFQjN5fCwt7flsne39i4kWbMoG+/VdegyNjYuG7dujVq1DA2Ni7NzxcUkFj8z10jI0pPF2pswK1bt27nzp1Vq1b19/fn+a6Ux9nZeebMmaGhoVFRUWV8lwgNDX337l3Lli35xhy+FZ+nCYGKTiaTzZjRTyKpPWvW7nr1zJXa14wZM3bu3Pnw4cNffvmFH0OFSumbb77R0tKSyWT8VBQRxcTEtGnTRi6Xm5mZ/fTTT+PGjRMX/rgpM0NDQycnp4CAgM6dO5uamgrYcjmn/oDQyMiIMZaZmblixYrVq1cT0erVq93d3QcNGsQTXajA3LlzCwoKevXqNWvWrFI3kp2dPWLEiKioqEOHDo0YMULA4RUnISHBxcVFLpdHRUU1aNAgJydnxYoV+fn5hWsQQ6nxzActWrTgdz08PMLCwhR3lYSHncrupYjIyMiCgoJmzZrxLfugSrVq0dy57297e7+/0bIl2drSwYM0cKB6RnXs2LGIiAgjI6ONGzd+9FUhFovXy+XF/vyIEVSlCqWkUJ06RERhYfTFF0obrCYKCgpasGCBSCTauXOnCt4uDAwMhg4dumPHDl9fX15EvtQKR4CMsYsXLxKR4lIPKrTt27ffvXu3fv36EyeWaiLpc2hra3t5eXXv3n3ZsmUjR440MzNTdo9QTjRu3HjUqFESiWTx4sW8CjcIQ91LlIwxFhYWJhaLdXV1+eZgFVdaP3XqFBEZGxuXfavnjh07iKhevXqqyZU6atQoInJ2duZ3PTw8iMjOzg614wQxfPhwIvL19VVlpzyhn4oT7u3du7fwCwlU5qNnCFeuZIyxjAxWt656towqqj8PGzasuA+Of08qc/gw696dnT3L9uxhdnZM0FqvGi4uLo7nQF+6dKnKOg0NDSWiunXrlrH6lr29veLD/e7du1RoWz5UaG/evOEr1UeOHFFZp7y8+KRJk1TWI6ger5Cs7L3BU6dOHThwYGJiImNs69atRDR58mSl9ljeqH+FkIjs7OzGjRu3a9euefPmnTp1ytTUdMGCBYsWLZo9e/a9e/e0tZU4yPz8/Hnz5hHR0qVL+UcsEUVGRvLVoRLq168fn0QfP368t7f3rVu31qxZs3TpUiWM9x/Xr18/cOCAvr4+X1aNj49fu3YtESkqj0OpRUZGHjp06Pz583yjgiq7Vs3G1CL4q13FnQIRaWtT4eWWNWuoRQuysSEiqlaNTpygBw/o2TP6e0u4iqxdu/bJkydffPGFu7t7+/btP/ocsUhEjBXbRPv29OWX1KwZXbpEVapQUBCpcCN95ZaZmdmvX7/U1NSBAwfy+SPV6NChg42NTWRk5Pnz5/lVeCm8fv06PDxcX1+fZ/PjVe+xPFg5LFu27NWrVx07diySCESpNm7cePHixR07dkycOLF169Yq6xcqn8DAwNjY2DVr1qh7IGpTLgJCIlq5cuWxY8f8/f3PnTvXq1evOXPm7N69+9GjRz4+PjxRipJ4eXk9fvzYxsZm6tSpigePHTv2WR+08fHxPPG3WCz28vLq2LHj6tWrx48fr0h/JDjG2MyZMxljc+fO5QcI582bl5WVNWLECEXJTvhcycnJfn5+e/fuDVMc6iKaOnWqtra2i4uLasZQZJ+qaqhlnyoQkVhM/fv/c7fIBtHr12nmTBoyhI4eVd2QkpKS+BzThg0bWrVq1apVq9K31aIF4UUlNL79xNLScu/evSqe+xs7duyCBQt2795d6oDwwoULcrm8U6dOvHAiDhCqRXR0dNOmTRV3U1NTdXV1jYyMbt++/ebNm+J+SiaTSaXS4r5rZWX1yy+/8EsgXg5ONaytradPn75+/fpZs2YFBweXuuu8rIy0xGdEZGQiqVJDuSdyAcopNa9QFrJq1SoisrGxyc/PZ4wdO3aMlFxpPTk5mR+3O3v2bOHHjx07NvRz8LIECvwA4YgRI5Q0bMbY7t27icjc3JxXXLl69apIJDIwMFBU8ISSy83N9ff3d3Z21tHR4f8patSo4eLicv78eXd3d/6Iq6urCjbiZmVl8b3TKs5xzJMsP378WJWdwr+Kj2dVqzIiFhiouk753MeQIUNU1yV8Dj8/PyKys7Mr/CD/0FS2xMREbW1tXV3dUh+v4GlRV69ezRjLyckxMDAQi8XKTpEKCjk5OUeOHFm+fDmv1pCfnz9nzpwOHTr4+Pgwxvr37y8pnuUnNyrwZV61bLFLT0/nBwjLUiku+mbgLveBlw9u2L1gcPgF1MgpX1SzZbRRo0ZEFBMTw7BlVO1mzZq1c+fOyMjIbdu2zZgxY/DgwT169AgMDFy+fPnGjRuV0eOiRYvS09P79evH6wcqDB48ePDgwaVuds2aNadPnz548ODUqVOVsV737t27xYsXExEvxymXy/lq4fz585W3Jln5MMZu3rwZGBi4bt269PR0ItLV1R0wYMCYMWP69u2rp6dHRD169LC2tp42bdqmTZuePHni5+cn7Anm8PBwS0tLRSLHhw8fyuXyZs2a8bc/1UhPT4+PjzcwMFBZUl8oIXNzcnenJUto9my6e5eUuXf+vTt37uzfv19XV9fT01PpnUGpDBw40NjYODw8/P79+1999VVWVtb3339/9erVJ0+eKKa0lMTMzKxnz54BAQEHDhxwdXUtRQuLFy+2t7fnS4LBwcE5OTn29vbKTpEKCrq6uiKRKCoqauzYsUSko6Pzww8/+Pv75+TkEFGbNm1q1KhR3M8yxoo7VpCSkhIYGGhkZKTskzIfZWRktGzZssmTJ7u5ubVv354vPn9IT0esVfzyIWPMvIld5+GzGtk5Xj2xreFXHVPjn8RH37Fp36eOBZJvg2ZQbzxaxMmTJ6lQfhdFpfUHDx4I3leRTDbCUmp+l4ULFxLR119/LZfLmcoz2VQC8fHxW7Zsad++vUQiqV+/vqmpafPmzT09PZOTkz/6/ODgYH7J8tVXXwmyBpuQkODl5WVnZ0dEmzdvVjy+a9cuIho5cmTZuyi5kJAQImrdurUqO4USyslhDRsyIrZ1q9L7ksvZ6NH7iGjhwoVK7wzKgB9wmDNnDr/71VdfEdHJkydV0PXRo0eJqEWLFmVvip/eX7BgQdmbgpILDAx89uzZ48ePt23bduLECcbY3r17+QphqS1btkwkEq3k6bDUoaCgoHHjxlWqVPnEte7Mkd2XOkmK+7p9do/P7N6BO5dvn9P3ecT1p/dCtrl2++vRzZx36er6pUABK4SqUb6yjwwYMKBnz55v3rzh80zNmzefOHFiQUFBWapBFGfWrFl8ba1JkyaCN+7u7m5hYREeHs73dgooLi5uw4YNIpGI79TPzMzkxx1Xr1796XdDTcAnFLiMjIytW7euXLny/v37/JG3b9/6+vr269evTZs2P/3007Nnz8zNzadPnx4SEvLw4UN3d/c6PEX+Bzp27Hjt2jUbG5v79++3bt06ODi4dMPLysrau3dvjx49GjRoMGvWrPDw8Dp16sgL5e5XS0YZtXQKJaSvT6tXExEtXkxpacrta/9+2rdvVJcuKYsWLVJuT1A248ePJ6K9e/fyM118tUfwz5qP6tevX+3atR88eMAThJYFzyiDA4Qq1r17dwsLi8aNG0+ePHmgQGVt+LmeT5w/FFxiYuKgQYMePXrE7+bn52dnZ+fl5RkZGdUshr5BFYNqNYr7EonEJvUafdl5QJveYy4fXE9E9W3a1rdpo18FVQ1AY6g7Ii3q0aNHOjo6Wlpa9+7dY4y9fv2a13n39/cXsJcDBw4QUZ06dd6+fStgsyroYtCgQUQ0duxYfpdPsrZv356vFmoyb2/vKVOmrF69OicnhzG2aNGi06dPh4WFdezY8ebNmxMmTLCwsOBnIZo0aTJr1qzQ0NDPWr/NyMjo27cvEenp6e3Zs6fkPyiTyYKDgydNmqSoNa+np+fk5HT48OHCJ38KCgratWtHRKdPny5542U3ffp0Ilq3bp0qO4XP8s03jIjNnq3ELrKyWP36jIiptswKlBJfFTx16hRjLDk5WUdHR1tbmydMVza+WdTV1fVzfzAnJyc1NTU2Nvbu3bunT58WiUSGhobKnvWHf6VYIZw6dapD8RwdHZsUw8LCgu+3UtlBdD4JojjqvHz5ciKytbUtdU2U6JuB53cuY4wVSPM3jG/99F5IwNZFgg0XygYrhKpR7gJCxtiMGTOIqGvXrvyul5cXETVq1EioV0N2djY/a7djxw5BGiwOP0A4b948oRrkNXyrVq2akJDAGHvy5Imenp5YLL5586ZQXVRc9+7d+/nnn/fs2ZORkREWFsYfzM/P79q16/Xr1yUSibm5ef/+/ffu3VvqvbUFBQX8YkgkErm7u/9rPPno0SMPD4+GDRsq5l/s7e29vLyKpGSIiIhwd3eXSCRisbhmzZqjRo1STYoIztHRkVRV8xNKJyKCaWszbW0WEaGsLhYvZkTM3p6hiGmFwIsMDRw4kN/lSz1r165VQdd8z0WNGjWCgoICAgIOHz7s7e29fv36H3/80d3dffLkyaNHjx40aFC3bt3atGnTvHlzCwsLY2NjLS2tIpPRZmZmDRs2LGNVQyg1vmV08+bNAwcOdHJy2rNnT1mSyvCqyIMGDVLByG/fvl04/oyPj+fboy5dulTqNqNvBm79b9dTG912zut34/QuBITlCgJC1SiPAWFaWhovCcg3uEulUr6fbc2aNYK0r7IC7sIeUywoKOCzwj///DN/pH///kT03Xfflb3xSiAzMzM/P//NmzcvX77k1eSPHz/u6OjI/7kOHjyYlJQkSEfe3t48ecOQIUOysrI+fEJKSoqXlxevv8xZW1svXbo0Nja28NNevny5du1a/jflGjRowN/4+MZpQUb7r/gK/EvUDS/fJk9mzZplfffdJmU0/vw5MzRkIhELDlZG8yC8IquCp06dIqIvvvhCGX3t2rXLyckpPf39YSq5XG5mZlbr8wtL6urqGhsbW1lZtWjRol27djzFN84QqldiYuKzZ8+ePXuWkpKSkJDwpHiPHz+OKl5iYiLPuKbsuUW5XO7g4EBEixa9D9h4LOrs7FyWZmXS/JzMtzmZb+WyAsaYrECan5stwHBBCAgIVaM8BoSMsc2bNxORlZUV3/534cIFIqpWrVrZL1tfvHjBJ5MuX74sxEj/xXfffUdE/fv3L3tTv/zyCxE1bNhQ8H+TSqygoGDo0KERQi+sBAYG8tSgLVu2fP78eZHv8t3C9Hf5iqCgoMIbenNycg4fPuzk5KT9d9ZIY2PjSZMmBQcHy+Xya9eu8QzajRs3joqKEnbYH0pISCCimjVrKrsjKKOUlKyaNU2IKCAgQPDGhwxhRMzFRfCGQYkGDBhAf2/2lkqlEomEiG7cuCFsL8HBwfxq7Pjx4/wRnoG2evXqnTp16t27t7Oz88SJE2fNmrV48WJPT8+tW7f6+voePXo0KCjo+vXrDx48iIuLS0tL+3DXQ0hICE966efnJ+yYQS1+/vlnImrevLlUKlVeL3v27CEiU1NTPkPBa27p6+s/ffpUeZ2CeiEgVI1yGhAWFBTwMtmKvFVOTk5ENGXKlDK2rIIigYUVV+rwc2VkZPBV02PHjjHGpFIp//dZtWqVQCOtPLKysqKjo0+fPp2bmyuTyYYPH66MLLUxMTHNmjUjIolEUmTLbnZ29rBhw44ePVr4/UtxklBRuEJxkrBI1cH4+Hi+ulizZs2LFy8KOOabN2/OmDGDn87lzp07R0SOjo4C9gJKsn79er7aLOzn4h9/MCJmaMg+mNmAco3n0FKsCs6dO1eQj8jCnj9/zlNtzZ07lz9y7tw5LS0tsVgsyKl+PvNrYGBw+/btsrcG6pWXl9e4cWMi+uWXX5TURVZWVoMGDYjot99+Y4zJZLK2bdsS0ZIlS5TUI5QHCAhVo5wGhKzQeTm+AhYdHe3i4vLixYuytKmWAu6rVq0iIhsbmzIeDPv9998nTpzIb2/atIkEPVdZmbx48eLXX3+9cOHCmDFjXFxcfvnlFyVl3Hn9+nXXrl2JSF9ff9++fcU9LSoqysPDo3CVP36S8BPlmDMzM/mhIG1t7S1btpRxnPHx8V5eXi1btuS9zy6UnISfRPrvf/9bxi5ABaRS6RdffEGCZgAqKGAtWzIitmKFUE2CikilUr6bgE9IRUVF8YW7j+5jL4Xs7OzWrVsTUffu3flJv6ioKF6n7qeffhKkC8bY999/T0QWFhYoT18JHD9+nE9l8ryjguMVmO3t7fl5H55Z19zcHDW3KjcEhKpRfgNC9veWmPHjxwvSmkwma9OmDRF5eHgI0mAJ5eXl8coWmzYJc/4nLS2Nn9/gKeZAjaRS6X//+1/6WJqZtLQ0b29vBwcHkeh9Ndz69eu7u7uXMA+bXC7nh12JaNKkSaXYhJORkbF79+4uXbqIxe+ry5iZmbm5uRVeLx03bhwRbdu27XMbB7XgmfqNjIyEyieZksL+8x9macmycV6mApozZw4RTZ06ld/laYr3799f9pblcjnfTdOkSRN+njk9Pb158+ZENHjwYAGn2PLz8zt27EhE//nPf5S61RBUo2vXrnZ2/1m8WPg59+fPnxsaGopEouDgYMZYZmZm3bp1iegTs7FQOSAgVI1yHRDGxsbyLJqCnItQYwF3vrfH2Ni4SHrJ0uF1AhRZWEHtvL29+YFAZ2fntLQ0f39/Z2dn/hbG5+w/PElYQn5+fvr6+kTUs2fPEtYvUexNrVq1Kh+Avr7+h1UupFLpmTNn+H6w0NDQzx0YqEufPn34HEFZGhkz5p+EpbNns0OH2Jkz7+9ev85wpKuiiIiIKLwquG3bNiLq1q1b2Vv+6aef+Bl1fgBbJpPxoju2traCf4AmJiaam5sTkZubm7Atg+o9eJCupcW0tdnDhwK3PGTIECJy+fus84IFC4jo66+/Rs2tSg8BoWqU64CQMebu7k5C1NnLyMjgZ+4PHDgg1Ng+S8+ePYlo+vTpZWzn4cOH2traWlpa9+/fF2RgIIizZ8/yw6IGBgY8DNPW1nZycjp06BBPAlRqV69eNTU15VP1n05X+/DhQw8PD15ShYjEYrGDg4O3t7ciPSDHq1zwzWZE9Ouvvwq1xwxUICoqik+T3bp1q9SN1KvHunVj/D21eXO2dStTFCw4coQh72MFws9Q8c+1t2/f8iWUuLi4srR59uxZflBQUROVL0XWqlWrjC0XJzQ0VFdX18DAANteKoFp0xgR+89/hGwzODiYF67kWdxiY2P19fVFIpHgWZSgHFJNQNi+fXszMzOenUgzA0JtKt8WL168Z8+ea9euDRkyhKdRKZ0LFy4kJia2b9+eb4NRvQ0bNrRs2XLr1q0FBQX8+r50Dh8+XFBQMGPGjLL8a4DgevXqdfXq1Q4dOmhpaTVo0GD8+PFjx45VBF1l0b59+9u3b/fv3z88PLxDhw5Hjx795ptvijznjz/+cHNzu3fvHr/btGlTFxeX0aNHK4JDIkpISNi/f/+ePXsePnzIH/niiy9cXFyGDBliaGhY9nGCCqSnp/fp06dNmzYhISHDhw8fPXr0R59maFgnO3t6cY107Uq1alGrVrR7N333ndLGCioxfvz4mzdv7t69+9tvv61evfqgQYP279+/d+/eH374oXQNRkVFjRgxQiaTeXp68lxu+/btW7dunY6OztGjRwtXVRVQhw4dvL29f/vtt1mzZllaWhYuxgMVzo8/0qFDdPEi+ftT//4CNCiTyfhB90WLFvGkMnPmzMnNzR03bhyfEAFNs2HDBktLy0GDBgnY5tWrVwVsrUJSd0T673bv3t2tW7ey/6bVq1fnmak4uVyu7DzFRSZTW7VqVb9+/TL+Fubm5l9//fXr16+VOnIohYyMDJ7/WhmlljMzM/mRWl1d3V27dhX57o0bN+j/l69QfCs7O7tIlYuaNWvypwk+SFA2Nzc3ImrXrl379u35hdFHWVnZELHivpYuZS1bsowM1rIle/Xq/Qphs2asVy/Wqxezs8MKYUXCVwXFYjFfOeHliCwtLUtXZTctLY0nihwyZAh/G7lz5w6fMFLBSWN3d3eJRGJvby/I2QpQo40bGRFr1IgJsqjDl2vq16/PN7MoMg4mJCQI0DqUe0VWCPlpMiLq2rWrkvbKaeYKoYgxVsYQRQXi4+N37txZlhYuXLgQEhLSvn370NBQkUiUnJzcv3//pKSkyMhIJS2PnDlzZtCgQe7u7itWrCCiR48etWzZUi6XT548uSwrhCYmJvwMIZQ3165d69Chg52dXVhYmDLaZ4wtW7Zs2bJlROTq6rp+/XotLS3Fd8+dO9e1a1fFwUW5XH716tW9e/f6+fllZmYSkZ6eXvfu3ceMGTNw4EAdHR1ljBCU6smTJ19++aVUKr1x40br1q3Xr1+/c+dOR0fHD99PDAxq5+RMK66db76hmTPp7l06coT+/JMuX6YZMygri+bMISI6epTu3KGVK5X6q4CQRo0adeDAgeXLly9ZskQul1tbW1tZWR08eJBXKio5mUzWv3//33//3dbWNjQ01NDQMCkpqU2bNvHx8dOmTeOFcJVKKpWOGDHi2rVrbdq0OXr0KN6mKq6CArKzo4gIWr2a5s0rU1Nv375t0qTJq1evjhw5MnToUJlM1qpVq/v37//8888LFy4UaLxQrunp6eXn58fFxfEdCnK5fN++ffPmzUtJSRGLxaNGjVq7di1PiCCUGTNmbNmyZfLkyfxgtqZQd0SqIoozhDwhlUwm4wm1ly5dqozuFJlFN27cyB/p0aMHEc2YMUMZ3UF54OPjQ0RjxoxRai87d+7kUV+vXr0+mmbm0aNHHh4ehXd28SoXmHSv6Iqkk5k5cyaVNoNIy5aKNnGGsMILCgoiooYNG/I1vVIfCZ49ezYRmZqa/vXXX4yx/Pz8Tp06EVHHjh2L1EpVnpSUlFatWkkkksWLF6umR1CSCxcYEatWjb18WaZ29uzZIxKJFMVyt2zZQkRWVlZlPJwPFcjBgwd1dHQMDQ09PDyy/86InZaW5u7uzi+HatSo4enpKcjb1OPHj52dnYnIzs6OJ5jRHJoSEDLGdu3aRYVK1vClQiXVJFy9ejUVqj3Ii/MYGxsrqTgPlAczZswgotWrVyu7o5CQED4Z1qJFC8W259evX/MqF4o4sEGDBu7u7pr2jlZZFSk48ejRIx0dHS0trXv37pWitb/LjLPHj9ns2ezCBRYQ8P6RGzfYwYPCjBlUQyaT8dPCly5dKnUje/bsISIdHR1FI4oKgcnJyQKNtEQePHjQsGFDiUQiSP0MUKN+/RgRmzChrO2EhITwbLdpaWl83fv48eMCjA8qiL/++mvo0KH8wsbS0vLIkSOKb0VFRfGpUiJq0qTJGUW+7M/39u3bOXPm8AizevXqa9euLd3G+4pLgwJCmUzGzx8vWbKEPzJ8+HAi+vbbb4XtKDk5mSecPHv2LGMsLy+Pn8ooe4VxKM94rhf+R1e22NhYXhPMxMTEw8Oj8BFBY2PjKVOmoJJEZfJhSfpevXpRoQJ0oOGWLFlCRGPHji11C2fOnKlevbqPjw+/u2HDBiIyMDC4ffu2MEP8HEeOHJFIJBYWFnfu3FF97yCUx49Z+/ZMwOPqfNYVNbc006VLl2xtbfl1jqOjY3h4uOJbQUFB/IqI75p5+Jk1T2Qyma+vLz98IRaLXVxckpKShB5+BaBBASFj7OrVq3xVkK+rvHjxokqVKkR0+fJlAXuZMGECEfXr14/fXblyJRE1b94cVXcrt9q1axNRfHy8arp7+/Ytjwp4vUEtLa1u3br5+vqihkTls27dOiKytrbmp+r9/f1JuLqmUAnExcWJxWJ9ff0DBw48fvy4dJ81imugoKAgbW1tkUh0UH2LxYsXL5ZIJHZ2dq9evSryOs/NzcW7XLllZ/fP7U6dWHAwq1uX8U/FP/8s03b0Mm6LgErgE5Fbfn6+l5cXX4zR0dFxdXUtYenm69evt2vXjgeTbdu2vXbtmjJ/g3JNswJCxtjIkSM0YJr1AAAgAElEQVSJaNiwYfyuh4cH3yss1NJwWFiYWCzW1dXlJeOSkpKMjIyI6Ny5c4K0D+VTYmIiv0ZXZacFBQX79u3z8vLavHkzYoPKquDVq1aWlkTEN8Mozid7eXmpe2hQjkycOFFRrUFHR8fKysrJycnd3d3X1/f27dslj6CePn3Kd+UpttKohVQqHTp06Lx5865evbp27VpF1c3t27c7Ozu7uLjMnDlTjcOD4lhb/3Pb1pb9+Sezs2POzowxFhjIyvJH4xOg06ZNK+sQoYJ78+aNu7s7TzTKTw8qEpCmpqa6urryfHu1atXy8vL6RNb3Fy9euLi4iEQiIqpXr56vr28ZC55XdBoXEMbHx/NVQX5SIjs7m5++2LlzpyDtd+7cmYjm/n1GZ9y4cUQ0cOBAQRqHcosf8ercubO6BwKVzuTJcmPjM1Om8Htr1qwhombNmvHzyQAKa9as6dmzp4WFBb/EKUxLS6tx48YDBgxYsGCBr6/vrVu3MjIyPmwhMzPzyy+/JKIBAwao/fwMf4Vfvny5R48ex44d4+O5cuUKv2hzdHRMT09X7wjhQ1ZW7MKF91/W1uzPP9nEiczFhQUEvA8IBwxgbdp8/GvQoDdtisE3BGJbBChER0fzQqlE1Lhx49OnTyu+defOHZ4Qq3nz5h/9oMzKyvL09OS7qwwNDd3d3TMzM1U49nJK4wJCxhhP3G9ra8tnDvbv309EderUKeH68if4+fkVburOnTt8tfDx48cCjBvKsV27dvXo0cPT01PdA4HK5e5dpqXFtLVZRARj7PX/P58M8FG5ubkRERGHDx/28PBwdna2t7fns+lFGBsbOzg4TJo0ycvLKygoKCEhgWdusLGxKfunoVD++OOP2NjYU6dO3blzZ+XKlYyxe/fubdiwYd68eeoeGnyEpSVbs+b9V9267wPCxERma8v8/dnMmczCotgSqc2bZ374KlWYPn36oUOH1P37QfkSFBTED9jz04M8+RBjTC6X+/n5fTTPlr+/P18HIiInJydlFySvQCpGHUJh5eTkNG/e/NmzZz4+PhMnTmSMOTo6BgcHz58/f9WqVWVp1sbG5vnz59u3b//+++8ZY507dw4JCVmwYMFKVPWq7GbPnn3o0KGVK1eOHTtW3WOBSqRLF7p0iWbPpvXriYgmTUq6f//XJk2W79mj7pFBRZKfnx8dHR0ZGRkZGfno0aOoqKjo6Oi8vLwiT9PS0qpevfrNmzcbNWqklnF+QkFBgVQqNTAwuH///q1bt86fP793796PBrqgRo0bU0zM+9t2drRhAx04QD4+tHkzHT9OLVvS+PH0wevuPS2tHJnsQXEtN2jQwMzMTAlDhopNKpX++uuvS5cuffv2rY6Ozvjx43/66aePVmENCwubOXNmSEgIEbVq1crLy4svJMJ7ag5I1eTQoUNEVKdOnTdv3rAPDv6VztKlS4nIzs6OLzzu27ePiExNTbGtRRP07t1bIpHcuHFD3QOBSuTIEUbEatZkr18zxlhYGNPSYrq6LCpK3SODyiAhISEoKMjLy2vSpEkODg7VqlXbvHnzxYsX1T2uT1GcCBo3btz9+/fVOxj40IdnCCdOZIyxggJmZ1emM4QAn/D69WvF6cGaNWsWOT346tUrxXdNTEw+fbZQY2loQMgYc3R0JKI5c+bwu+PHjyeiAQMGlK61IglLs7OzGzRoQES7du0SbMRQXslkskaNGkkkEgT/IJicHNawISNi27a9f6RzZ0b0Tw1BAKGV54uke/fueXl5TZ8+fdOmTdu3b+/VqxeO0ZZDJ0/+c/vMGZaczBSFS548YYUqBQAILzw8nCfyICIbG5tz587x7KM8uSPPPorrtOJo4pZR7u7du61btxaLxQ8ePGjatGlycnLTpk3T09PPnTvXs2fPz21t5MiRfn5+w4cPP3jwIBH98MMPP/74Y6tWrW7duiUWi5UwfChHnj171qFDh7p1696+fVvdY4HKYsUKWrKEvviC7t4lbW06dIhGjKA6dejxY6peXd2DA1C1vLy8rKysatWq3bx5Mz8/v23btnwSFgCgsCNHjsybN+/58+dEVLNmzbS0NCIaMGDA2rVrra2t1T268ktzYxVbW9vvvvtOKpXOmTOHiExNTRcuXEhEs2fPlkqln9XUtWvXDh48aGBg4OnpSUQvXrxYt26dSCTy8vJCNKgJoqKiiKhZs2bqHghUFgkJxM8zb9hA2tqUk0MLFhAR/fQTokHQTHp6ejVr1tTR0XFwcOjSpQuiQQD4KGdn58jISE9Pz2rVqrVr165p06YBAQEnT55ENPhpGh2urFixokaNGgEBAWfPniWiWbNmNW3atEuXLh8etf80XV1de3v7uXPnWlpaEtGcOXOys7NHjhyJ46oaIjIykhAQgoDCw4mIBg+m7t2JiFavpmfPyNaWxo9X77gAAADKOQMDA3d392fPnh08eDAiIqJPnz7qHlEFoLlbRrl169bNnTu3WbNm9+/f19HRyc3N1dfXL0U7crm8oKBAV1c3NDS0U6dO+vr6UVFR/BghVHpTpkzx9/fftGkTT9oOIIDERJLLydyc4uOpWTPKyqJLl8jRUd3DAgAAgMpGo1cIiWjGjBlNmzaNior65ZdfiKh00SAR8SSlcrl81qxZjLEFCxYgGtQcWCEEIb15QydOUGgo6egQEW3fTllZNGwYokEAAABQBk1fISSiM2fO9OvXr0aNGo8fP65du3ZZmvLx8Zk8eXL9+vWjoqIMDQ2FGiGUc2fPno2NjZ04cSKKYkFZPX5MI0fShAmkrU0+PrR1K9nb0/791KkT/V1LFwAAAEBACAiJiPr06XP27FlnZ2c3N7dSN5Kdnf3tt9+mpKQcOnRo2LBhAg4Pyi3GWGxs7LFjx5ydna2srPiDUql01KhRYrGYp5wF+Axjx9KIEdS7NxHRzZu0dCn9/ru6xwQAAACVGQJCIqLIyMhWrVoZGhry7LSlVq9evYYNG16+fFkkEgk1NijPpFLprl27zpw54+bm1qVLF/6gl5eXnp7e77//fvr0afUODyoee3s6f55MTIiIZDKytqanT9U9JgAAAKjMtNU9gHLBxsbm2bNnc+bMefLkSVnacXV17d69O6JBzaGjo2NgYPC///1PW1v7xIkThoaG1tbWwcHBu3fv/h0LO1AKVatSVtb7gDA3l7DzHAAAAJQMAeF7pqam+/btU/cooOIZM2YMv2Ftbc0Ymzhx4o8//ohJASilHj1o71763/+IiPbsoV691D0gAAAAqOSwZRRAMKGhoa6urtbW1jKZLCIiYsmSJaNGjVL3oKBCycmh6dPp5UsSi8nIiLy9UYkeAAAAlAoBIYAwfv/9dwMDA36SMDMzc+TIkThDCKUkkxFjpI0dHAAAAKB0CAgBhJGcnKylpWViYkJEUqn0xo0bHTt2VPegAAAAAAA+BQEhAAAAAACAhhKrewAAAAAAAACgHggIAQAAAAAANBQCQgAAAAAAAA2FgBAAAAAAAEBDISAEAAAAAADQUAgIAQAAAAAANBQCQgAAAAAAAA2FgBAAAAAAAEBDISAEAAAAAADQUAgIAQAAAAAANBQCQgAAAAAAAA2FgBAAAAAAAEBDISAEAAAAAADQUAgIAQAAAAAANBQCQgAAAAAAAA2FgBAAAAAAAEBDISAEAAAAAADQUAgIAQAAAAAANBQCQgAAAAAAAA2FgBAAAAAAAEBDISAEAAAAAADQUAgIAQAAAAAANBQCQgAAAAAAAA2FgBAAAAAAAEBDISAEAAAAAADQUAgIAQAAAAAANBQCQgAAAAAAAA2FgBAAAAAAAEBDISAEAAAAAADQUAgIAQAAAAAANBQCQgAAAAAAAA2FgBAAAAAAAEBDISAEAAAAAADQUAgIAQAAAAAANBQCQgAAAAAAAA2FgBAAAAAAAEBDISAEAAAAAADQUAgIAQAAAAAANBQCQgAAAAAAAA2FgBAAAAAAAEBDISAEAAAAAADQUAgIAQAAAAAANBQCQgAAAAAAAA2FgBAAAAAAAEBDISAEAAAAAADQUAgIAQAAAAAANBQCQgAAAAAAAA2FgBAAAAAAAEBDISAEAAAAAADQUAgIAQAAAAAANBQCQgAAAAAAAA2FgBAAAAAAAEBDISAEAAAAAADQUAgIAQAAAAAANBQCQgAAAAAAAA2FgBAAAAAAAEBDISAEAAAAAADQUAgIAQAAAAAANBQCQgAAAAAAAA2FgBAAAAAAAEBDISAEAAAAAADQUAgIAQAAAAAANBQCQgAAAAAAAA2FgBAAAAAAAEBDISAEAAAAAADQUAgIAQAAAAA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t2rXr9evX+vr61atXV0HvOTk5vKjs0qVLDQ0NFQoFPwvOycnJ3NzcysqKv4w/VMF4QAV4vmiPHj1U3G90dDRpQEBY8dVPPT09IyOjLVu2+Pr6dunSpUWLFtu3b586daqoZ5opFIpDhw716tXL1dV1586dKipptWYNrV5NhoZ08SL170+qKr+BPYQgCB6NjBo16tPOdvhIFBUVjRkzplOnTlKpdODAgXfu3NHR0ZkyZco333xz79491YxBT48WLSKxs+fi4+PHjBnj4OAQHx/fuHHj9u3bOzo62tjYaGtrDxw4EHX/K6lr167ffPPNq1eveGVgYSkUipkzZzLGFixYUK9ePcHbL23ixIn9+vWLiIjYunWr4I2/evWKH+S4cePGT3xW+uNTv359GxubFi1aeHh4LFu2LC4urnv37vzgRxXYtGlTSkpK+/bt+cb4ffv23b5928LCYu7cuaU3mE2dOlU14wEVUEtFmdTU1N9//71WrVqtWrVSZb9qwCpqwoQJycnJa9as2bRpk52d3ePHj1u0aCGTyVq1anXs2LEKN/sucrl83759jRo14sPmlaN69eoVHx8veF//IyODNWnCiov/ejh1Kjt0SNwe//b1118T0eHDh1XTHXySsrKyrK2tLSwsHj9+rO6xaCJfX9+ZM2fK5fKEhIRBgwZlZ2cnJiaK193Ll+zlS9anD2OMDRrEfH2ZnR1LTf3/C5ggMjIyVq1aVa9ePZlM1rRp061btxYVFb31lb169RKyYw3z9OlTQ0NDiUQSGhoqbMvbt28norp16+bm5grb8lvxE+pMTEyeP38ubMv8APqBAwcK2yxUQHx8fL169erUqXP37l2x+3r06JGVlZWlpeWNGzcYY1lZWW3atJHJZIGBgWJ3Dar36tUrHx8fe3t7Y2NjLS2tP//8U5W9nzhxgoi++OILVXaqFhVfMVi/fn2tWrV69OjRpUuXQ4cOWVlZnTp1qmfPnq9fv54+fbqjo6NQB+gxxoKCgmxsbMaNG3f//v369et7e3sfO3bM0tLywoULrVu3XrZsWZmTMISUkEDNmpH079zajh0pOlqsvv4XVgih8k6cOJGfn9+tWzdlCg2oUoMGDYqLi7W0tKpWrZqenr5ly5Zu3botWbKE/+sWUFERbd5MDRrQ77//9czmzbRyJRHRpEnUsiWdPi1ALzxHo3v37tu2bZPL5SNHjgwNDZ0xY4aOjk5RUdG2bdvCwsIE6AaIiKhu3bqzZ89mjP3www/K09UqLysriyfXbdy4UTVnedna2g4cODArK8vDw0PAZm/evLl7924dHZ2NGzcK2CxUTKNGjZycnORyubA/5bfy8PAoKipycHBo27YtEW3YsOHly5cdOnSws7MTu2tQmZs3b65atapTp07m5uZOTk6HDh3Kzc1VKBSjR4++c+eOyobBlyVVn6eqBoKHmIGBgc2bN5fJZNbW1lu3bi0pKalMayEhIe3bt+dDrVevnre3d/Hfc93p6enOzs58qbB169YRERFCDP9/xcay+Hhma/v/z3h5sY0bhe/obTp06EBEly5dUk13jDGFQrF3716FQsEfvnr1ysPDY8aMGb/99pvKxvApCQwM3Llz5/Lly7Ozs9U1Bnt7e5lMhnVmtejfv79CoXBycpo+fXr//v1Pnjw5a9YsCwsLmUzWrl27I0eOKP+tVdKhQ6x+fUbEiNi0aX+tEDLGli1j/fqxBg3++tKwYezBg4r3EhYW9uWXX8pkMplMZm9vf+/ePeWXTp8+3aVLF5lM1rt3b7lc3r9//4cPH7q5uTVt2tTNze3JkyeVe3+aKzc3l6d07t27V6g2Z82aRUTdunUT6tfvQ9y/f19PT09LSysyMlKoNvnBOTxmho9BRkYGzxQ7efKkeL2EhobKZLJGjRq9ePGClVotvHXrlnidgmrk5+eHhIS4urqWTmU3MDCwtbX18vKKj4/v168fEVWrVi08PFw1Q+rcuTMR/fHHH6rpTo2EDwgZYy9fvnRxceH3DYMHD46Li6tAI6GhofxyT0S1atXy9PTMz89/82UXLlxo3LgxEUmlUldX15ycnEoPnzHG2O3bzN6eaWmx69dZ69Z/3UYVFLAuXVh0NPPwYCdOCNPR2zx69MjZ2VkqldasWbNXr14qS/bz8fH59ttv16xZk5mZyRhbv3795cuXX758aWtri1u6cnn16tWYMWOIiG9radmy5YPK3IlXVExMjL6+/sCBA/Py8lTfOyg9f/5cefm6cePG4MGD+eWxf//+165dq0zL9+6xfv3+iveaNWO//84UCpaV9ddXS0pYdjYrKmJeXszEhBExHR3m6soyM8vXy9OnTx0dHZs2bVq3bl0bG5uAgADll+7fvz9u3Dj+drp3737mzBn+fEFBweO/FRYWVuY9ajhfX18iMjc3zyzvj+1tYmJidHR0hA3MPpCwgWhAQAAR1axZMz09vfKtgVD27t0rk8k6depUUFAgRvvFxcVOTk4ymWzbtm38mfHjx8tkMjc3NzG6A9VQJoXyk7G4mjVrOjo6BgQEZCk/0hgrLCzkh6YaGRkFBweLPbC8vDxdXV1tbe2MjAyx+1I7UQJC7tSpU23btpXJZEOGDFm5cuW7Npm8KTw8XFnL1MzMzNPT883b2cLCQuUddl5enru7u7a2NhFZW1uHhIRUatx37rDhw5lEwohYlSrswAF24wbr3ZvZ2bHu3dmePezaNaalxYjYqFHsxYtK9fWGx48fT548WSqV8liC54saGxtv2bJFLpcL29ebIiMjt23b5uXllZmZefr0af5kdnZ2v379Xgj9TkWVnZ29bdu23NxcvkD96NGjb775xsHBYc2aNSroPSAgoGbNmkRkaGjo5ubGT2oyMTFR/fYGfirX1KlTVdwvvJ9CoQgMDLSxsZHJZBYWFs7OzklJSeVtJDWVuboybW1GxGrUYF5e7P3ZGMnJzNn5r9ebmf3z67msrKz58+fr6enxC9GuXbuU0V16evrixYvr1q3LdxL+/PPPxcJuVQTGGGMKhYIXUViwYEHlWxswYIC6rgmZmZm1a9cmotITChWTn59fv359IvL29hZkbCCUkpKS3r17f/bZZzt3Xhaj/S1bthDRiBEj+IXowoULMpmscePGKSkpYnQHooqOjvb09OzWrVvpinfNmzd3d3cPDQ1918xRSUnJxIkTiUhPT+/o0aOijvD8+fNE1K5dO1F7+UiIGBAyxjIzMxcuXMhvjlu1anX16tX3v/7WrVu8ZDkR1ahRw8PD411zoh4eHgYGBjyPnD9z48YNfpiSRCJxdHR8/fp1uYf78OH/3zEZGjJX1/+J95R3TwoF8/ZmVaowIlatGvP2ZkLMd6akpLi7u+vr6/PVTkdHxwcPHrx48cLR0ZH/hXTp0iU6OrryHb1Vdnb21q1bmzVrFhcXV1RUlJGRwf+ZnTp1atCgQXPnzlVBOCqgY8eOjR07du3atXzyeP/+/Xfv3lUoFBMmTAgLCxOv3xcvXowYMYL/vHr06MErHmVnZ/MntbW1PT09VZamVVxczKvVX7lyRTU9QrlkZ2evWrXKyspKJpM1adJk8+bNHzinXlRUtGNHULVqf634zZzJPnyNJCKCdeny14riwIEP3/PPQaFQBAQE1K1bl19U7e3tlWkCxcXFfn5+LVu2lMlkdevWdXNzq8j1Fj5YZGSklpaWrq7utWvXUivh4MGDJE5xlw/EjwivU6dOUlJSZd7IwoULiahNmzaV3JMCYggLi65XL9HYmD17JnDLqamp/KjJEydOMMaKior69+8vk8l27NghcE8gmrcmherr6/Ok0DeLrj148GDTpk2jR48ufe/E6yTz2yoB0+nftGLFCiL64YcfxOvi4yFuQMiFhoY2bdqUiLS0tJydnd+6nyo6Otre3p5vCDQ2NnZ3d39/HsiUKVP4r1G7du2ioqL4k8XFxZ6enjymql279odPQxY9fswmTmRSKSNienrMxYX94+fls2ds2LC/bqx69mQVSovlXr165e7uzqu0a2lp2dvbl0myPXnyJL8t09HRcXd3FzYTIycnx8vLq2nTpjzp66effnrzNdOnT79w4YKAnYrt7t27/v7+mzZtyszMLP2O5s2bp0xpE1xAQAD/rKpataq3t3eZi5enpyefA3NwcBAssfm9goKCiKhJkyYq6Asq7NmzZy4uLrwWQt26dX18fN7/+pCQEL7m3LHjC1tbdudOuXtUKFhAALOyYp06DSMiXiO6zGsiIiKUp1Z26NChdNx44cKFXr168cuFg4NDbGxsuUcA5Tds2DBBjlWQSCQ//vijut5FSUlJw4YNjYyMKvkueO7M+fPn1fVG4P2+/poRsQkTBG52+vTpRNTn703SmzZtMjMz++abbz48AQ3Ua+fOnaX/+fMEmcDAwDLljuVyeWRkpIeHB1/m4XhF2dI8PT35NW3z5s0iDfirr74iokOqOlxAvVQREDLG8vPzPTw8+KG09evXL707MyYmxtHRkSd8GhkZubq6fuDS/59//slPoSize/D+/ftffvkl/wWys7N7f5H3xMREV1fXLz77jOnqMh0d5ujIEhLK8cYCAljNmoyIGRgwT88PysEqJTU11cPDg+dMSyQSOzu7mzdvvvWVmZmZrq6uPKJo0aLF5csCJGMUFhb6+fnxYs08s1d525eTk3Px4kU/P7/k5OSioiJ7e3tl1P3vUlBQoFwfe/ToUd++ffPz8w8dOhQTEyNgL8+ePeNHIRHRwIEDnz59+taX/fbbb9WqVSOi1q1bP3z4UMABvBU/tmTt2rVidwSVd/bs2ZYtW/JfoT59+ty+ffvN18TGxg4cOJC/pkmTJqdPV6r0dk6OfMmSJXweysjIaMWKFTwz/9mzZ87OzvxSY2Fh4e3trcwOuH//vr29vVQq7dKlS5cuXVDhXZX69OkjkUgMDQ2rVwJP/VXjbqv8/HxLS0upVFqlSpXKvJEqVaqQEKmnIJKHD5m+PpNImIAHpty9e1cqlUqlUn55fPnyJf88RdG7f4u9e/e2a9eO3p0UmpmZGRAQ4OjoaGZmpowDq1evPmbMmP379781YdDT01MikUgkEjFudUpKSqpWrUpEycnJgjf+EVJRQMjdunWLV84kIh5j8NIpRKSrq+vs7Fzev/QyuweViz8KhcLb25sHWlWrVvXy8noz4/HFixczZ87ky4na2trR8+ZVsADfy5ds7FhGJDc1dbC1feud3Juys7M9PT355YyIbG1t37rF//Xr1zt27FAO/tKlS82aNePRo7Ozc+mNtuVSVFTk5+fHd3jKZDI7O7sy51ylpaX5+fnFxMR8//33EydOPHjwYMU6+ng8fvzYzs7u3r17mZmZn332mUwm69Wr19atW1NTUyvTLP9N43cn1atX/8cNLfHx8c2bNyciU1PTym52fa/Xr1/r6elJpVINuZB9AuRyuY+PT61atYhIS0vL0dFROTWWnZ3t4eGh3Mjn4eEhVJpAYmKio6MjT82oU6eOj48P/8A2MDBYvHixcpYtMzPTzc2Nz+iZmJjs2LEDU/KqdPLkSRIi1VOZeir6+b3vwPOvKp/qyVNPVXaOIlTAwoWMiNnYMKG2m/C1GldXV/6QH0HZt29fYVoH8Y0bN46I3sxQePTokbe3t52dHf+M46ytrfni4T/WJNuxYwefwXR3dxd2wFevXiWihg0bCtvsR0ulASH7O6uTT0vz/+vq6k6bNq0yhzVHRUXxmxi+e1B5i5+cnMwXSYioR48eytSmN9flBKhW/NtvOwcNIiIdHZ3Fixe/53YtNzfXy8vL3NycD6xbt27vycYcP348EXXt2lV50mvptVYLC4vjx4+Xa5hFRUUHDhxo3769ssjh2bNny9XCv0tWVpavr++ZM2f4gsatW7fu3r27cOFCZYps/fr1p02bduHChQpskkxISOjdu7dyLfrZh22YyMrKGj58OP29pbD87+mDbNq0iYgGDx4sUvsgkrS0NHd3d/4PvHr16v/5z392797NLxc8ShSjvNOVK1c6deqkXHvs06fPo0eP+JcUCoWPjw+vBcIvsOrafqaxioqKmjRpQkQbhTjxyMnJiYiGDx9e+abKKykpiWeLVT7VUy6X81yyFStWCDE0EF52NrOwYETM11eA1o4ePUpENWrU4NuVb968qa2tLZVKxSusAILjhyErV03y8/Pd3d35DghOKpX26tVr3bp15d2JsH//fp5RP3369MoXvEhNTeULlcbGxvyAEw0plK3qgJCLj4+3tLSsUqVKv379lHcelVFUVOTp6clnF2rXrl361LWAgAA+6W5gYLBgwYLVq1eXXpe7fv165XvncnNzlcuVDRs2PHfuXJkXFBYWent78zofPMz7x2AsKCioTp069Mbuwdu3b3fs2FG51vry5ct/HJ5cLg8ICGjUqBEvef/FF18EBgaq8hwqtcjLywsJCdm7d++qv/G02IKCguPHj48ePdrS0pJHhu3bt1+7du0Hnq4hl8u9vb35zY25uXl588tLbykcM2aMGJPcbdq0ISKxC3CBSO7evdu3b18qpWfPnqKmbZeUlHh7e/MCYA0bNuQLOFevXuVHMBFRx44dBclUh/Jav349/6EIclPy4sULPhmq+mO1+PqAvb298pns7OyJ5XHs2DHl9166dEkikRgYGOBIpI+Wjw8jYi4ulW2nsLCQ7w9SHjXxxRdfENGsWbMq2zSoSmJiIp/lLB2w8VrBRkZGdnZ23t7elZnuDAoK4hl/48aNq1i963v37q1du7ZHjx78Np7jt3n9+vXThGQE9QSEjDG+zU/YCh/379/nlwm+YqOs5P769esJEybw6Qf+1f79+4tykD1jYWFhPCeQZ3XypOeioiIfH5/PPvuM996pU6cP336TkZGh3D3YsmVL5Y44HpPwvfXvT1ZUKBT+/v4815SIOnfufOLEiX9X1VDxvHjxYuvWrfxMbZlM1rp1axsbG29v7/ccJR8dHa1cTrG3t69wfcWgoCCent62bVtBpkWUIiMjicjU1FSkk6BANU6cOKGjo6Otrb1v3z7V9Jient63b18dHZ2JEycqU0ktLS19fHw++cmjj5Nyo9SpU6cYY4WFhf9YrPtdlPsCfvzxRyJq3ry5Ko8JuXz5skQi0dfXL32te/nyJZXHkiVLSrfJa5KPGzdOZe8CykWhYIGBbMWKv7JG4+NZxfYdl/mN5UdQ1qhRo5I7PkCV9u3bx+/MSz95+PDhc+fOCXUhOn/+PN+/M2TIkLeeW/6mkpKS0NBQd3d35R0yEWlra3fr1s3T0zM2NjY6OtrCwoKIunfv/skfRai2gJBvJqzwZ9u7lN7TVa1atdLFHrdv386fDBVwm/PblF6ulMlk33//vTIUbNu2bcUqMbyrUuvDhw+VKwkDBgx4c640JCSE59MSkZWVlbe3N84Ke5NCoQgPD1+4cKGy/lWVKlUmT5586dKl0i/jCc/8J1uBfN03xcbG8h+rmZlZ5XN3S0pK4uPjjx8/3qVLF8yefhp47qgqA3t+nWzdujURGRoaenh4aMLM6EeLb5QaOHAgf7hu3TqJRLJ48eLytsOrXvFVwTfXW8SmUCh4SkuZkefn5+8ujzKL5E+fPjU0NJRIJGJ/pkOFXbnCjI3Z9u2MMRYczCpQvb/MmrbyCEocNfHvMm3aNCISb5sMFxERwYu99+7d+z3T+sqkUGXCIJ9Dt7e39/HxKRP4JSQkWFtbE1G7du0+JB3v30ttASEPx/nWuJiYmH379l27dk2oxp88ecLL8TVu3Fg5TxAWFkZEXbp0EaqX97tz5w7PtuK/nc2aNfPx8anMutx7KrUqDzwwNDT09PTk6V4hISHKtNI6dep4eXlhvegf5eXlBQQE2Nra8rUR/ivk4eHx+PHjW7duKQ+6VK79Vl5mZia/V5NKpeW6VhYVFSUkJAQGBnp6ejo6OtrY2BgaGiovbTVq1Jg5c6YgIwQ1UldAOHHixClTpryrXi6oxo0bN7S1tXV0dPiOmpSUFJ5TcPr06fI2tWbNGv4xxKsBHTlyhErtyBLbnj17iMjS0vI9t2gVs3jxYn6jhpyXj9OVK2zCBNa+PXvx4q+A8Ngx5uf39v/8/V/7vWHXrl1Dhw7t3Lkzrw3O6xK1aNECU9v/Lq1atSKiMpPsYnjPml5CQoKXl5etrW3pI3ysra1dXV1DQkLe8xv15MmTxo0bE1HTpk0rU/HkI6e2gJBvjeM3HFu2bCGi77//Xtgufv3114sXLyof/vHHH6TamlQlJSU8hJgxY4ZQH1dlKrW+evWKP//8+fORI0cq1yGVr6ldu/aHH3gNSnFxcQsWLLC0tFSmECh3h/75Z6XK/b9JoVB4eHjwEHT8+PG8+n8Z2dnZ165d8/HxmT9//tChQxs1alQ6zV3JysqqX79+Q4cO1dLS0tLSOnnypLBDBRUrHRAePnx4woQJYh/2wAPCqVOnitoLfIhevXoR0ezZs/nDSZMmEdGQIUMq0FRhYSG/odmyZQt/pkzNRvFkZ2fz+zM/Pz/BG8/NzeXHW+/Zs0fwxqHyrlxhU6awoCA2btxfAaGV1V/nN7/5X/PmV978UFMaNWrU4sWLhapLBKqUlpampaWlp6f3gZmclVR6Te/58+fvTwot871Pnz7dvn37wIEDy5xt+OLFC16awcrK6v79+yp4F6qntoCQL9SmpaUxxlavXk1ECxYsELXHw4cPk8qrq/GFSmHvy4uLi728vPhlsVatWqVPsuZFaIyMjCQSiampqYeHR4WPpgDGmFwuDwkJcXR01NfXNzIyGjhwoOAz3Er+/v78Z9quXbs7d+5ERkb6+Pi4u7vb2dlZW1vzTaSlSaVSa2trW1tbV1dXb2/v0NDQ0j9r/m+qSpUqKML2r1Y6IFy4cCERrVq1StQeERB+JPz9/YmoZs2a6enpjLGoqCh+YkRcXFzFGjxx4gQRVa9enU8jljnVTTzz588nos6dO4u0DdXPz4+IzM3NhcraAAHxgJAxNnQoW7qU/fADmzmTjRv39v9cXR+Me4cBAwZIJBI9Pb1mzZqNGDFC3W8LyicoKIiIevbsqbIenzx5whPj+XEGnJmZmZOT06FDh8rcGCsUioiIiCVLlnz++efKF/fo0aNMm+np6V27duULLQKcTfA2iYmJd+7cUT4MDw/fsGHD8ePHVZMBobaAkNd34ekrCxYsUMGNzt69e4lowoQJovZSRs+ePYlI8DUl9r8VdAYPHqz8/b5+/Tr/dEQoKKA5c+aQ+Ac637x5k283LX0J43R0dKytre3s7Nzd3X18fCIjI9+/s0uhUIwZM4aIGjduzKdd4N8IAaFmUm6UUlYL4x8llbwE9evXj4hmzJjBH06fPp2I+vTpU9nhvltCQoK+vr5EIhG8XoCSQqHo3r07Ec2fP1+kLqDClAHho0esZs2K7CFUcnR0JKKvv/6aT5HAv4i7uzsRLVy4UJWdPn/+3NjYuEaNGvXr1+dJoWWOz+VV6F1dXZW5YERkaGjIS56+9SCxnJwcXrajevXqgpfdjoqKWr58+fz58/kC+PXr12fMmHHt2rWZM2fu3r1b2L7eSj0BYX5+PhHp6enxhy4uLkRUZn1WcFu3biWi6dOni9pLGXy+gR9uERoaOnbs2F27dgnVOD8izNTUtFevXsrJ16ioKCJq06aNUL0AY+zs2bOq+Vu9efOmRCIxNjZu3779hAkTPD09jx8/Hh8fX4FznPPy8tq3b09Effv2reQx0KAuCAg1U5kD3Pfv38/zQSpZ5u7evXu8bi2f3k5NTeWbz8XLQ+Znrjo5OYnUPnf/T5qwAAAgAElEQVT9+nW+fBofHy9qR1BeUVFs5cq//rx5M1u3ruJNJSUl8bLqwcHBgowNVKZbt270d6lklXnw4AFfFSyTm5CSkuLj42Nvb89/nTgrKytnZ+fAwMB/3GBVUFDATzg3NjYW9qCEwsJCT0/PVatWFRQU3Lp1S3nO0C+//LJ+/XoBO3oX9QSEvNi0mZkZfzhx4kQiEjsC/s9//kNE8+bNE7WXMviaNU/y2blzJxF9++23wnaRnJz8+PFj5cOLFy8SUffu3YXtRcMVFhbyRNzk5GRRO+I3gmPHjq1wC2lpaW5ubvyi9uTJE34I59y5c4UbI6gOAkINVOYA97y8PH6gsyCTiXz6tXfv3vyhl5cXETVo0ECMfeZ8Hs3Y2Pitc+3CcnJyIpVvCYH3u3qVWVgIczA9t2rVKkJFmX+bgoICfX19LS0tFS/t8lpWX3/9tfKZ48ePl04K1dLS6ty58+rVq8ub/1lSUsIvOHp6eqUPR62YvLy84OBgNze3tm3bhoeHp6SkKBSKjRs3pqenZ2RkTJo0qWvXrqr5qyu7MUk1cnJy+OcEf5idnU1E/KwI8eTm5pbuVDVKv9My71ooMpmM3y682SMIRVdXl6e/nzt3TrxeGGM+Pj5ExKdIKsbBwWHdunU8GaxevXrHjh3T09Nbv379L7/8IthAAUA07u7uubm59vb2fFPAmjVrnjx58vnnn1fmsqC0fPlyMzOzc+fOHT9+nIhmzJjRsmXLhIQEXtpNQHK5fNasWUS0cOFCXlRGVGvWrDExMTl27FhwcLDYfcGHYIxmz6bkZIqNFazNuXPnNmzY8O7du7t27RKsURDZ1atXCwoKWrduXfqMBxW4dOkSEfF8cqUbN24ok0ITExMvX768cOFCfszSh9PW1t6zZ4+rq2thYaGDgwM/GLO8nj59unv37tGjRzdt2tTJyenXX39NSUmJjY2tVauWRCKZNWtWtWrVqlatunv37h9++MHT07MCXZSXegJCHgEqgxbVxDBqiZRKv9My71oFPYKAeOJ4SEiIeF1cuHAhISGhTp06X375ZYUbWbdunZGR0S+//MLv8Lp27bpjxw4imjFjRkREhGBjhU+UoeHwjh2fm5ktUfdANNTly5f379+vr6+/du1aIkpKSlq/fj0ReXl5vVlZqgKqV6/u4eFBRHPmzCkoKJBKpXyRcOXKlc+fP698+0o7duy4ffu2tbU1DwvFZm5uzqvXzJo1q6SkRAU9wvv9+iuFhZG5Obm7C9amnp4eT/VatGhRamqqYO2CmN4amKml3759+/7+++9paWlBQUHOzs6VmaiSSCReXl7z5s0rKirie8E+5LsUCsXt27c3bNjQv3//Ll26LFmy5OLFiwqFonXr1nPmzPn999+Vs34PHz588OBBXFwcEZmamhYWFlZ4qB9OnSuEyiVBVUZKYq9DlsbXgiUSCc//Uc0SZZm/WxAKDwj58Y8idcEzHCZNmqStrc0Y+/HHH2PLP7napk0bX19fiUQye/bs06dPE9E333wzbdq0goKCYcOGPXv2TPhxwyckL888IqL269eW//xSEBpjjB8f6ubmxovKuLm55ebmjh49mheVEcS0adNatWr18OFDHgr26dNn8ODB2dnZS5YINguQnp6+bNkyIlq/fr2+vr5Qzb7fnDlzGjVqdO/ePb47A9QoL48WLyYiWrOGTEyEbHnkyJF9+/ZNS0vj6aPw8VNLQPj69ev4+HgjI6O2bdsqnzQ0NOzXr5+enp4gXUgkkjVr1nh6esrlcmdn5w0bNrzrlXl5eUFBQVOnTu3UqVP//v03bNhw+/ZtAwODvn37rl279vr167///vucOXNKL1Tu379foVB4eXk5Ojr6+vrOnj1bkDH/AxWkpb6pzJGA/HCPGzduiNqpg4MDER04cEDUXkrjIaiRkRF/OG3aNCLaunWrqJ1u3ryZiFxcXETtRTPxwzNLFwUWUHZ2trGxsUQiefDgAWPswoULRFS3bt2K1Wrn5zXXqFGDH5hTVFTEVx07d+6MQyn/RdSxh5ARMWwhVAvlAe45OTmMsbCwMIlEYmBgUHqXuCDK7O578OCBnp6elpZWRESEIO27uroS0ZdffilIax/u6NGj/Lr3+vVrFXcNpS1axIiYjQ0To1p+dHQ0PzEFhyp9/ORyOc8UVfF57vxSYGtrq4K+fvrpJ56+4e7uXvr5hw8fbtmypW/fvvxznBs+fLiHh0doaGiZkqcfA6SMqq5H1azdqX4hVHP06dOHRMsaPXjwYE5OzhdffNGgQQP6e7XQycmJH1hfXitWrBg5cmRaWtqQIUMyMzN1dHQOHTpkbW195cqVKVOmCDx0AE3y4sULb2/vR48e8aSP+Pj4sWPHjh079rvvvisoKKhwszk5OYsWLSIiT09PIyMjhULBVwvnzZtXepe4IHr37j1s2DBljw0aNHBxcVH2WMnGY2Jitm/frq2tzVcgVWn48OFfffVVWloar85VYYWFhc+fP9+wYUNKSgoRFRcX+/v79+/fnwfSaWlpkydPHjt27MyZMyvzE/9UPX1KmzaRREJeXiREmnNZLVq0mDx5cklJycyZM4VvHQR1+/btjIwMa2trPp+uMqpclpw2bZqvr69UKl2zZg0/KGLZsmXt27dv0KABP+5CLpfb2Nh4eHhERkYePXp02bJl3bt319HRUcHYyuUjShn99CIltcS9/B6FJ6mCsETdRsgjQJ5BnpOTc/jwYYlEwitZVYBEItmzZ0/Lli1jYmKcnJwUCoWpqenRo0eNjIz8/Pz4MjIAVMCTJ0/CwsL27dvHL7aJiYnr16/fv3+/kZERL/JcMatXr05OTu7cufO4ceOIaM+ePdeuXatTp46bm5tgQy9lw4YNenp6vr6+fGvx0qVLZTJZeHi4v79/JVuePXt2cXHx1KlTy1uqQRCbNm2SSqU//fTTnTt3KtxIQUEBP+41Pj6eiAoLC6tVq9azZ0/+Ie7l5WVra7t///7atWvzE0GgtNmzKS+Pxo8n8e7Gf/zxxxo1apw5c+a3334Tqw8Qgro2EIaGhqqy33Hjxvn7++vp6e3cubNjx47Lly+/fv161apVHRwcfv3115cvX0ZGRi5btszGxkY146kYqVp6fevS2ae6QvjJb5XUHLa2thKJ5MKFC4WFhUKloXPx8fGXL182Njbmx3YFBATk5OT06tWrYcOGFW7T2Ng4KCioQ4cOJ06cWL58+fLly9u0aePn5zdixIg5c+Y0btx4wIABwr0DAE1Rr1694cOHJycnm5mZubm5rVu3LiMj49KlS0lJSS1bthw5cmReXt5bv9HU1JQXCXiTXC6Pjo6WSCSbN2/mSQGdOnXq06fPpEmTRJrd4+VekpOT+cx9lSpVVqxYMWXKlGnTpm3cuLHCzebm5t67d69atWrLly8XbrDl0Lx5c2dn559++ql3796fffbZu15Wv359/gH9Vlu2bDExMRkzZky9evV8fX3btGnTr1+/u3fv8q9eu3bthx9+IKK+ffv+/PPPkyZNEvxd/HtdupR08mQdIyP68UcRe6lRo8bixYtnz549c+ZMW1tbYT+OQUBqCQhzc3Nv3rwplUo7deqksk6//vrradOm/fe//61Ro4aTk5OdnV2PHj0+wmXA91BPQFg6aJHL5fn5+VpaWoaGhqJ2qiEpozh2Qjzm5uatW7e+detWeHh4ZQqBvmnPnj2MsTFjxvAfXOnVwsqoX7/+gQMHBgwYsHLlyubNmzs4OAwfPnzx4sUrV64cN25cREREZQJOAM0kk8n4xA0R8WDg1atX4eHh2traWlpat27d4iuHb6pbt+61a9fe1WytWrXy8/ObNm3KH7Zs2ZKfeiz08P/fjz/+WDojvXPnzlKplIjeM8gPYWZmZmZmxo+8J6KSkpI9e/aMHz/ewMCgMs2+x7Fjxz7//HNehoeIPv/8c11d3devX79+/fpd35KXl5eWlvaer/KTe4ioT58+ZT61c3Nz+XsxNDR8189aM8nlcheXwTJZzZkz99SpI25hKhcXl927d9+9e3fbtm0qKrkB5RcWFkZEPXr0UGWnV65cKS4u7tChg4rvhFNTU+Vy+YoVK2bMmKHKfoWinoCwdFoj3z3Py2mI2qmGpIzi2AlRffXVV7du3QoJCREwIJTL5b/++iv9HQHev38/LCzM2Nh4xIgRlW/c1tZ27dq1s2fPnjhxYsOGDW1sbJYvXx4bG3vo0KHBgwdfuXKlatWqle8FQDM1a9aMiBo1ajRv3rwdO3YEBgYeOXJELpe/9cUSiaSoqOhdTbm4uERERKxatYofOKH8FsHH/K7G586dW1JSMmDAgMpszcrLyxs9enRsbKy/v//o0aOJaPz48f7+/ikpKbzSleCePXvm6OioUChiY2Pr1auXn5+/atWqoqKiRYsWDRky5F3fpaenp1Ao3vXVRo0aKf9saVk2sKlTp87Tp0+bNm365MmTevXqVf4tfDJ+/vnnmzdv1q1bd8qU6mL3xU9M6du37/Lly8eOHVu7dm2xe4TyevjwYVJSkpmZWZMmTZRP/vLLLwMGDJDJZOL1y/NFVRCFbtu27eDBg7Nmzfr6669JffmxglFLKRuebrFp0ybGWFJSEhFZWFiI3SkPBTMzM8XuSIlvxrC3t+cPeWGAhw8fitppv379iOj06dOi9qKx+KnHNjY2ArbJd0E0btyYFxRdsGABEU2ePFnALr799lsisrKyevnyJWMsOzu7VatWRDR06FC5GGXgQCCoMvoxUygUK1as2Llz565du8LCwoYNG3bt2rUKtxYVFaWlpaWrqxsXFyfgID/QiRMniKh69eqvXr2qZFP8SK46derw2d5Lly7xWqlPnjwRYqRl8S2Xys9Zfsri559/LtSVLSAg4O7duxEREfx46Pv37//222/jx48/c+aMnZ3drVu3BOnlE5Cenl6zZk0iOnTokMo6HThwIBE5OzurrEf4EAkJCd7e3p06ddLT02vVqpXy+X379kkkkgYNGoh6J8zr/x09elS8LjieKrJ37172dyxTtWrVkpISsfsViXoCwsmTJxPRzz//zBgrKCiIjIy8fPmyqD0qFAotLS2JRKLKH9Xu3buJaOLEifwhT6Gp/Mft+3Xr1o2IQkNDRe1FY+Xn5xsYGGhpafHIShAjR44kIk9PT8aYXC6vW7cuEV26dEmo9hljhYWFfLase/fuhYWFjLFHjx6ZmZkR0ZIlSwTsCIRVOiBctmyZoaHh2rVrRe3x4kX23Xds/35RO/l0XL16tbi4+MSJE9u3b79582YlW+MJqEOGDBFkbB+usLCwcePGRLR58+bKtyaXyzt06EBEHh4e/Bl+iRs/fnzlGy/j8uXLEolEX1//0aNHjLHExESeeXThwgWhurh//35qauqxY8f8/Pz8/Pz4yRxXrlz5+eef7927J1QvnwC+sNy9e/eKHZVUMffv3+cnplRmLgYEUVxcfP78+blz5/KLCcez1WbPns1/K9LS0jp37kxEMpns9u3bIg2DpxympKSI0b6SQqGoVasWESUkJDDGDhw4QEQDBw4UtVNRqScgHDVqFIl/JGDpUz54uqahoaGoPZZR5khAvu85Pz9f1E55YTexD3XUZLzW6MGDBwVp7fXr13p6elKplB8IdurUqdKrhQJKTk62sLCoWbNmTEwMfyYkJERbW3vWrFnCdgQCKh0QqsDQoczb+68//31MLKhOSkoKT+FWcYrHmjVriKhZs2ZCHY1V5gTFp0+fGhoaSiQSYWcqFQpFx44diWjx4sX8GZ6kOnr0aAF7+fTExsaWfvjq1SueOXXt2rXgdzt9+nTgu0VHR+vo6GhpaUVGRqr47fANhN26dRPkQ7NA8dfFdu/rvZVvTROkp6cfOHBg7Nix1av/f56wqakpzxU/cOCAvr4+ETk6OhYXFzPGcnJybG1tiahGjRpXrlwRfDy8bHKTJk0Eb7mM2NhYIqpduzZ/yPcN/vjjj2L3Kx41BIRFRUVt27YlIh8fH5G6uH37tr29vZOTk/KZ58+fE5G5ublIPb7VqlWriGjhwoWMMb51RCqVit2ptbU1EfHDzUEMfIePUCmdfNZg0KBB/KG9vT0R/ec//xGk8TKioqJKn3AdHR1NRNWqVcNR9R8tFQeE7duzTp1YcjJjjDVrppo+4X8IHpv9I5Gi0DKxGd9A2K5dOwFz1HnxLUtLy+zsbMZYeHh46SgU3pSfn3/o0KEVK1acPHmSMVZUVDRnzpyuXbvu3LmTMTZkyBDZuylr9rzVV199RURT1ZFrnpmZyTcQBgQEVKadH5//2DG246AHg4Y8GJJRktHqXqt//h4N9uTJEz8/vwkTJtSrV09Znc7a2pqfvFf68nXu3Dm+Tujg4MCfLygoGDZsGE+wFDydbcOGDUT07bffCtvsm37++WciGjVqFH/Ypk0bIrp48aLY/YpHpUVlFArFkSNHFi1adP/+fVNT0++//z49Pd3FxUVLuLNL7969u3Tp0mPHjjHGqlWrlpGRUa1aNVJT7c3StXPy8/PNzMxUUBwZVUbFxlcI//jjD0FaK11QNC0tLTAwUEtLi++KEdznn3/+ZtdjxoxBze6PU0ZGBq97kZaWJuoW/NKWLKHZs+nAAdX0BmXNnDlz9+7dMTExO3bscHFxUUGPCxcuzMzMHDx4cP/+/QVsdt26dUFBQQcPHpw2bVrPnj0XLFjg6+sbFRXl5+dX4eNVS8vJyVm0aBEReXp6GhsbKxSKH374gTE2b948vl0f3qSrqyuRSGJjY/mPQEdHZ+nSpYGBgfn5+UTUoUMHfr/0Voyxli1bvvVLL1++DA4ONjExWbZsmTgDfx8TE5Ply5dPnTp19uzZXbp0eVcxW4mxhGm/vWavtkT7Zt7NkOyQsMZhUon0cdFjE20TMYf8kSouLt67d+/EiROV1YbXr19PRCYmJjz4kcvlERERZ86cCQ4OTkhI4N+lo6MzcODAzz77bPDgwQ0aNHiz2S+//PLs2bMDBgzw9/fPzs4+fPiwgYHBoUOHJk+e7Ovr269fvyNHjgh48eF1TVVQ2aV0CZmsrKzo6Gg9PT2eLf9vpZq4Uy6X+/v784JsRNSoUaMuXbrwP/fs2VOQPfSPHj1ydnbW1tYmIj09PWdn5+fPnyu/umPHDiKqU6eO2BmbpX3//fdEtGXLFpX1yBjjV8Pc3FxVdqpRFAoFn49U5l5WWFRUFBGZmpryJaAtW7aQqnLQi4uL+bvgW2LgoyKXy318fMzNzbW1tXV1datVq+bp6ck3f4qqfXtWWMjs7VlwMFYI1eb48eMkUH2XfyRqJZsy9V38/PyIyNzcXJDSbrz4VufOnXmiYJlKNvAuwcHBjx8/jo+P37FjB5869/Pz4yuEFbZ8+XKJRCJSYsuHKCkpadSo0ftP7Gzh34Ku01v/axDdwCPZw/uVd+k2NXCF8Jdffhk/fvy6dev4Op6Pj8+vv/7Kv/Tw4cMZM2Y0a9ZMuWLcrFmzGTNmnDhx4gP/OV+/fp3XHOrZsyf/lpKSkilTphCRrq6uUIWIlPv6VJAlx6PfqKgo9vdmn+7du4vdqahUERCGhIS0a9eO/5u0srLy9vbmmcSBgYG8oLO+vr6Hh0eFb3eePHni7OzMpzR0dXWdnZ35diwuPDy8d+/e9PeiWYMGDfjhTirwzTffENEvv/wiXhfZ2dmlw5KSkhIi0tLSUuWubg3EV/AqH+rzFQA+sc0Y4yt4qinRxm86W7RooYK+oFz+/PNPnlRPRB07duRlooioefPmf/zxhxg95uWx5cvZwYN/BYSJiaxTJ9aoEbt1izk7M5F35sNb8GLRM2bMELujnj17EpGbm5sYjefl5fHFul27djHGFAoFn01fsGBBJVtOSEjQ19eXSCR8D1JWVhZfQt+PakjlV/mAkH+QzZs3T6gh/aPk5ORhw4bdvXuXP8zLy7O0tJRKpSYmJjXeofOxzjVu1Xjrf+1j2s9LmueT+j+bmDQwILxw4cLBgwe9vb2fP39+7Nix9evXb9++PSQkJCMj49mzZxYWFjKZrHPnzosXL7548WIFctpjYmLq1KlDRDY2Nny2S6FQzJ07l4i0tbV3795d+bdQZl+fePg2NBMTE16oktcAnz9/vtj9ikrcgPDMmTPK9dO6det6e3uX+R1KT093dnbmpyG1bt366tWr5Wo/JSXF3d2d71jV0dFxdHTk1X64q1ev8o9VIqpRo8b06dObN29ORBKJZOrUqRkZGcK8yXeIiYnhn4Xff/+9GO0XFhZ6e3vXrl27ZcuWyl0ZGRkZ/HdUjB5Bae/evUTUpUuXyMjImJiYp0+fpqWllXdvTGFhIa/zySsA3b59u/RqodiGDh1KRBs2bFBBX/CBEhMTHR0d+fWwTp06Pj4+fGYnJCSEX7uIyNbWVnkbJAh/f2ZlxYhYnTrMxobxebl165i2NvvqK0bEqlZl69cz8Zcn4f/du3dPR0dHW1tb1FMN9u/fT0S1atUS79OwTBeRkZF8QTI+Pr4yzfJq78oyAW5ubvyCjJnQClAGhNOmTev2br169Wr8DlZWVoL8WD8cT3kdMWIEf7hixQoiatu2bYXLyPum+n735LvSz2hgQMj/+SgUitTU1EuXLoWGhu7atcvX17dv377p6ekHDx6s/FkRjx8/5od8NmvWLCkpiT/p6enJ78w3btxYyfZ5poDyEBrx8FPl+vfvzx/yKu58a+6/l1gBYURExMiRI3lacM2aNT09Pd+Tq3nhwgVeplYqlbq6uvIN4u/36tUrd3d3nh6ppaVlb29f+koUHR1tb2/P76uMjY3d3d3T09MZY0VFRZ6ennzHVO3atQ8fPizImy0jISHBycmJJ68aGhoSUb9+/XhRbEEUFhZu3brVwsKC3yB27dr1xYsX/EsqO9RRw50/f97ExOTNM6MNDAzMzMysra3btm3brVu3r776yt7eftKkSS4uLvPnz/f09Ny6deuePXsCAgJCQkJ4zaF27drxNhUKxcWLF/ft26eC8aekpOjo6Eil0tJp1aBGubm5Hh4e/IJmaGjo4eGRl5dX+gVFRUVeXl68+IeOjo6rq2vlb+KjoljPnoyIEbHPP2cXLrC9exm/oSoqYhs2sPh4Zm//1wsaNWKVK9kA5cNXXXr37i1S+2WW78RTZhGS38oPHz68wg2ePXuWf7LzVKAHDx7wgweQ/V4uPGX0v//977Bhw+zs7Hx9fStTVIZnzVTmx/rhykwrJCUl8WTRP//8s8JtFimKesb1nJM0xzfV1zXRtUBRoIEB4bssXLgwODhYqNaeP3/Oi+HXr19fmdi5detWXkzE3d29Mo1PmDCB/vf4nPj4eHd3d8HPW+bXZ34scEFBAU9YSE1NFbYXFRM+IIyMjBw1ahS/grRs2fK///3vh+xny8vLc3d350GUtbV1SEjIu16Zmprq4eFhYmLCZxTs7OxKn/4UExPj6OjI2zEyMnJ1dX3zKJL79+9/8cUX/CpmZ2ennKWovMTERFdXVx5w8hXLrVu38lUgQ8P/Y+++45o+3jiAX9igoDhARBFHVcQiwwHugdaBq4pWLNaJq+IWN62gxWpt3FK0FlcV60IFFdEW3IKKCgKCIIKALNkj435/nM0vRURGBiSf96uvvpKQfO8i4Zvvc/fcczpeXl613AVRIBD4+fmJlu1++eWX4mW1srKyFi9e3KBBg8aNG1d3rhWqLicnh41fmJqa2tjYdOrUqVWrVuIFl6uuYcOG8+fPl/1bYCvFx40bJ/umoRwhFfoF+rHkeQ6HM3Xq1Ddv3nzqyZmZma6uruz81rRpUy6XW7NTSmYmdXWlqqqUENq0KeVyaSWHCQqiXbt+CAuHDKHPntWgQai27Oxs9t3BFnpJnMQ3cP+UcssU09LS2Nd3zfKf+Xw+u5oUlXcfM2YMIWTmzJmS7LTSSE1NTUxMTExMfPfuXUpKStynxcbGRn9aampqbX6tVScUClkWPavfTv9dwVH7SSGekHcj78bxrOORxZGU0pt5N2t5wPorPT396tWr3t7e4eHhcXFxQ4YMkeCUBv3vboTP/v1GOXr0KFv5tWjRoupO9ScmJnp7ezs4OGhpaWlqaoqyT3k8XufOnQkhTk5Okq3bzBb4sDEIVl3myy/r/QiCJAPCqKgoFxcXlmf8xRdfeHp6Vnft+OPHj21sbNi1sqOjY2Zm5sfPuXTpEnuCvb19eHi46PHExMRyKwnfsurpFREIBLt27dLV1SWEzO/Zk/7+O61dqsm7d+9EyatsxvLly5fsR+np6c7OzqzPVlZWbAVqdbFQULTdZ5cuXfz8/ER/M7m5uT/88AObPWAXi2x/OSyvlziBQDBq1ChCSLdu3T4e6SgoKEhPT4+Pjw8PDw8JCQkMDPTz8/Px8eFyuZs3b3Zzc5s/f/60adMmTJhgb2/fqVMnQkjjxo2lUc6hcuyK6sKFCzJuF8oJKwzrE9On09lOqqqqNjY2VSzAHR4ezhJU2CmlWnWuS0tLt28XNmpECaEaGnT5clqVicayMvrrr7RxY0oI1dUt9fTcLu2Ue6CU7t69m42QSrwWmjQ2cK/EzJkzCSFjxoxhd7ds2cK+xVg1gWrZu3cvIaRt27bs3+T69euEEF1d3Uq+7kE2avNrrbojR44QsdJEbK8RLS0tyUYsSo6VjY2Li9u0adO6desePnwo8Sby8/NFuxGKJjDOnz+vqam5YMGCqhxBIBDcvXt37dq17HqGYdOMOjo6V65cYU8LCQlhQxWjRo0ql3dTY7m5uazeG7sI/OmnnwghVex2XVZZQMjj8cQnoDIyMrhc7tOnT9ndkydPrlmzxt/fn1IaExMjCgXbt2/v6elZ48sFHo/n5eXFIitDQ8MK9ypcunSp+AxYUlKS+Lyci4tLFSf9Xr9+PX706KKOHSkh1N6eiq0/rIaMjHgPD5YaqqKiMmXKlHK7vjKXL182MTFhPXRzc6v6OjGhUOjv70p3aQoAACAASURBVM92OGFfhN7e3qJpgYKCAi6Xy6oqsSA5JCTE3d2dbV9mamoq7eE6ZcNK2zVt2jS+Zp8WMUKhcPLkyYSQTp06sZRm2bh//z4hxMDAQGYbncHH3pa9/S7xO5VwFRJOjJ4anbx5sroTNf7+/qIkLgcHh6pcD7G1iH36HGMnvOfPq9fnjAw6dy51cNjJSsz5+PhI9coP+Hz+l19+SaSwMamMN3Avt9VhcXEx2y+3urVM8vLy2KzpmTNnKKU8Ho/9+2zdulUq/YbqKC0tZcvD9u7dK6UmCgsL2XXUH3/8QSkVCAQ9e/YkhGzYsEFKLYL0VLgb4aNHjyr/KiwsLPT392cRhygO1NHRcXBw8Pb2fvPmzceVS8PCwliB0wEDBkikxPGVK1cIIb1792Z32SSBAlS0+mRA+O7du61bt7q4uJw8eZJSmpubu3LlylmzZrEQ8fjx44sXL05ISJg2bVpgYKCFhYWRkVG7du02bdokkSTauLg4VhqUXet8Kofq43m5mpSa9fOjzZtTQqi2NvXyqix3qpy8POrlRRs1ooQMtLS0t7evfPavoKDAzc2NDWB88cUXN2/e/GwLQUFBoilTExMTUYFW+m9RGdHuZH369Llx44bohREREaJyPo6OjjIoX64Mzp49y+Fw1NTUxP+pa6OoqIj9focNG1bLdOKqmzdvHiFkxYoVsmkOyikTlnHTuXpP9Eg4UX+k7vrGNZdfw6+ooqIitgkbIURbW9vNze1TC7CjoqJEFbb69bOvzQ7ksbGxTk5ObFFAv379JPW3ABUSrZcrNwPWsWNHnSoT1d5g5LKB+9atWwkhZmZmbBzq/Pnzbm5ueXl51T1OQEDAnDlz2G22T0/79u1lU4gLPuvs2bNszqfC9K7aYztP2tjYsJiBbaVrbGyMZKh6qqysbNKkSSyiC6z0a+n169csKVR822RTU1MXFxd/f3/xM0CFlUujoqJYgdPu3bvX/sO5fv168m9ZXaFQ2KRJE0JIUlJSLQ8rd5XNEG7fvn3btm0JCQmPHz9mc267d+9mAeGkSZPYct7bt29///33hw4dWrdunai0iUQIhUJfX1/2D92oUSMulys+bJCZmenm5iaal3N0dKxV3l12NnVx+bBKxtKShoV95vn5+XTzZqqv/+ElI0eWiCWvVu727dtsP0YOh+Pi4vKpb8SIiIhevXqxD32rVq32798v2pajrKzM29ubLToihPTq1YvN05bD4/G4XC7LC/rUXCtUXVRUFEs84HK5EjxsYmIim+Ct5VrqKiouLmbLHUVT/SANfCH/aNbRdSnrjmUdE1CBc4JzbEkspdT/vX/75+3Z5lcOcQ5xJRLYKyk5OVlUm9TY2FhUm5TJyclxc3NjKQMS3M/w2rVrtra2LCycNm1aYmIin88PCAhISkpiJ+qCggIPDw9nZ+edO3dKe5WaYmMFgWfMmCH+ILu4qaJRo0aJXigQCNhYobu7uyzfRWlpKVvyIKmNebOzs5s2bUqQ+l7HDB482MpqyLp1kh9reP36tY6ODofDYbNJ+fn5bI5ItFce1EeV7EYoEAjCwsLc3d1tbGxENfxUVFRsbGzc3d3DKr1Q/7hyaUJCQocOHQghXbp0Ed+argYGDBhACGEX3qw+fJs2bWpzwDqisoCwsLCwuLhYIBBcu3aNhVuigHDw4MFs4jUuLm7SpEnS69/bt2+//vpr9jno27fvixcv8vLyvLy8WP4JKyrDqvZLQGDgh/rramrUzY1WuGyjtJR6e9MWLT6Egn360CpM9JXDip2yS7SWLVtWWDMgMTFRU1OzWbNmXl5eorxntpKQfaYJIV27dhVfSVih2NhYUQWdJUuWoKpkzWRnZ7N/9m+//VbiBw8NDWUfBhmUGD1+/DghpGfPntJuSMk5vnJck7LmXsG9zambb+TdmBg/MeB9wIiXI1go2Dmyc0BugGRbvH//Plumz36/d+/eZbvbs+EGFRUVZ2fnjyts1UZJScnOnTs7dOjAKhB6e3vPmzdv8+bN7Mvi3LlzV69eLSsrmz9/fkCAhN+sUomPj2dVNMUXShQVFRVUmfjwuRw3cGd7n+rr60skY2XhwoVEmlVYoWaePctVVaVqalSim+NQSumECRMIIc7Ozuzu6tWrCSG2trbYa6S+EwqFy5cvF83pFRUVVZIUWvXVwh9XLk1NTWVJ5m3btq3Nqp+AgIDly5ezdMh9+/ZJ6cpQ9qpXVEYUEH799ddsejQsLGzu3LlS6ZqYkydPGhoaEkI0NTVZfhQb9Qyv8rxcVeXn00WLqIoKJYQeO0YfPKAjR9KvvqIjRtD79+nt27Rlyw+hYO/eNDi4Nk09ffqUpb8TQhwdHd+9e1fuCVevXhV9Z7NQkJUhIYSYmZn5+vpWcdydzbW2bdvWwsKiY8eOPj4+GLCvFj6fP3fu3C+//LJ79+4Sr+7AsEoJWlpa0i4Py5Zx79+/X6qtKLn4knjrF9bij0yMn7ggaQEJJ/pP9LnpXJ5QKkvvBAKBj48PO1WqqKi0aNGCnS4GDRokve3s0tLSVq5cOXz48Hfv3nG5XDbvdPjwYfbToqKiadOmSaMmgVJxc3MjkthnT+4buLO85YULF9byOJGRkWpqaqqqqsh0qIMWLPhQkViCQkNDORyOjo7O69evKaXx8fGs0D8KqiuMH374gU3zsPFxpm3btq6urteuXatZYsvHlUuzsrJY8p2RkdHz6i6jr4iTk5PCXFNxKKWfTTiJjo5u0qSJgYHBnj17DA0NHR0d9+zZU1xcvGLFijVr1lhbW7MkYKl6//79ypUrT548KRAIrK2tN2/ezCZtpeLuXfLHH+Tnn0nv3uTKFdK6NXnzhnz1Fbl0iVhaElNTsmEDcXSsfTtCofDgwYPLly8vKCjQ19f38vJycXH5+GnXr19ftWrV48ePCSGmpqZr1qyZNWsWqyZadWlpaWvXrmVrYW1tbbdv386W9cNneXp67tu3r3Xr1n/99Vfr1q2l1Mq8efPYotCwsDDxgTEJSk5ONjU1VVdXT01Nbdy4sTSaAEJIcH7wH1l/HDU9KnrE8ZXjuhbr/N77rTBY0UStiVRbX7VqVUhIyJMnT+zs7CIjIy0tLb///ntWml96SktLNTU1BQIBIURVVfXUqVOTJ09+8eLFli1bysrKDh48yEo6Q83k5+d36tQpNTV1/PjxbIS7Zq5fv37nzh07O7vbt29/vI2qDLx48aJbt24CgWDOnDls5KJm/Pz8oqOjFy1axJYRQp2SnU06diRZWeTCBSKRE49AILCxsYmIiPD09GTLCMePH3/+/Pnp06ezZYSgGGbNmnXhwoXs7Gxra2sHB4fRo0dbW1vX8kx14cKFyZMnl5aWuri47N+/X0VFpaCgYOzYsTdu3DAwMLh69aqlpWVtjt+mTZukpKTnz5+bm5vX5jh1QlWiRn9//+fPny9ZsmTSpEmTJk3y9vYuKyvz8PD45ptvfv31V5lNN2VkZBBCGjduLJvmaGAgFZ/8nDePXr5MX7yo5QYVH3v16tWwYcPYr2PEiBHiC/2DgoK6d+/OflSuqEzNXLt2zdramqV4bd++HXUmP+vSpUstW7Y0MTG5c+eOVBsqKytjyb12dnZSqpGwadMmQoiTk5M0Dg4iT4qefPXyK/FHJsZPjC6uoPiwNLAyjE+fPs3Kytq8eTMR2xNcLvbv3y+9qoPKY8aMGew3W0uNGjViFRrlxdrauvbDas2aNdPV1WWFDKAO2rmTEkLbt6cS+Srbv38/IaR169asyr+o0lItV4JBXbN48WJCyOrVqyV72ICAAG1tbULIN998wy56CwsLhw8fTghp3Ljx7du3a3zk6OhoQoi+vr5ipN2pVeXkO3r0aELIr7/+Kv4gK7MjSwUFBYQQtnpQFnJyiHhbjRuTnBzSubPE22nbtu3Vq1dPnz69YMGCwMDALl26bNy40c7ObuPGjf/88w8hxMDAYNmyZYsXL2b1VGtj6NChvXr18vT0PH78+C+//BIQELB9+3a2w2ZMTIwoJZUQkp6eXlBQ0K5dO7kMJNcRUVFRixcvppR6eHjY2dlJtS11dfW//vqLrf6aO3fuH3/8UZujZWdnR0VFdevWTTQzQyn19fUlhMyYMaP2vYVKmGuZJ5cl3yu8Z9vAtlhYLCAC2fehRYsWTZo0Ydkysnf27Fk1NTUDAwMrK6u4uDhRfSyomfz8/CtXrmRmZk6cOLFr1641Ps7169dv3brl7e09bdo0uZzYo6Kinj59KhQK58+fX5sZwlOnTr148WLnzp179uyRYPdAUhYsID4+5PlzsmsXWbmyVod6//79xo0bCSE7duzQ0dERCARLly4lhKxdu1ZKqTQgL6GhoYQQUUFsSRkxYsTVq1cdHBxOnjyZn59/+vRpHR2dCxcuODk5nTlzZtiwYZcvX65WymFGRkZgYOClS5cCAgKMjY0FAkFOTg6rcVW/yTsirYZnz54RQszNzWXUXng4HTHi/3dHjKASX7L4X6mpqY7/zUQ1MDDYsWOHpDbTFBcaGsqKBLZu3drDw8Pf39/Ly0tUrs3Ly8vZ2Xn58uVTp05VjJGPGsjJyWH/RCwmlI0nT56wwrDVqsWXnZ0dGhrq7e3t6upqb28vSgYW3xLg5s2bhJBWrVrJbH8LZRZbEjshfsKwl8NGxY16UvRkZfLKxFIZlfhn80hsTTKr9S/7GUIfH5+cnBwPD4/Zs2f7+Pig6kMtrVy5kkh0DaG8CjOyXJhFixbV8jhRUVHq6uqqqqrSWxwLtXT9OiWE6urSKhcBqdiRI0c4HM6AAQPYXTYE0K5dOymt5wd5yc/PV1NTU1dXZ/PAEie+GyEr78/n82fNmtW6deuq7OJLKX306NGmTZt69OghGk3jcDisbLuFhYUClGysTwHhnTt3CCG9evWSXZPjx9NNm+jt29TDg44bJ5s2z507p6mpqaOj4+np+aldxSSiuLjY09OzVatWq1atCgsLGz169M2bN9kFR0REBLsxbty4Kv6pKBgejzdx4kQjIyMHBweJlOmvujNnzrDdDoMrqlokEAji4+MvXbr0888/z5w5s1evXhXOmevq6vbo0SMoKEj0wmnTphFCNm7cKMO3AnJQFwJCkKC4uDhWZfTBgwe1P9rvv/9O5LR1G9ukTl9fXyKb1C1atIigymjdNno0JYTOmlXb49y6dYvV/8jOzmbnt7Nnz0qgf1CXXL16lRBia2srvSaioqLYhm2i3QiFQmHlgVxxcXFQUJCrq6t4oru2tra9vT2Xy33z5o2kKpfWBfUpILx27RohxN7eXnZN8vn01Cn644/01KlqbFhfO4WFhewDJ5vmnjx5kpubGxoamp6eHhwcfOvWLQ8PD/YjgUAwYMAAqQalddaGDRuMjIy6desml1GftWvXEkKaNGny8uVL8cefPXvGUuHLadq0ab9+/VxcXHbs2HH16lVWh01cfn5+w4YNORxOXJwENr6DugwBoYJhSzZm1f6ymlJKqUAgYNWtN2zYIJEDVlFpaekXX3xBCNmzZ49EDiiKDSrctwnqgthYamdHQ0MldkCMAiiwDRs2yODbqoq7EWZkZPj6+jo6OrLdp0Upe87Ozn5+fuWuikWVS01MTGq1I7q81aeAkI0vjpPVTJ28pKWlEUKaN28u745QDw8P0Z6eiqrcjljFxcXFxcVCoXDDhg1t2rSpfOdT6REIBOwq0MzMjG34yeTn57MUhT59+ri4uHC53KCgoMoHpfLy8h48eMDWDQ4aNEj6fQc5Q0CoSK5fv84m/Ku++9Zn3blzh8PhaGtryzL746effmIXYbWsiyYO2YN1kJXV/2/360dDQ2nLljQ5mVJKb96ktSkXgjxhxcaK6okWLklPJXN6z58/9/Ly6tOnD9vAkOnSpYubm1toaGglGfv5+fmDBw9mQaPEtkaXOfmUHKiZ/Px8QohoH0JFxWrnyLdKO5/P9/DwUFVVXbJkiRy7IW0PHjwIDQ0dOHCgjY0NIWT79u2PHj0SCATm5uabNm2aOXOmqampXDqmoqJy/Pjx3r17P3/+fNq0aWfPnmWnp4YNG+bm5lby2cjJyXn16lVkZGRUVBT7f2JiolAoJISMGDEC5WQA6hE+n8/qZ6xfv56t/ZMIOzu7KVOmnDhxws3N7dSpU5I6bCXS09NZQLhjxw4JFjpiW/U8e/aMy+WybcpB7vLz/3ObzyeGhmTpUuLnR3g8Ulxc8yMvW7aMx+MtWLDAwsKi9v2EOoXH4z148IDD4Ui7eh8hpEWLFn///ffIkSPv378/aNCgS5cupaenX7x48dy5c2/evGHP0dbW7tOnj4ODw4QJE1q1avXZYzZs2PDixYsTJky4cuXKoEGDLl++3Lt3bym/D8mrTwFhXYiUZEC+ce/du3dv3rz55MmT1NTU4cOHb9myZfLkyWyGXfFkZGRcuXJFtK9Xr169VqxYQQixtrbesGGDvKJBRldX19/fv2fPnhcuXNi0aRPbs5X89/OfnJwcHR394sWLqKio6OjoyMhItjWLOE1Nzc6dO3fu3NnV1bU+nqEAlNahQ4eePXvWoUMHVo1dgrZu3Xr+/Hk/P7/vv/++X79+kj34x3744Ye8vLyxY8dKtn6gqqrqjh07hg4dumXLlunTp7do0UKCB4eaEQpJcPCH2wUFhBDSvTspKSEBAURdnRBCxo0jb99W/NpWrd4nJw+r8EeFhYVRUVH6+vo//vij5DsN8hYeHl5UVNSlSxdW90XamjRpcvXq1dGjR4eGhtrZ2bGFWoQQY2PjUaNGjR49esiQIRUuz6mEeOXSr776KjAwsG/fvlLouxTVp4BQU1Nz6NCh8r1MlwH5xr22trY9e/aMjo5me0wTQgwMDOTSExmglP72229xcXE3b96MiYmZN29eWFjY1atXHR0d68JmG23btv3zzz9HjBixadOmLl269O3bVzTv9+rVq4iIiArDv/bt25ubm3fp0oX9v3PnzqqqqnLpP8hFz549CwsL2ZSyvr7+gAEDZPMVC5KVk5Ozb9++0aNHz549W1NTU7IHb9Wq1caNG2/fvr158+bLly9L9RQRERHx999/Ozg4bN++XeIHt7e3nzNnTlJS0tatW8vtjAVyIRSSx48/3C4q+nDj55/JiBFk0yZCCHnyhLx+XfFrCwvVoqIefurICxcu7N+/v0R244S6hm04IYPBKZFGjRpduXJlxYoVRUVFDx8+HD16tIODQ58+fWpz7aehoXHq1CkXF5eUlBRnZ+e9e/eOHDlSgn2WtvoUEGZnZz9//pztJqnAWEAorxlCDoejqqpqbm4ul9ZlzMHBgRDStm1bHo8nmj3T19e/d++eUCgUTyKXF3t7+61bty5fvnzWrFnsgyGuadOm5ubmnTt3NjMzY7GfiYmJXPoJdcfr16+zs7PZbR6PFxsba29vL98uQQ1s27YtIyOjU6dOY8aMkcbxFy9efPbs2Tdv3pw8eXLq1KnSaIIQQil1d3fPzc21s7OTUqbJunXrBgwYwLLr2Z66IEdqamTFig+3jx//cKNFCzJzJtmxg3TrRi5cIKWlFb9WVVVVILj/qSObmJhgElhR3bp1ixAi4yk1HR2dffv2CQQCCY6IqaqqHjx40N3d/enTp/Pnz+dyuePHj5fUwaWtPgWEbFaX7dKmwFjKqMJnxtYp6urqhBCBQNC9e/fu3bsHBgamp6dLcNFObSxbtkxFReXhw4eBgYGieT9zc/N27dqJ9hsEAEUSGxt77NgxVVVV6SXIaWlprVu3bt68eVu3bh09erR4MT0JOnfu3IMHD5o3b87qQ0pDmzZt5syZs3fv3o0bN/r7+9eF5A742IIF5PBhQgjp1q2SZ2kT0lNGHYI6g1J69+5dIvOAkJF4fgSHw9m0aZOhoeHmzZu///77rKys2bNnS7YJKalPAaF8p85kRkneZp1y69at2NjYkJAQOzs7Pp+vpqZWp0YilyxZItlBLACoy9zd3fl8/syZM83MzKTXypgxY3x9fe/evcvlcjdu3Cjx45eUlLBaMmvXrpXqEOfixYv/+uuv8PDw8+fP16PxeIUknhfs6Um6dCFz5xJCiKoqOX36PyVnAJgXL15kZGQYGxsr0oqwhQsXamtrb9iwYePGjXl5ecuWLZN3jz5P/klxVackM4Tq6uojR46U6nUAlGNtbT1hwoT9+/d36dLlyy+/PHnyZF0bZkY0CKAkAgMD//nnn0aNGi1fvlzabW3atElVVfXQoUPx8fESP/ju3btTUlK+/PJLR0dHiR9cXMOGDVmVUQ8PjyLRwjWQh7Fj/3971ChiYEBsbD7cbd+eWFrKpVNQp7EFhP3795d3RyRs5syZXC5XTU1t+/btmzdvlnd3Pq8+BYTsRK/wAWF2dvbjx49FNV1ABnR0dBo1aqStrd2vX7/+/ftLvIQDAEBV8Hg8T09PQsiqVav09fWl3Zy5ufk333zD4/Eknpv69u3bAwcOsOwpGazHdnR0tLKySktL27dvn7TbAgAJkssCQtlwdHTcvXu3urr63r1716xZIxQK09PTy8rKRE8oLCyMj4/n8Xhy7KRIfQoIWS6lwgeESjIRCgAA5Rw4cCAhIaFjx47Ozs6yadHNzU1PT+/69es3btyQ4GF//PHH4uLi8ePH9+rVS4KH/RQVFZVNmzZxOJx9+/aJNhMDgLpPgQNCQsjYsWMPHjyopaV1/PjxY8eOHTt27Pbt22lpaYSQkJCQSZMm/f777yNGjEhPT5d3T+tVQKgkkZKSvE0AABDH5/NPnDhBCPHw8JDgBu6Va9as2ZIlSwghEty24e3bt0FBQdra2mvXrpXUMT/LxsZm7NixJSUlBw4ckFmjAFAbKSkpiYmJjRo1UuDi9kOHDj127Nivv/761VdfxcTEPHr0qHHjxoSQVq1aHTt2bPPmzQ4ODoGBgfLuZr0qKqMkkZKSvE0AAKUVGxvbrFmzJk2aiB4pLi6+fv36kSNHwsLCZLkfFyFk1qxZubm5s2fPLigoSExM7Nq1K3v87t27mZmZhJBOnTp17Nixwtfm5ua+ffuWLXp/9OhRSkoKe/zQoUNFRUUtW7aUyTv4YP369Z07d547d25SUpK6urp4pejw8PDXr19bW1srUuEKgPouJCSEENK3b1/FLpTANjZLTU11cXFJTU3V0NBwcHC4dOkS++m7d++6dOki1w4SUr8CQgMDg5KSEoXfjwEBIQDUmJ6eHp/PZ6u2NDU19fT0tLS05N0p+I8rV66EhoYaGBgMGzZMVD9s27Ztf//996pVq6ZMmSLj/qirq7u5uSUkJAQEBCQkJPTv359tfujh4eHk5EQI+dSa9piYmCtXrqSlpfXv33/EiBFFRUW5ubmEkODg4D59+si+2HrLli1dXV1v3bp18+ZNDQ2NIUOGdO/enRCybt06Pp9vZWUVExODgBCg7lDsfNFyjIyMRKNUomgwNDQ0JibGw8NDfv36oD4FhG5ubi1btjQ0NCSE+Pr6ivbpHjVqlCKd4hEQAkCN3blzh924du3arFmzZs2aJfrR9evXORzOkCFD5NQ1+GDIkCEPHz4khJiZmaWnpxsaGkZGRr569cre3l6OvWrbtq2qqqq2tvZXX32Vk5PTsGFDgUDw7bffVvKSTp06BQUFaWpq2tvbZ2dn9+rVq2/fvpTSkydPTpo0SWY9L6d3797sKrN79+7p6enFxcXPnz+/cOGCvPoDAJ+iVAHhxy5fvnz06NFDhw7VhQnSerOG8Nq1a3/++eeJEyciIyMJIWZmZhYWFhYWFufPn1ewgpxKUjsHAKQkPz9/69atFy5cOHbsmOjBp0+f7tq16+DBg3LsGDClpaVTp04dPHgwIWT16tV8Pn/dunWsJop8O9a7d++ZM2eWlZX5+Pi8fPmSz+cvWbJk0qRJ4eHhn3pJ//79nZ2deTzevn37kpOTCSHBwcE9evSQ0k73VcHn88eNG+fg4CAQCDZu3BgdHc3j8WbMmOHo6FgXFuoAAJObmxsZGampqclm8pWHUCicOHHigQMHFi9e3KlTp7179167dk3enao/M4S2trZPnjzh8Xjm5ub3799nhctevXplbGzcvn17efdOktgMITamB4Ca0dXVbdOmTVFR0ZAhQ9LS0rS0tBo2bPjjjz+6u7tvF983GuSkYcOGojP84cOHd+3a1aFDh/fv37979y4xMVEgEMhrtNjCwoLdWLVqFSEkODiYEJKUlLRw4cKLFy9W/pL169ezGz4+Ptu2bZN6Xz9NQ0Ojc+fO7La3t/eVK1dMTU337dtXUFAwfPjwESNGyLFvACCSkJBgZGRkamqqbOsaVFRUjh8/npaWxtYWEkKaNWsm3y6RehQQNmjQYNasWUKhkBBy5coVFhB6e3u7uLjIu2sSNm7cOGtrawMDA3l3BADqK1tb2+HDh3M4nGvXrrVq1SosLGzEiBHiNTagjigsLNTV1dXX17948WJcXFx+fv706dPlnj5UUlKioqKSkZFhbGzM4XBKS0ur8hINDY3o6OimTZuamJjIoJNVUVhY2Llz5wMHDlBK+Xx+SUmJvHsEAB9YWlq+efMmPz9f3h2RA01NzTZt2si7F/9RbwJCVVXVpk2bstvu7u6EkKysrOjo6K1bt8q1XxJWUlJibm4+YMCARo0aEUJ4PN65c+cuXLhw6NAhZRtBAYAaEy2rdnR0TEhIOHr06M8///zgwYOcnJykpKS6c70O06dPP336NLv9008/WVlZ1YVTvZeX1+DBg/fv36+urp6fn1+Vbes3bdo0c+bMvXv3yr6WTCVcXFz++OMPOzu7iRMnlpaWbtmyRd49AoD/UPhSkfUFh1Iq7z5U24sXL8zMzH755Zd27dqNHz9e3t2RpPv37x8/flxHR8fZ2dnc3PzBgwdv377dvXv3+fPn8TcDANX1/v37hIQEVmkmLy/v0qVLO3fuVLYFG1BjfD5fZjsiSpXCvBEAAGmoN0VlxB08Tsw7ogAAIABJREFUeJDP50dERDg4OMi7LxLWtWvX9u3b6+rqmpmZcbncHj16jBs3TltbW979AoB66dKlS/n5+QsXLly4cOGMGTPMzMwQDULVKUwQpTBvBABAGurlDKGSCAoKGjp0KCHEwcHhzz//xAwhAAAAAABIVr2cIVQSLBoEAAAAAACQEgSEddehQ4d4PN7r169LSkqSk5OLi4vl3SMAAAAAAFAoCAjrriZNmuTm5h47dmzQoEFnz55NSUmRd48AAAAAAEChYA0hAAAAAACAksIMIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASgoBIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASgoBIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASgoBIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASgoBIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASgoBIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASgoBIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASgoBIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASgoBIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASgoBIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASgoBIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASgoBIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASgoBIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASgoBIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASgoBIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASgoBIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASgoBIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASgoBIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASgoBIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASgoBIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASgoBIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASgoBIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASgoBIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASgoBIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASgoBIQAAAAAAgJJCQAgAAAAAAKCkEBACAAAAAAAoKQSEAAAAAAAASkpN3h0AAACAasjMzPTz85s0aZKenp6qqqqTkxN7/N27d4cPHzY1NZVr7wAAoJ5BQAgAAFCfPH36NDQ0NC8vb86cOU2bNj116hQhpKCgYPLkySYmJvLuHQAA1DNIGQUAAKhPTE1NJ0+erKOjo62tvXnzZvbgsWPHnJycVFTwtQ4AANWDbw6A/wsKCuLxeOKPJCUlHTt2LC8vT15dkobw8PB3796J7kZGRh44cOD8+fN8Pl+OvQKAKlJRUYmKijIxMdHQ0Bg9ejQhhM/nX7hwYcKECfLuGgAA1D8ICAE+OHHixNmzZ3fv3p2ZmckeEQqFq1atOnLkiHj4VN8FBAQcOXLE19c3OjqaEJKUlLR06dIePXpERUXt2LFD3r0DgM97+fLlnj17zp49q6amZmFhQQi5ePGivb29lpaWvLsmYffu3SspKRHdTU1NvXz5shz7AwCgkBAQAnwwcOBAY2NjfX39Ro0avXjxghDi6+vbu3dvBVuT07dvX2NjYxUVlc6dOz979qygoEBTU9PCwsLGxiY3N1fevQOAz2M5C7q6uiUlJTt37iSEHDlyZNq0afLul4T5+fn5+vr+9ttvb9++JYScOnVq8+bNohRZAACQlHpfVCb2YdB9/4Oq6pqGpmb9HBdpaDeUd4+gvtLU1Jw6dWppaWlxcfG5c+eaNGly/vz5v/76a/78+fLumiSxHDOhUEgIOXbs2NatW/X09IYMGSIUCs+cOSPv3gHA57GAkJUY7dGjR15e3owZM5o3by7vfknYkCFDYmNjGzRo0KJFi7i4uMmTJ0+ePHngwIHy7hcAgKKp9zOEeZmpX3S3/2bd7+qa2rfP7mcPCvi8yl8F8LEXL1706dNnw4YNenp6a9euXbVqVf/+/e/du5eZmfn06VN5905iMjMzhwwZMn36dELI1q1bL126ZGBgEBwcvHDhwoULF8q7dwDweQUFBYQQXV1ddXX13r176+npjRkzRt6dkjyhUOjk5DRw4MDS0tLDhw/LuzsAAAqr3geEjFAg4JeVNGjULOLG6WPuTic9p2elvJJ3p6C+4vP5b968GTlypKGh4evXr4uKitLS0uTdKalITEx8+/atiYmJurr68OHDc3Jy5N0jAPg8UcqovDsiXdHR0X379l2zZo14MVUAUELJyckss4kQIhAIcv9FKZVvxxRGvU8ZJYRE3PBLfHYnJz1p1PyfctJet+zQbbCzm7w7BfVYQUHBgQMHRNcfkZGRI0eOlG+XpOTnn3/eunXrsmXLZs+eXVpaumnTJnn3CAA+Lz8/nyhBQCiKeyml7969MzQ0lHePAEAO7ty5ExISMmDAAAsLiwYNGjx8+HDDhg1WVlYcDuenn37icDjy7qAiUISAsNvgSbZj5+Rnpx//YarduLkaWjry7hHUb40bNxYfjf7pp5/k2Bmp2rdvHyHEx8dH3h0BgGoQrSGUd0eki8W9enp6xcXFnp6eTk5OXC43Nzd38uTJe/fubdasmbw7CACy0KxZs0ePHunp6RUWFg4YMCA9PX38+PELFiyQd78UiiIEhMX5OTlpr+Mf/d20ZTt59wUAAEC6lG2GUEdHZ/fu3YQQOzs7eXcKAGQqNzf3ypUrAoFg69atPB5PV1d3+fLl//zzz/PnzwcMGDB58mR5d1BB1PuAsHnrjmmvnt85u1+/hcnoRdszk182aNSMEEKpsLQwX6thI7n06vXr1zk5OZaWluyuQCC4ePFifHy8jY0NKqQBAEBtiKbO5N0R6VKStwkAlfj666/ZTmBMfn7+gAEDvv76az09vdmzZ7du3bp3795y7J7CqPcBYZuutm262orutupkQwh58+LhuR2uRh0sHN28ZdwfPp8fERFx8eJFHo+Xn5/fr18/QsjSpUtNTU379+8v484AAIDiUZIZQvY2GzbEblIASio1NTU6Orrcg8+ePZs4cSIhxMLC4tWrVwgIJaLeB4QV0jc0yc18m5v5tiA7vWET2S1Dj4qKmjNnzo4dO7S0tNTU1Hr27PnkyZMmTZrEx8f/+uuvAoFAQ0NDZp0BAACFZGho2KBBA4UPCJVkqSQAfIqRkVG7du3i4+PFHzx8+PDt27dbtWp1/fr148ePy6tvCkZBtp0op2ETw4497IV83pNgP5k1+v79+5kzZyYkJJw/f37mzJlz587V0NC4ceNGbGxsTk7OvHnznJ2df/nlF5n1BwAAFFJAQMDFixcVfupMSSZCAaASP/74Y7lHKKULFiwYOHDghQsXmjZtWsXjODk5PX78mN12dXW9fv26JHtZ/ylmQEgI6T7cmRASfvU4pUIZNCcUChctWpSUlNS1a1d3d3cDAwM+n19SUrJs2bLGjRubm5v7+PicPHny7NmzZWVlMugPAAAoJEppfn4+h8NR+EgJM4QAMGjQoHbt/lMzMjIysnXr1jY2NpqamlU/jkAgEG1aKBAIRLsaAqOwAWE7qwFNjEzfpyfFP/pHBs39+uuvwcHBjRs3PnjwoJaWVnFxsbOz8+jRo2/dutW2bdu4uLiioqKMjIyysjIVFYX9N6/vVFRU9PT0tLS05N0R6eJwOHp6eg0aNJB3R0ABFRYW3r59Ozw8vKSkhD0yYcKEyZMnT548OSIiQr59Uxj5+flCobBBgwZqaoq56EOkoKCAYIYQQLlxOJx58+aJP8Lj8Z4/f16DQ4WEhPj7+/v7+ycmJkqmcwpEYYMTDodjNcyJEBJ+5ai02woJCeFyuSoqKnv37jUxMSGErF27Nioqqri4+Nq1a2pqasuXL//uu+8WL168a9cuhf8Kr79KSkpiYmJSUlLk3RHp4vP5MTExCQkJ8u4IKKDo6OjTp0//+eefL1++JISwiaxTp06dOnWqW7du8u6dgmDzZo0ayaeGtoxpa2sjIARQchMnTmzSpIn4I48eParBcd69e5eSkpKSklJYWCihrikORQ5OrIc5/X1ie+zD6/lZabpNW0ipleTk5Pnz5wsEgtWrVw8aNIgQcvjw4VOnTuno6Pz++++dOnUihDg4ODg4OEipAwAAdUT79u1btWolEAi6dOny66+/jho1SkVF5dy5c6amplZWVvLunYLIzc0lyhEQPnr0KC0tDekMAEpOS0vL2dl5586dokdqFhBOnDjR2tqaEFKzCUbFprAzhIQQHb0mnW2HCwX8R0EnpNREaWnp7Nmzc3Jyhg4dumjRIkLIo0eP2PpXLy8vFg0CACiD8PDwMWPGZGZmurm5qaqq2tjYNG/efMSIEbq6ulwu99y5c/LuoIJgAaEyrKxTntAXACo3ffp0dXV10d179+7JsTMKSZEDQkJI9+HOjYw7+QfdFggE0jj+6tWrnz59ampqunv3bg6Hk5mZOWfOnLKysjlz5rA9UgAAFF5paamHh8e4cePi4uIePnxYXFxMCOnfv7++vv6MGTPs7e1dXV2vXr0q724qCCUJk3g8XnFxsZqamo6Ojrz7AgAViIiIEC/NUlpaWm5/CAkyNDQcM2aM6O67d+/evn1brSMsX748Kyvrl19+mTZtWnBw8IEDByTdx/pNwQNCU4s+PqHvdvx+NiAgQOIHP3ny5OnTp3V0dA4dOqSnpycQCL7//vvU1FQbG5v169dLvDkAgKrLzMx0cnLy8PDo27fvxYsXpdfQo0ePhg0btn//fg6Hs3DhwsDAQG1tbUKIj49PaGioj49PbGzssWPHunfvLr0+KBUlCQhFE6EcDkfefQGA8s6fP3/mzJnTp0+zVc0nTpxwdnZesGCB9FqcM2eO6LZQKLx//361Xt6zZ8+7d+++efNm4MCBx48fP3FCWsmD9ZSCB4SEkFmzZhFCvL29JXvY+/fvT58+3dTUdPv27WZmZoSQn376KSQkpHnz5j4+PuLz2gAAMnbu3LmuXbv++eefP//88+3bt8eMGdO/f/+goCDJtlJaWrp58+axY8e+fPmyY8eO/v7+69at09DQYD81Njbu2bNnw4YN//zzz0GDBrFTMdSeUgWECv82AeopOzu7lJQUVrecUjp+/PijR6VbxNHCwqJ169ZCobCkpCQnJ+fhw4fVPcLGjRu5XO7MmTNtbGwUvqR8dSl+QPjdd99paWkFBgZKsMhsVlbWN998U1paamNjM27cOELI1atX9+/fr6am5u3t3aKFtArYAEDNZGVliVcVKy4ujo+PF22NIANCoTAyMvLWrVvSLm72/v37uXPnfv311+np6X369AkNDeVyuYaGhqGhocOGDevdu/fFixdFezHVxuPHj4cNG7Z3715CyMKFC69du2ZpaSn+hJEjR2pqak6ZMsXd3X3MmDGY55EUlpQl0x2MXr8mPj7k4EHy5o3M2lSqYqoA9U5BQcGPP/5oaGiYkpIyd+5clhgibatXr05PT8/JySkrK6vuDCFUTvEDwqZNm06cOFEoFB46dEgiBxQKhU5OTomJiba2ttu2bSOExMTE+Pj4UErXr19va2srkVaUUExMjPjdlJSU6OhoKS3+rENSUsiTJ0QJKiCX+/1mZWUlJCTIZmfY8PDw06dP37x5k40KPX78+Ouvv/79998dHBykt+BBXGJior29vZ2d3fDhw01NTX/44Qc2+yFxAQEBXbt2/e2337S1tb28vEJCQiwtLRcvXhwXF8flclu0aHH37t0xY8ZYWVmdPn26xmFhaWmpl5cXmxj84osvyk0MgvQkJCTMnTt3x44dpqamR48e3blzpyz+gu7dI5MnE319oqdHvv6ahIVJvUVCiDLVzgGoj1hN6QEDBhgbG//222+yaXTs2LHt27dntx8+fFjjS8SkpKSbN29W91UCgeDUqVMeHh6rV6/29/fPyMioWet1FFUCoaGhhJAWLVqUlZXV/mhubm6EEENDw+TkZEppfn5+ly5dCCFr1qyp/cGVlr+/v6en57lz53g8HqV027Ztc+fOXbly5dixY9kjMhAcHEwIGTRokGyao0IhnTGDTpxI166lvXrRixdl0ywLikxMTGTTHOPn57dly5YLFy6wu7t27Zo4ceKSJUvmzp0rg9YzMzMXL178ww8/FBQUUEqTk5PT0tIopb6+vr/88otUmxYKhXv27GF185s1a8bOFey2p6dnbm6upBrKyclxcXFhB+/Tp09sbOzHzykoKOByuS1btmRP+/LLL319fQUCQbUaevLkiaWlpYODg7Gx8cqVK4uKiiT0DqAysbGxzs7OqqqqhBANDQ1RCWt7e/tXr15Jt+1Ro+iDBx9uh4bSceOk29y/zp8/TwgZPXq0bJoDgFoqKSkZNmwYpbS0tPSff/6RUitcLlcUwjx9+rS6L7906ZK+vr6RkdGoUaOEQmHVX5iVlTVs2DBCiL6+vqgDRkZGjo6OXC43LCysul+mdY1SBISU0q5duxJC/vrrr1oe58KFCxwOR01N7e+//2aPTJo0iRDSqVMnCV7bKaHo6OgJEyZcvnyZ/UXFx8ezx0eNGpWSkiKbPsg6IPT3p9999+F2Vhbt2JFKYsDis2QfEAqFwuDg4HHjxkVERLBHbG1t2ejMt99+GxUVJe0OvH//PiQkhKVKDh8+XPT4tm3b/vzzT+m1m5CQMHjwYPa14ejomJGRQSkNDQ0VPairq+vm5padnV3LhgICAoyNjQkhbGKw3NdSWlra5cuXRXdLSkq8vb3Z8wkhXbt29fX15fP5n22lpKRk7dq1ampqhJBu3bqFh4fXstv/kZtLv/2WDh9Ohw6lLi4Ucea/EhISXFxc2D+7urq6s7NzXFwcpdTPz6958+aEEB0dnY9/6ZLUoQMtLf1wu6CAdu4srYb+y9fXlxDy7bffyqY5AKiZ5OTk2bNn37lzZ9myZebm5itWrPjuu++MjY3/+OMPaTSXl5cnyiRn2XnVkpub++7du+q+KiIiol27dmwwd/PmzevWrRswYEC5Asj6+vrHFi6knp705k1aUPDJY1UnCpUlZQkId+3aRQgZOnRobQ4SGxvLPoWiWYXt27ezqzoZXNQqtrCwsLS0tGvXrj1//nzFihWU0tDQUDc3t40bN8qmA0VFRTNnztTU1DQwMHj48KEsmly7lh49+v+7gwfTly+l3Safz/f09NTQ0NDV1b169aq0m2PevHnj5uaWnp7+4MGDc+fOHThwYPDgwUlJSYWFhVOnTr106ZJsulHO48ePR44cWVJSEhISEhkZKdmDC4VCb29vXV1dQoiBgcGZM2fKPSE0NNTBwUE8LMzKyqpBQ+/fvxdNDPbu3TsmJubj56xYsYIQYmFh4efnJxoQLS0t9fb2bt26NXttly5dKg8LIyIi2M7yKioqLi4uBZV829XM0qV0z54PtzdupJ6eEj5+PfRxKPjyv6eI7Oxs8d/+ixcvJNyDqCialkZtbKjo+iklhdrZSbiVT2Df2gsXLpRNcyISySQCUDalpaXv/+Xj49OyZUsjIyNP6ZzJly5dys57s2fPlsbxyzl58iRL87GyskpMTBQ9zufznz9/7u3t7ezs3LZtW0JIkLU1JYQSQlVVaZcu1MWF+vpSURJHRgadMIGOGkWHDKGurrKZA6g6ZQkIc3JyNDU1NTQ0evfuPXPmzC1btvj5+YWHh1d9Wq+goMDc3JwQMm7cOHZRdfv2bXV1dQ6Hw1bjgGS9ffs2KCho+PDhWVlZFV7mStCpU6dMTExYLha75J0xY0ZqaqpUG6Xr11Nf3//fHTCAJiRItcHg4OAvv/ySECIq7zF69OgKcwslpaioaM+ePR06dGjZsuW9e/dEjz99+nT27Nnff/+9o6PjjRs3pNeBT/nnn38mTJiQlpZWVlbWq1cvIyOjSZMmPX78WCIHT0xMHDJkSLmJwQrdunVLFBY2bNjQ1dW1Wp+6wMDAVq1aiSYGPxXO7du3T1TpytLS8syZM+Jhoa+vb4cOHdhP27Vr5+3tXS5Ju6yszMvLi/1ptGvXTlqJQF26/H9I9fVr2revVFqpJxITEysPBcVdunSJfQy0tLQq+RhUT3w8dXGhqqp08WL6ww/Uw+PD4+vX059+on//TdeupSUlEmjoEyIiIrp166atrW1lZSWz7JvU1NSrV68GBQWxlPLMzMxp06Y5Ojp+/fXXr1+/lk0f6pq8vLyEhIRKPn4AFfLz82vdurWRkdH69eurlZlZFYmJiez02LVrV8keuRw+n+/m5sYumb799tvKl0gkJSUV+/nRRYuotTVVU/sQGbL/TEzonj10+nR64sSHZy9ZQnfvlmrnq0tZAkKW6llhvTJ9fX0bGxtHR0c3Nzdvb++goCBRvqI4Z2dn8dTQ1NRUthRn9erVMn83ik90BTB//vy//vqrdevWkyZNkvz4N6WPHj3q378/+yRYWVldvXrV3d2dFSNu0KCBu7t7cXGxhJssK6NcLl2xggYEUCenDw++e0fNzGhGBh0xgt69K+EWKU1KSmIfYEJI69atf//9dy6Xy/4c1NXVXV1d379/L9kWhULhmTNnrK2tjYyMWrZsuWDBgrdv35Z7jkAgGDp0aHp6umSbrty4ceNOnjzZtm3btWvXenp6njx5cuPGje3atWP9nDZt2qNHj2p88HITgxXmqDs4OJRLE71z546DgwP7ymnQoIGrq+tn06TFJwbt7Oyio6Mrfz6bD2RhAyHE3NxcfD6wrKzM19f3iy++YD81NTX19vZm8yQRERHW1tZsEEEqE4MibdtSUSCamUmtrKTVUN2WmJjo6uqqqanJRqYcHR3Fh2yioqK++eaba9eulXsVW0HKPkKWlpa1GtqIjqZTp1JVVUoI1dSky5bRoiK6cCEdNowOG0YXL6aFhbRjR0oI7dJFGierp0+fTpgwgb2Xhg0bslNWYGCgxBv62KtXr1xdXRcvXpyQkEApjY2NvXv3LqXUz89PZrkqdUdZWRmrmm5hYaGiouLs7FzJ2JZkZWVlFRQUSGZoA+QnMDCwTZs2RkZGrq6uEi8GMWHCBHaSrMGAkUAg2Lt379y5cysf6cjMzBw6dCghRE1NzcvLq3ptFBTQ0FDq5UUdHGjTppQQuncvbduWinL7IyKog0N1ey5VShEQvnz5snHjxoSQtWvXBgUF7d+/f/ny5ePGjevateun9iFp0qRJjx49pkyZsmHDBl9f3+Dg4AEDBujq6rKYpKysrF+/foSQQYMGyazkiZJgKaNr165dsmTJDz/8MH78eH9//06dOhkZGZmYmLi7u0tqtDgzM9PV1ZUVaWjatCmXyxV9/bx8+dLR0ZF9Etq3b+/n5yeRFiml9MoVamZGCaEqKjQmhs6fT8eOpatW0V696LVrdN06SgjlcOi339LkZIk0WFBQ8KkQt5J/gVqKiIgYO3askZGRkZHRsGHD7t+/L/oRSxk9fPjwpk2bHB0dDx48KJEWq66kpCQ5OTniX2zgPzMzc/v27R07dmR9HjNmzK1bt6p7ZFZKVDQxWOEqhfDwcPaERo0arV+/PjMzU/Sjx48fOzo6sutgTU1NFxeX5E98Bq5cucJSPSufGPwYmw8UlWhjaaKiMxiPxzt8+LAoLGzbtu3YsWPZlqpffPFFDf5BqmfkyP9XLgkI+P/yWqXx+vXrSkLBFy9eODk5sa0mBg4cWOERAgMDWaaDurq6m5tbqWjhXxW9ekVdXD4MbKurU2dnWtHYKKWU3r37//OYiwvNz69eQ58QGRkpXjjHxcXlxo0bvXr1Ev1Nif+9SMPDhw937dp16NAhHo+3dOlS0anyzJkzUsp8q5uEQuGJEydEJ4r27duzBAEDA4MjR45IfLZHXH5+vpeXl66u7ogRI6ysrMTzSqA+Cg0N7dChg5GR0fTp00skmlPAqkUSQoKDg2vw8qVLl+7Zs4flAlToyZMnLAu0efPmtc1jEgjos2c0PZ22bv3/B+PiqMwqVlSN4geERUVFbOnL2LFjPz6RCYXCN2/e3Lx508fHx83NbeLEiZaWlmxU8mMNGzY8cuRIQUHBokWL2LBlDVamQhUlJyfHxMSwi92cnJz169e3atXKyMjIzMzMx8enNqFLWVlZVebHgoODLSws2K9+8ODBooIoNRQbSx0dPyQPdOhARUFmejp9+pSyK4/CQuruTrW0KCFUR4e6u9emtIZQKPTz82MXiBwOx9HRscKsp/DwcDa6weZIa5kQmJaWtnLlSvabsrS0PHr0aIW/qeLi4sjIyLpWhykzM3Pz5s1ffPEFCwsnTZp0t8oTIH5+fqzymL6+/pEjRyp5ZuVpohEREaKwkF0Tv3nzRvTT3Nxc0USQra1tzebM2XygKE20bdu24mmiAoHAz8/PzMyMENKyZUupTwyK3L9Pe/akx47Rw4epjQ393JynIvk4FBRPkn/16pWLi4soTHJ2dq4wh4XJzc11dXVlcaOFhUVYWFiVOvDyJXV2/jArqKFB586ln82QLC6m7u5UXZ0SQtu1ozXeGPBAAAAgAElEQVS6JhP5OBQUjYYIBAJvb2+2gMfQ0LD2leEqFBUV5eLi0rJly7Vr17JHEhMTWZ2enJycYcOGKU/KaFBQkI2NDTs5mJmZsfHQly9fsqkSQkj//v0lvuiaUlpcXLx9+/YmTZqwLyw2iK+qqurq6lrXvimgWh48eMDG9KdMmVJYWCipw0ZGRrKlEGy7XclW1Tpx4gQrGGNtbS3Jv/0+fajo3H78OF2yRGJHlgTFDwi/++47QkjHjh2rlRSXnZ0dFhbm5+fn5eXl4uJib2/PPnmGhoajRo1iX1pVv1gEiYiNjZ0yZQq7WO/fv//NmzdrcJCgoCBR6X97e/vnz5+zx4VC4cf1i3k8nre3Nyvlx3JmajIEkJ9P3d2ppiYlhDZoQN3dP7P25s0b6uxMORxKCG3Vivr61qAm1YMHD3r37s3eZvfu3W/fvl3uCSUlJeJ5hv7+/qampuz5Dg4OCdVfzVhSUrJ//34WTbVp08bT0zNfQvMGMpaXl7dnz57OnTtbWVlpamr26dPH39+/kue/fft29OjRon+6KhbFrTxN9NmzZ+LXx2wJmWhiUCJLxVhY2LFjR9ZzU1NTLpcrGsEVCARTpkwhhEydOrU2rVRPcjI9epSeOEFfvaIxMfTTY7cKIykpydXVlU3gs1BQ/K+ShYLiKwkrCQXFhYSEsJleNTU1Nze3SgbmRXVrMm1sPswKxsVV4w08eUJZEQUOh7q40Ly8aryWUkppVFTUp0JBcfHx8YMGDRJNFUpwKPb58+fTp09nBTDat2+/detW8Z+mpaU5OjqGhoZKqrm67NmzZ6KxqlatWnl7e5c7yfj5+RkYGIgGUiU1TsQGodhsDLu+v3XrVmFhobu7O5uZNDIy8hVfby81Up38VGbR0dGWlpajRo2ytbWt5Tz/8+fP3d3dRVdx7PRICGnWrJmzs7O/v38ti0KxRYPsmM7OzhLeV+nmTWpnR48fp/v3UxsbmpQkyYPXmoIHhDt37mTD8Oy6n8fj1WYUge1i2bhx465du+7fv19y3YRq8Pf379mzJwsL16xZU/XQJSYmhgXzbIBAvLhleHh43759tbS0Kjxadna2q6srO+/o6+t7eXlVMRdLIBAkHz1KDQw+5FbNnl2Na9ybN2m3bh9mFAcOjHnypIqvS0lJcXFxYVMELVu29Pb2rvAzv2XLFnV1dRcXF9GykKKiIi8vLzY9rq2t7ebmllflyzt/f//27ds7ODgYGRlNmzatBvFkXZObm7tnzx7RdkN9+/atsCir+MSgt7d3dVt58uRJJWmiz549++abb9ivUk1NjT2tKisGq47H44mHhUOGDBH9yNvbmxDi4uIiqbY+o3t32rr1h6prLHdaVMhEQf3444/seldVVfXbb7/9eFbw460mqq6oqMjNzY0FWubm5uJp20xcXNz06dNFTfzu5lbDolalpaKpwoOjR3+8vvFTPg4FxSfDRUQnE/EFujX7cyvnxYsXbFbQyMioXbt269evF88f27hx471796ysrLZt2+bv73/nzp1aNleXsSJG7FTDvuM+dR2ck5MjmoJu165dQEBALZsOCgqytLRk5x9zc/NyCzSePn0qGtkcNGiQNOoIMGFhYS4uLhMmTFizZo0sEiKUT3x8PNu2oVu3btWt2CcUCu/du7dy5Up2BIaNoxkYGCxZskSU3swiw1mzZgUEBFTlOq3ckEdGRgZb91GTRYNV9Po1PXqUnj5NExPp+fNSaaKmFDkgvHv3Lvu6PXXqFHtk6dKlgwYNqk0RC5ZKcfjwYcl0EWqkrKzMx8fH0tKyRYsWGhoarq6ulYcuOTk5bm5u7MPQuHFj8YguNTV1xowZ7OutRYsWlSSjR0dHjxw5kp1xOnXqJL6xW4Xu3btna2tr06KFsEED2rNnTaovCATU15caGp6xtWXzk5Xku1NKS0tLuVwuu2D67D/LsmXL2KVY8+bNxUeCk5OTnZ2dWexhbGzs6+tb+aDps2fPRBU1ra2tQ0JCqv0267D8/Hwul2toaMjeoKWlpWjnhrdv344ZM4Y9XsvdMiMiIpydndmH8OMr4/j4eBcXl4YNG+rq6m7ZskUahRZEaaK///676EE/Pz82GyPx5irWvDklhLKT8/btlBC6bJmMmpaH0NDQ4cOHs1xu8cvcT+06WDO3b9/u3Lkz+e9mIdUqYVpVEREvv/mGEMLhcGbNmlV5Pg77SH82FKSURkREaGhoODo6ijZlSUhIEK3UHTVq1KfW2VYuOjp60aJFxsbGLJ1h5cqVH59aIyMjHz9+fPRfVQ9065eMjAw3NzeWrsy+NSrcFtXX11f8uyAsLKx79+7st+Dg4JBUo4mO+/fvDxw4kB3ExMTk4wlJRiAQ/PbbbyyVVFtbe9s2n+quja0EG/hjBeTZnwkhZNy4cfV9h/G66e3bt6zOedu2batyWhMIBGz7sXLxnrOz88aNGzkcjqampqgIXLmZQ3ax5+jo6Ovr+6lkpSVLlri7u4vuPnr0iOVJtWzZUuoDQHl5tEkTqq4u7dry1aKwAWFaWhrbeXn58uXskVOnTpFap3r+9ttvhJB+/fpJqJtQcykpKVOnThWFLkePHv04dBEIBL6+vizLhcVUouEAtpJQT0+P/JsAU5WFCkFBQWx5FSHE3t6+wqUUr1+/njRpEuuYiYlJ9LlztdqHNCdn6/r1rLZH48aNd+zYUWFGhL+/vyjlxsHB4ZVo35tPi4qK+uqrr9hLzMzMrly5IvrR/fv3bW1t2Y969OhR4cmx3MQpl8tV1AJLrM4ByxwmhHTv3t3NzY1doDRu3Lj2MxVMRETExIkT2RWJpqbmvHnz/sfeeYc1db1x/E3CHgKCSKCoDCeKAxUVceIAqbMoDlxVUKtYJw4KWhdaB9KqDBcgglgXLjTKT0GcKIqCyhAEBdl7huT8/jj2NgVExk0C4XweHx9yk5z3ZN17vuddgotdbE6oVf54PJ7ggozD4eAvufAs/gdcuBI7P0+cQABo0SIRmRYHu3fvhv820fpu18GmUVZWtn79eqy+DA0NJ0+ejE8mUlJSixYtamAMakPgcrnu7u448UZLS+vixYu1H9PYIFhfX1/sB9DR0bl69So+yOfzfX19m/YDjImJ2bx5M/YK6unpubi4iLjKccuhpKTEzc0NXwFxuPK3Ijuys7NxRp+gjw5/3Hj/UVFRsVFB7O/evaMiI9TV1d3c3L5bzTs3NxenTw8b9tHQEDVfnsfFxTk6OlIFI7S0tJycnO7du6ehoQEAGzZsaK4BQl3k5ubipQWbza6dpIOprq6OiIhwdHRks9mUutPV1XV0dORwOFwu99OnT/hjOnToUO2nJyUlubu7m5mZUe215OXlra2tfX19a6zxBBMaz5w5g89dw4YNq10RXSjY2SEAtHy5KGw1DMkUhFwud+TIkfijxavnd+/e4RPfn83r+1FSUoLHef36NU2TJTSLZ8+efStZLiwsjKoKM2rUqJcCUZc1dF1cXFzDLdZWktR2eGlpKRV1qaCg4OTkRFce3fv376nsjq5du1JrI4RQdHQ01Tmjhq5rCN9SknjVhVNnGQyGnZ0dFeYhmFopJSVlb2/fFqorlZSUuLu742YzeIvBysqqaQ6KeqCqazAYDME6RrgGjFCbRtYgKioKAExMTERkz9QUAXx1pF+8iADQ1KkiMi0OcJrK7t27qSM4aVNaWvrnn39uyIZOo4iOju7fvz+DwZCXl69dwpRGEhMT8cUX/tuEs8lBsN8aMD09fcqUKfj4xIkTv1v4gfplsdnsbt261ekVbCNQ/SSoK+B3S6Z9K3vw8+fPVEOjfv36fbco6KdPn6ivgaKiopOTU6OKO4SHv8TVbRkMNH8+aoKWLy9H584lUtudDAZjzJgxwcHB1Dbr/fv3cTARSQsSEsXFxdjJr6amJuieoXQgFZIDAF26dHF0dIyIiKC2+3k8Hg5KmjhxYv3hSykpKTWUoZycHFaG+fn51MO4XC6VNGhvb9/o4sxN5u1bxGQiWVnUjPAiepFMQbh27Vq85YPjuIqKirAfeQ7V9q0ZrFixAgAcHR2bP1QTKCgouH379o4dO86ePfv06dM6ozvaGrXLaT569Eiw7Z5gPnpjIz+/RXZ2do2eDZcvX25mXZbvwuFwqOAWCwuLyMhIag7t27dvct+IemJNcdcKHFCEu1bcvHkTR30AwJgxY761ySeplJWVTZ06FQAsLS2FZ+Xt27eHDx8WPDJo0CAAqJ0JJjySkpIAQF9fX0T2Jk5EAAinJP3vfzh1VkSmxcHy5csB4MiRI9SR2NjYxYsX0+iyq0FFRQX2tr1scEJy08DJfnhfrGPHjkePHm1mEKzggJqamufPn6fuCg4OVldXB4B27dq5u7vXGen35s2bWbNmYd+7nJxcjbq+bQp8raQqDA8ZMqThZaVx9iC+3NTIHrx69Sq+9uEwHCq+VxCqnwT+Gtjb2zfND4P7+CopIQCkqorc3VEDozsTEpCTE9LQQPLyZWpq7VVUVOzt7evc2T958iSe5J07d5owQ8J3qaiomD59OgAoKSldu3YtJCTEzs5OsE+4vr4+1oG1n7tjxw58Ymn4rzg1NdXLy8va2pqqQMNisczMzNzd3WNjY8eMGQMAsrKy3t7etL7KBvDTTwgA/RPGKHYkUBBeunSJwWBIS0vjdCY+n//TTz8BQJ8+fWipePvq1SsAUFVVpbF+bgPJz883NDTs3bs3CKCmpmZiYmJjY+Pk5OTl5cXhcJKSktpgsazi4uJNmzZh6YKXDkpKSrt27aJiUZpcG6YeBP2TGBMTE6F2bKuqqjpw4AA+deJS7NLS0mvWrBHc8Woa9VSjiY+Ppwpp4gcYGhpebmH50CLj1KlTALBw4UJRGh0/fjwANNb92xzy8vLwL0VE9mbPRgAoIAAhhF6+RACob18RmRYHc+bMAQB/f3+RWayurmYwGEwmUzRXB8qzR2mApUuXNmeb7MOHD1S6smA53y9fvuAW1QBgbm4u6PlsYAnTNoJgP4kePXpQ6dCN4lvZg4JFQbW0tAQ3YSsrK728vLCDEe/YNt87nZSELC2/1lwzMUHPnn3zkVwuungRjR//tW43ABo0CJ09+7jO9RvlZ964cSPeZaDqkBPopaqqau7cudQyBjNgwIBdu3bVUzXtyZMn0tLSTCazaTm9GRkZR48eHTt2LKUM8YpRR0dHPB0vX75EDAZSVEQtI8ZK0gQhFRrq4eGBj+zZswfrN1ryMTBDhw4FkZeW4fP506ZNw3sn69atmzFjRt++fQV/S4KoqKgMGDBg5syZW7ZsOXHixP3795tT9KIVkZSU1LNnTwUFBVNTU+ol09M94hvw+fyAgAB5eXlZWdlvlfSknczMzJEjRyooKOjr69NYcxLV26/i1q1bqqqq8vLyGzZsoLfDbOvi8uXLADBlyhR8MzMzMzY2lubi1LWYNWsWAAQGBgrViiA8Ho/JZDKZTBHVV1ixAgEg7DH7+BEBoE6dRGFXTOCKx/V3NGk+79+/f/bsGa7zjhW+qqqqUC0KQu3GGhkZ0RIEK1hotEb2YHBwMD7DKygouLm5JSQkNLBuTRshOTkZvxu6uronTpxoTm2qerIH379/j/0tADB69OjY2Njg4GCqMiTuJ0HTC0IIoeBgpK2NAJCUFNqyBQ0ejPCH/PYtcnZGGRnIzQ117vxVB8rJIRsb9C37XC7XwcFBVVUV54/w+Xx8ytXT02sLCRHNJCcn5/bt2+Hh4YIb069fvx45cuSVK1e+9Swejzds2DB5eXl9ff0//vjju6eIgoICnN6yadOm5k/4+PHjZmZmsrKyOjo64gwZmDQJAfD+2Cm2CQggakH48eNHwTNCSkrKvn376ArlLy4uxqGhs2fPxkfCwsJwufY6E9ybjK+vb9++I+bMEWnZsd9//x1v2NeIKcrIyAgPDz958uTWrVtnzZplYmIi6HkXREFBwdjYePr06UFBQaKcuYhZv349AOzbt486goMTAMDCwkJIyZ94Z1SUMunYsWMA4ODgQPvIPB7v1KlTOJ+byWQKntBx4qUwuhK3Iu7fvw8AI0aMwDdNTU0BQNhdSZctWwYiT2vBlSSa73xuELjVxM6dCCFUVIQAkLKyKOyKieHDhwNAwwP2moZgYGpycjIAdBKtzN6+fTsAODs70zjm58+fqQK/VlZWlNLLysqytbXFx6niTCtWrGjjUhBjZGQkJye3efNmurauamQPUidAPp/v4+ODS/5g9wt+gJCiG0pKkJMTYrGQpycyMEAzZiCE0PPnaNYspKj4VQr26IHc3VH9pzE+n4+/PHp6erjOUFlZ2eDBgwFg+PDhbXkDtCHExcWtXLlyzZo1lO+Xy+XOmDFj27ZtZ86cqeeJK1euFPTf1A/Osh44cCBdaX6PHj0CAFNTU1pGaxq8qMcZR02jn6tVVzcimVZIMOtUDkLi9evXvr6+Fy9evHPnDgDEx8d7e3tHRERkZWU1f3CE0OLFi+Pi4vr06ePj4wMAaWlps2bNqq6udnZ2xr41urCxmZ+Wdv/s2XHR0TSOWh93797dvn07k8kMCAgQ7MQCAFpaWubm5osWLdq5c2dQUFBUVFRBQUFeXl5UVFRwcLCbm5u9vb2FhYW+vn5ZWVlMTMzFixfj4+NFNO+WwfLlyw0MDIKDgzkcTo2AW0JtmEzmwoULExISXF1dDQwMqCLvBAxuPJifn1/nTdEYFQ2UIBSFMdzyEdtSVgZpaSguBi5XFKbFQWFhIQB8a/OOXis4akY0FuucAL1GtbW1r1y5Ehwc3L59+xs3bvTu3Rsn/3To0CEwMPDSpUtKSkpKSkqzZs2Ki4s7cuTIDz/8QKP1Vkp+fn5FRcUvv/wiLy9Py4Da2tp+fn44e/Dly5dmZmbz58/Py8tjMBhLlixJSEiYNm2aioqKioqKl5dXVFQUVdGaXhQVwc0N3r6FsWOhb19QVIRr1wAApKRg+nSwtgYOB+LiYPVqUFWtbxwGg3Hy5ElTU9Pk5OTp06dXVlbKy8tfvnxZV1f3wYMHOEFAGPOXDPT09IyNjfX19XE/Ty6X6+3tPWHCBMEyoc3k+PHjgYGBSkpKAQEBeP9dMmCamBaNVeCh/OzsI+KeC4hUEBoZGSGE1NTUhgwZEh4erq2tvWvXrs6dO+N7y8rKmjP4H3/8cf78eWVl5eDgYEVFRS6XO3v27Ozs7LFjx7q6utIx/X+Rlwe8NebjQ+/AdZOammpra8vj8VxdXS0tLRvylDoTC/Pz8589exYYGEivPG75WFhYvH371sbGhsYx79+//8svv5w/f57GMZvD2rVrf/nll6qqKroGVFRU3LZtW2xsLK7FTKBoO4JQlEZfamsfHjr09D/l4IZ07izLYmUXFIjAtFgoKioCUQlCbEXsgjArK2vnzp1+fn60jGxjYxMTE2NtbV1YWLh8+XKc2w8AU6dOraqqKioq8vX1rbF52paprKyEf3p504i1tfWbN282btzIYrH8/f1xlxoAaN++/axZs7KysiZMmEDF7gqPrl2//rF3Lzg7Q3k5AICvL1y9ChYW8M9J5TtgBdipU6fIyMgFCxYghNhsdkhIiJKSUlBQEE4+ItSJnJzc0qVLV65cyWKxbG1t09PTb968uWjRInxvVVXVuXPnSkpK6h8kLy/vl19+cXFxqX1XQkICLhXp6enZrVs32ucvXtjsrQCQmXmQz//OWyRsRCoImUzmunXr1q5dq6Sk9OnTpxqL1379+vXs2XPixIkODg67du06c+ZMRETEx48fGzLy//73v61btzIYjNOnT+M+vI6OjpGRkZ06dQoKChLGyWjZMmAwwN8fiotpH/s/VFRUzJgxIycnZ9KkSc7Ozs0ZSlVVdeDAgba2tm3QS4Zbb9FITEzM0aNHw8PD6R22yXh7ex89epRGQYih/X2TAIggFAbJcnK/PnoUkpSEb+YxGFU8XoHkCkJB352wrYhREArq3tTU1N9++83Dw4OuwXFzwjNnzmzZsqVfv374YHl5eVVVlaysLBWvSACAiooK+CeG8/79+3v37o2MjKRlZEVFxb17975582bs2LEbNmygSvxji7RL0PrR0oIlS+DAAQBoqA7879O1bty4oaKicu7cuV27dgFAv379goODWSyWs7NzYGAg3fOVQHr37r1///6CgoK5c+f6+fl5e3sHBQWtWbOmT58+Dg4OuHBonU8sLi4+evSor69vjeOVlZWzZs0qLi5euHAhrkMjYSgrj1VSGlZdnZuTc1y8M5ESsT2qCAqusUaRn59fXV1dUlISExMTExMjeJeKioqxsTGTyTQ0NDQwMDA0NOzatatgo5KMjIy5c+dWV1dv3boVZ4sFBAR4enrKyclduHAB96+knR49wNwcwsMhKAiWLhWGha+sXLkyKiqqc+fOvr6+OC+CQCCIEUVFRRkZmdLS0qqqKhkZGayahC1dJF4QikVmiwuEUHFxMbQBQVh7ArS/5BprRNG4XlsdgvIsNDTUzc1tz549ZmZmdI3frVs3DofDEBBhQvJJfpfly+HUKfinzXCjMTIyCgwM/PHHH11cXPT19efMmWNpablv375169YtXrxYT0+P6mFIqJO9e/ceOnQIl/H09vZWVFTs3Lnz0KFDHz9+fPXq1atXr+rr69va2trY2Agu4+th48aN0dHRhoaGNO4ltTS0tDYlJk7+8mWfhsYyJlPUPxkKUQtCiufPn+NqkPimmprahw8fCgsLP378+PHjx9TU1Pfv38fHx6ekpBQWFubk5ISFhQk+XUlJydDQ0NDQUE9P78aNGxkZGWPHjsX56zExMfb29gDw119/UfWRhYGDA4SHw9GjQhSEPj4+J06ckJOTu3jxIu62RCAQxI6qqmpWVlZBQYGmpibxEArDlmQLwpKSEh6Pp6ioSFU/FxKCekwsSkn0SYyicb22Lqqrq3k8HovFwt83QW8hjTD+65ITsYdQXR3mzQMAYLHA1xeSk5s+lKWl5f79+9esWfPzzz/r6ekNHTp07dq1iYmJx44dmzZt2uPHj6lEJ0JtRowYQbkuBg8eLCMj06tXr+HDh6ekpAQFBQUHB3/48GH37t179+4dM2bMd+NIb968+eeff0pLS585cwYXtpVIVFR+VFAYUFb2IjfXt0MHB3FNQ2yC8MWLFzo6OjjIeOfOnQsXLrS0tMTOQGNjY8FH5uTkpKSkxMbGJiUlJSYmJiYmJiUl5eXlvXz58uXLlwDQuXNnNTW1wMBAHBpaVFSkrKw8Z86cn3/+Wagv4aefYM0aePkSoqJAGMLz5cuXq1evBoCjR48OGDCAfgMEAqFJqKmpZWVl5efnE0EoJFuSLQhF5qwTFIEtxEMoyrRJAqaGs040vjshyc5voaYG06bB8eOwejUsWQKHDzdrtF9//TUhIeHo0aOTJ09+/PixgYGBh4dHYmIih8OxsrJ6+PAh+YJ9C9yVDUMFcgNAly5dNm3atHHjxsjIyPPnz1+7do3D4eCNm6CgIHNzc3zOFyQzM3PRokUIITc3N1zNW4LR0tpSUHBRWdmcOoIQD6CO8FoGnwF1ht2yWE2JkxZAbIJw6dKlAHDu3LnvPlJDQ0NDQ6OGry8vLw+Lw2fPnh0+fFhOTo5Kdho+fPiLFy9w1WOhIiMD8+fD/v3g5UW/IMzLy5s+fXp5efmKFSuo3FwCgdASEJQrRBAKwxYRhM0HB6YyGAy8sy4W1xkRhC2BGtpMNL47sYSMyslBWRnk5tIwlIeHx8ePH69fvz558uTIyEhVVdW///7bzMzszZs3tra2V69eFbZ7XyJhMpnm5ubm5ubbtm27cOGCu7s7ADx8+LB///5YPfL5fPxIPp8/b968zMzMCRMmrFmzRpyTFglqajPU1Kalpa0uL3+LULmu7l/x8SN5vDqKlPTYb6QYFFvHECdPgrU1/Por5OdDZSVMnAjr1zdKIrbWnLT27dsPHjx4zpw5hw4dGjNmTHl5+dmzZ6l7tbW1RXMasrcHBgOuX6e5Ojqfz587d25ycrKpqenBgwfpHJpAIDSbNiUIRVPZRVlZWVpauri4mMvlgqQLQtFEb9YITCUewjZLDW0mGt8dNiri0j64ZAQtgpDFYp09e7ZPnz5xcXG2trbV1dXt2rULCQnR1NQMDQ1dt24dDTbaMO3bt1+6dOmYMWMAYOTIkerq6jji7/PnzzNnzrxz586ePXvu3Lmjqal5+vRpRvMcX62F4uL7XG5mt253unePkJc3BpBiMFi1/wGTBay6/jGZsHIlTJsGN27A7dvw7BlcvNioCbRWQSiIg4MDAHh5eYnedNeusGcPcDiA3ZP37kFlJQ3Duri4hIaGampq/v3336RUGoHQ0hC9IGzXrh2LxSoqKuLxeEI1JIiIVZmgbJBsQSjKEqOUFdErperq6rKyMhaLhYvJYRksyjo6BEwNBSiRIaMYXGkhJ4ee0bAC7Nix461bt1asWAEAenp6Fy9elJWV9fDw+Ouvv+gx0+aZMWNGWlraoUOHAAAhdP78+XHjxrm4uDCZTH9/fy0tLXFPUETIyPxQWvqssPAmQojBYPXrlzdgQHXtf4oBr6C6uo5/8+fDkyfw008AACwWLFv2tSlng5EEQTh16lQ2mx0TE/Po0SPRW3/wAJYsAbxIc3WloQvFtWvX9uzZg7v6kI66BEILRPSCkMFgqKioIITwelc0iFiVYXMPHjyIj4/HRiVbEIrYVyb6ojKUIsUb/KJ51aTKaG3E6CEUccgoFoS0eAgxXbp0uXjxopyc3PHjxx8/fgwAZmZmJ06cYDAYDx48oM1Mm0deXn7y5Mn4D1wNRF5eXllZWaiFIVsasrJd9fWD8vOD4+KMKyreN/r5XC4I9glr1w4auVqQhBhoaWnphQsX7tmzx8vLSzCfVWT07QvHjsHKlTQMlZiYaGdnx+fz9+7dO378eBpGJBAIdCN6QYgN5eXl5efnCy9B+vLly7Kysg+Vr/UAACAASURBVJaWlpRFEKEqW7Jkye3bt2fOnNmzZ8+YmJg+ffrggCLJQyyCUPSusxrajISMiou24yGkMWSUYtiwYadOneJyuVTDCUVFRYTQ8+fPce1WOo21STIyMtatW4c7EJaXl8vIyEyePPn9+/dxcXHz58+/evVqGwkZBQBFRVNFRdOcHJ+cnBMMBpPPr6j9mI7XVWTe1aX05swBGRnIyfn6M3jxAoyMGmVdEgQhACxbtmzfvn3BwcEHDx4UQTmZGqxbB7NmwYwZAAAvX8LJk2Bo+O8/Tc2GjlNeXj5r1qyCgoIpU6Zs2LBBeBMmEAjNQVVVFf5RSoLJb9KC+3N0I1R5lpeXt2rVqrNnz7LZ7Li4OPwCRSkIo6Ki/Pz8YmNjWSxWu3btlJWVX79+PXfu3JMnT7q4uIwYMUIEcxAZYhSEoiwqI5aYVdJ2ojZtx0PYrh3IyEBREVRVgYwMbcPa2tpSf5eVleEaJ2vWrCFqsDkUFRW9e/cOANzc3Khu9aqqqu/fv9fU1ExLS+vXr9/169c9PT2XL18u1pmKiLKy6PLyN3JyhoWF19TUbFJTV9RZVKb9fSOZOovK9O0Lv/0G8+bBmjWQmQmennDzZqMmICGCsFOnTuPGjQsNDfX398etGkSJnBzs3AkbNwIAxMRAYOB/7m3XDgwMvorD3r0f6ehUGRoa6ujo1B5nxYoVL1686Natm6+vb9vZESEQWh01lJKKikpOTk5hYaEG3pmjibKyMqpTa22jNHLjxg17e/vPnz/Ly8uvXr2aWkw/ffpUSkrqy5cve/fudXR0lJeXp900AHC53IMHD/72229cLtfAwODkyZMjRowoKio6duzY3r177969e/fuXTMzMycnpx9//FEYExA9osmmE4uDThCxuCiJh7A2NcqKSrCHEADat4cvXyA3F9hsoYzv4uKSkpIycOBAXL2C0FgQoPCS8JM5Jz9d/hR2JwwA5OTkFixYYGlpOWXKlHbt2mlqagKArq6ut7f3Tz/9tHbt2mHDhvXt21fcExcuXG4mi6WCELeoiKOhsVRFxZrHK0aojqokMjPkwbS8jiFMTaFXL+jZE+7fB2VluHMHGukekxBBCAAODg6hoaEXLlxzdFwtejFlZQUnTsC7d3DwIJw6BYmJkJQEiYmQmAgFBRAdDdHRAAA9ex59+/YMACgoKBgYGBgKEBkZefr0aSUlpYsXL5KLGYHQkqndIyEnJyc/P58uQYgQ8vHx2bp16/3793v16lWnUVooLCzcuHGjt7c3/BMZ1a1bN2zl119/9fPzA4CSkpJNmzYdPnx448aN9vb2ghq1+cTExCxcuDA6OprJZNrb2x88eBAXIGnXrp2Tk5ODg4OHh4e7u3tkZOTkyZNHmpvfcXGRsrCgcQJiobZouXfvnqurq4+PD37/hWFF7IJQNNl9RBDWpu20nQAAdXUhCsLXr197eHiwWCwvLy/iHqyflKoUbWltGca/jtrP3M+nc0+fzj2dWJkIAEYjjH744YdPnz7t3Llz7dq1Hz9+rDHCjBkzli5d6uPjM2fOnGfPntF76WlppKdvzc8P7tLltIbGYnykQ4dv+EV/qnegvn2hqeJZEorKYKytrSdOvPrwYWh4uIgsIvSf5pDu7lBWBp07w8KFsHMnBAbCs2eQnw85OfD4MZw5A9u2gZlZhyFDhmhoaJSVlb1+/frSpUt//PGHg4PD2LFjDxw4AAA+Pj5GjYz6JRAIIqaGNnNxcTl+/HiHDh1oGTwlJcXCwsLBwSEnJ+eiQNloHI8aHx9PixUAuHnzZu/evb29veXl5d3c3CIiIrAaCQ0NNTY29vPzk5eX37Nnz+XLlwcNGpSRkbFmzZouXbps27YNr+ybCZfL3bt376BBg6Kjo/X19cPCwry8vLAapFBVVXVxcUlNTXV3d9fS0vqRy5UaNw7694fz5+vuzNtKyM3Nhf+Kli1btoSHhw8YMMDDw4PqxNVMxo4dGxgYuHDhQqjVk1A0iEWRkqIytRFLyKi4PISDBv3ev79Ffn4U7SPz+fxly5ZxudxVq1YNGDCA9vFbO+ncdPtU+3EJ45anLs+rzlvzaU1aVRoAVKGqq4VXZybP7PKmi3O6c2Jlora0tlNHpytGV6ZOnQr/XNrqxMPDA3f+WL9+veheicipqkrLzfXn88vk5fuIcx5IgnBxQQBozhwRmdu3D1lbo/z8pjy3oKAgKioqKCho586dCxcuNDc3X758+bVr1+ieY1sEnzj27dsnVCseHh4AsHLlSnxTRkYGACoqKoRqVJBjx44BgIODA76JV9LFxcVCNdqzZ08AiI2NFaqVlg/umGRsbEzvsHw+38vLCy/ZcdcZwePy8vJdunRhMpk2NjZv375tjqGCggJ7e3t8CRg2bNj79+9rHx86dOi7d++op3A4HFNTU3yXhoaGq6trftPOfQghhF69eoVXVAwGw97evqSk5LtPKS4uzvPwQJqaCAABIBMTdOUK4vObPAexUFRU5ObmJi8vz2azhwwZkpqaio/n5eUJfiLN/Hxrg8WYkpISvcPWz59//gkAK1aswDfxBr+wz1GDBw8GgMePHwvVSusiKCgIAGbOnIlv6urqAgD13RMSOO/33r17QrVSm+nTpwPAhQsXaB/Z09MTANhsdkFBAe2Dt3aq+dV94/qGFoYihO4V34spi5maNPVW4a1f037VeKUBzwGeg+wL2ZkfZoYWhvIQDz9r5cqVAODh4YEQSklJAYBOnTrVGPn169c4WyEoKIjeOePGBKampvQO2wRSU1dGRcGHD3PFOw2JEoRpaYjFQjIyKDNT6Lbu3UNSUojBQNevC90WoVEQQSg8iCDE4OAWXV1dGsfEjkEsCWxsbLKysihb48aNw8d79OiBN1NZLNacOXOa9kHcvHkT97PBjsHq6mp8PDQ0FK8U5eTkBI8LEhERMXr0aDwZHNWZl5fXKOtcLtfNzQ3/XvT19f/3v/81bvalpcjdHWlrf5WFffogX19U11RbGgUFBdu3b8fVevCbDwCqqqonTpzg/yNrr1+/jj+aej6CppGamgoAOjo6dA3YEHbt2gUAmzdvRghxuVz8veULWcN3794dAOLi4oRqpXWB6zfOnz8f38SxDJlCXifh/SPRK/OlS5cCgJeXF73DZmZm4YKFwcHB9I4sGTwvfT4hYYLgkalJU50/O2Mp2Cu2l9sXtyxuVo1nNUQQon+WW6qqqikpKTTOuYUIwqqqLy9eyEdFMcrKXot3JhIlCBFCP/6IAJCQtQDKyEBsNgJAW7YI1xChCdQWhBkZGX/88Qftao3H41GLG7ELQj6fz+Px6DWRlJR08OBBwSNEEGIiIiIAQFpaOjg4uPlvu6BjsEOHDufPn6fuCg4OxuGpampqfn5+CKGUlBRHR0cc/cVkMq2trZ8/f95AQ9gBiAtWCToACwsLqeNDhgz5rnsqIiJi7NixWNgoKys7OTnl5uY2ZAIxMTGNdQzWTVkZOnwY6eh8lYW9e6O4OHTjBho7FllZoYkTUXQ0QggNHfrvU4YNa6KtZlNcXOzm5oY/RwAwMzO7e/duRkYGdmUAwPjx46mFjuBn1K9fvxcvXjR/Arm5ucuXL5eXl1dVVX39WnRrjo0bNwLAnj17EEJ8Pj8vLy85OVnYRnEb68+fPwvbUCvCy8sLAJYuXYpv4mpGhYWFQjWKq4BE41+iCNm0aRMA7N69m95h58/nmZu/mDZtAb3DSgyhhaELUhYIHpmaNDWqNGpl6srnpd+8QgkKQvTfNZUgfD5/ypQpAGBubk7jNlkLEYRpaRuioiAxcZp4p4EkTxBeu4YAkIEBontt/C9VVWj4cASAxoyheWO6pKQkICCAzhHbJLUF4aJFiwDA0NBQeHt7YheE9FJSUuLq6opVh2DMDxGEVVVVLi4ugjkPvXr1Onv2bJOvUunp6dbW1rUdg+np6VRRTWtr6xoL3NTUVKrsJ4PBsLa2fvbsWf2GQkNDKcfggQMHKB1769at7zoG6yQiIoKauZKSkqOjY0ZGxrceLOgY1NPTCwsLa6CV+qisRL6+yMAAqaujuDjUt+/XCP7ERNSrFyovR4aG/z64a1fRx5diKUh1QjIzM7tz547gA4KDg3EhIgUFBTc3N+pDCQ0N7dSpE950cHJyqqysbNoEcnNznZ2dsQDANTBkZWV37NhRVVXV3NfWAHAZxqNHj4rAFgX+UTR9r0ESwQ6WVatW4Zs5OTlJSUnCdtX26NEDAGgPfv4uf/zxBwCsW7eOOsLlcps55v37iMFA8vIoMbGZI0ksceVxw979Z9NtatLUxIrvvF81BGE95OXl4VPitm3bmjVRAVqCIKyuyo2OVo6KgtLS71zBRYCkCUIeD3XpggDQfy+7dPLLLwgA6eqirJre7+ZSXV29Y8cOmgdte8ycOVPw4ocQunv3bp8+X1N1x4wZ8+rVK3otlpaWSklJAUB2dja9I9fD1q1bAWDSpEn0Dsvn84ODg/GZl8Fg2NjYfPz4Ed/F5XKxcggPD6fXaGtB0MG1ZMkSb29vQ0ND/L3S19f38vJq7MpD0AEoGONEHVdVVa0n9ikzM9PJyYmqvWZhYfHkyZNvPRgvCgUdgI11DNZJZGQkJQsVFRUdHR3T09NrPOb169cmJiaUY5DmwObKShQdjU6dQq6u/x6cMQM9eYK6dEGRkV//6eqKUhAWFxe7u7vPwN1pAUaOHPmtZKrMzEw7OztKMVJu29LSUicnJyaTCQB9+vT5ruCvAU5WpCJUzczMQkNDmzNgY8nKysIFcp2dnYVkIicnx9XV9ddff6WOVFVVAYCUlJSw1U7rYt++fQCwfv164Zl49OjRzp07BY906dIFAD58+CA8o3Vy8uRJAFi4cCG+mZycrKioaGFh4erqyuFwysvLGztgZSXq2RMBoF276J6rZGERb3Eo89Cnqk8RxRGplakNEYT41Dd//vyG+Kvv37/PYrGYTCYt+4mVlZUHDx4EgN69ezd/tKbj6sodY5J5a5E45/APkiYIEUI7diAAZGMjlMEDAhAAkpVFT58KZXxCc0hNTcXnlx9++KG2mPHy8sK5E1JSUvb29lk0CfqQkJAuXbpISUnJyMhoaGi4u7vTGNJQJ7m5uY6OjiwWC3eztLCwePPmDS0jP336dNiwYXgFOXDgwMjISOouSlSrqKgoKCi4uro24craevmWg6uqqsrX15dqFdClSxd3d/eGOIq/5QBMT0+fPHkyPj5p0qSGRL5lZWW5urpSTe0sLCwePXpU+2E8Hu/MmTPUl/P27dtY9tOSrhYdHW1jY4O1JZaFeOb4fcOVBmlzDNaJuzvav//fm4sWIQ4H6eig33//+k9dXTSCsKSkxMPDo1evXmw2u1evXuPHj29InuTVq1fxz7lGYidV+lVKSsrJyakhX63aUlBwAk0YsLFgnYa/kCoqKngXoKioiF4TW7ZswVHW0tLS1B5EdnY2ALRv355GWxLAjh07AMDOzk4Yg7979w7/9hkMhuCZh81mA0Dt7SFhExISAgA//vgjvvn333+DAPLy8qNGjTp06Obdu6i0tEED/v47AkDduiERBgC1Ssp4ZQcyDyxOWfxb+m+fqz4fzzmew82p5/H37t1TVFTU1tbGkQsWFhbu7u5fvnyp5yl4H/yHH37Iyalv5HooLy8PCQmxt7fHq0F1dXV8ghJN0ERNioqQmhoCQC1jk10CBWFGBpKWRlJSiPYkgpiYCgUFBIA8PWkemdBMiouLN2/ejEMclZSUxowZg9egSkpKu3fvpqRLXl6eo6Mj9uapqam5ubk1ORYLIfTs2TMzMzN8mTEyMurXr1+dUopGqqqqDh48iJd60tLSY8aMwdXVpaWl165d25zSZ58/f7a3t8feA21tbS8vLyp6LSEhgZIoenp6Q4cOxX8bGhpeuXKFplfWovmug4vH4wUHB+NqFgDQuXNnd3f3egRzRkYG/hA1NDQCAwOp48HBwTi8sH7HYJ1kZ2e7urpS1fZxolqdjywtLXV0dMTizdTUlMbyG8+ePZs8eTIeWV5efu7cucbGxvh9W7lypXCj+G7dQnMFSrQNGIA+fRJxyGhpaamPj0/fvn3ZbDabzf7xxx9v377d8Kfn5+dThUaHDBlCfS4lJSWOjo74tzlgwIB6vNBYCgomK9apwMvKypycnHAEqZGREY1lP7Kzszdt2qSkpIQ/9EmTJq1atQqfirW1tWk5XeTm5gp+z2t4xXEMmLa2dvMNSRJJSUm4FpSNjQ2NtWSysrKo66mCgoKTkxN1DeJyuViuNzC7mEYiIyMBYKhA/vCXL19CQkKcnJxMTEzw78jU9CIAkpJCJibI0REFB6Nv1cZKTETy8ojBQN84mxKayJUrV/CCbejQoaNGjaKaOkpJSY0bN87T07NOZcjlcvGe9fTp0xtlrri4+Ny5czNnzsRnJ0y/fv2mTZuG93lHjRpFl5OgEezZgwDQyJGitvsNJFAQIoTs7dGGDTTXGs3Pz+/evfvIkW52dsJdVSQlJR05ckSoJiSJb4U4Ut5CANDV1fX19aWe8u7dO0tLS3xX9+7drze+UGx2djb20eEdJsorGBIS0rlzZ8rtQ28FBQ6HQ/WopLyCOTk51Ezat2/fBP9kZWWlu7s7vnjLyMg4OjpSe/mCmYSKioqUV7BGCG5MTAyNL7NFweVyjx07RpXErL+EOpaFOM0SADp16uTu7l5WVlbng3/++WdBB2BGRgZOmgcAKyurT58+NW3C2Dkj6B0KCQkRfEB4eDgOc5WWlnZ1dRWGNzsmJsbOzo7FYklJSTEYjC5dunxLmtIJn48mTEA7d6I7d9AvvyBHR4QQvYKwhqPv6dOnfn5++DfeTCkoyI0bN3BgNs4epPatHz582KNHj99//73OZ9WuW/NdZ+yDBw/wFsbBYcPQ+vXoG1/UBoK/eII67ek/UTRv3rzBrSCwIGny1n4Nz6eFhYWglM3KynJycpKXl1dTU2MwGFZWVmlpac15RZIEn8///fff8ZlcXV39+PHjzQypxd83ykNrb28v6AnkcDjGxsbS0tKbN29uzpZr03j37h3eFKjzC5CTk3Pp0qXNm/NMTBCL9bUoFQBSVER1znTSJASA/ok/JdBDQEAAzsNfvnw53nrOzc319fW1trbGV1sAYDKZZmZmbm5uif9N3ExNTcXbpp4N8MyUlpaGhITY2dkJ6sBevXq5urpSwflRUVF4AfnDDz/Uk3NBP+XlX6tTNvViQTuSKQg9PdHkyV+v/gcOoNDQ5g7I5/NxfFf//v3Ly4UbNzB9+vR169YJO+xQMnj27JlgiOODBw9qPCAsLAz7KPAO0MuXL6m7QkJCDAwMqLVFA/0kVVVV7u7uOBpKWlra0dGxhl+utLTUzc0Nn33wpmnzM6bi4+OpNK2uXbvWLo0THR2Nmz4BQM+ePUMb/I0PCQnR09OjFGxSUhI+zufzfX19cb0+BoNhZ2dXo16I8EJwWw7v3r2bOHGirq5uv379Gp75xuPxQkJCqLbFHTt2dHNzqy0LBQNUmuMYrJPanqKQkBDBnLS+ffsKu/pfWFgYti7sdmf/wuWiCxfQgQOIw/l6RLBv1blzzRn75MmTK1eu/OOPP7CfMygoaP78+VeuXBk/fnxUVFSfPn0oKXj//v3mGEL/LTTat29fqopsWVlZ7bimOkuYNtBQeXn571u28Dp2/BoS17Swpdzct3v3Uv3uraysntZKqOByue7u7rg1jpaWVmN7xNUOghXcmsnMzFy3bh3OpGUwGIMHD8bnZzU1tdOnTzflFUkoiYmJEydOpN7Dpm3kVVZWenl5aWpqUleN+Ph46t6IiAjqity1a1eRLq//QTA1ms1m29jYeHl5vXnzprYGLi5GHA5ydUUWFmjcuDqGCgpCAKh9e1F0Mms7HDlyBF+GnJycat+bn58fHBxsZ2eHTxeCEo7ql3v+/HkAkJOT+1Y9CEpe4vAEQXmZkJBQ+/FZWVmjRo0CAFlZ2RMnTtD4Yuvj8GEEgAYMaDnddCVTELq5oV69EP5YnZ1R8zuU/v777/gCQ62YCeIlIyNj69atVIijn5/ft7Y8eTyer68vvoAxmUw7OzsqZqa2uqs/s5nD4VD+HwsLi3qKbaakpODaNkwmM3rUqKYvRvPz0Zo1a8aMwWrhwIED9US611B39Wfzx8XFTZgwoU4N+eTJEyoudNCgQQ8fPvzWIDk5OStWrMAhQ+rq6mfOnGl+PbeWAF6/durUic1mm5qa1vMOfAs+nx8SEjJw4ED8Nmpqarq5uZXWylnJyMiYOnUqtZJusmOwTgoKCn7//XeqxCVeT8vKyu7atUsEH1NycjK2GxERIWxbIiA1NXXLli0XL16srKxMSUmZN28e3mA+efLkX3/9NW3aNGtr6yZ7Bevk/v372Jf7rWS/75YwbSgvX6L+/REAYjCQvT1qeLJfURFyc0OqqnwGY2DPnjVcdrVJTEzEqy4AsLGxaUgJrvrzIXGYtGD2bFRUFPrvz2rChAlUJjkBIRQSEoKrDUtJSQnGg3wXHAGhr6+P39hhw4YJ/rTj4uJsbGzwXRoaGm5ubqKsuV2DhISESZMmUf5qjLa29uzZs48ePRoXl1p7sVDn8mHUKASAfHxEMOW2gpubG964+eOPP+p/JOXcozabKGUYGxv7888/A4CRkZHgZmt2djbWgVQZcBaLZWZm5u7uXmc2vq+vLxXHzuVynZyc8LPs7e2F7tmuqkKdOyMAdPmycA01BokVhN7eqH9/lJ2NnJ3R33+jyMim7/HcuXMHlza6ceMGrdMkNIWqqiofH5+uXbuy2WxLS8stW7Y0xHWTn5/v5OSEoxFUVVUFswfT09MXLlyItaVgCzhB3r17Z2VlhU8WDY8yvX//vtucOV9DUszNUaNailVXI09P1KEDAshv3371L780xAVXVlZGRfLUiP+swaRJkwCgQ4cOnp6elDv606dPdnZ2eG9VR0fH19e3IZFFb9++tbS0HDVqFJvNHj58eBNXpS0GHFHMZrO1tbU3bNjQTAcvh8MZNGgQtU5ydXWlXMrBwcHq6uoAoKKiQnsbZQpc7lJLS2v+/PnGxsa09LVrCHFxcfhVC++liZIvX74UFRUlJSWlp6e7ubnt2bNn9+7dUVFRDg4Ov/32m5Baugk6dXv37k153vBn2rFjx+ZKQYqqKuTmhmRkEADq0uVfF+u3yMtDLi5IReXryW3ChKKGJSLilps4gEJTU1Mwkr8G9edD1paCtYumfqulB6GgoIBKNNDR0fnWVU8QDodDJckbGRkJRqmkpaXZ29vj0RQVFZ2cnITd4bCB8Hi8N2/eeHl52djY4GAWzNChQe3aIQsL5OaGIiJQjS3W0FBERcNcvYp8fFqO/6Z1w+fzf/31VyzSTp482fAn4jIwdnZ2ggpfT08P74X98ssvqamp7u7uFhYWeG9aUAfWX6LmwYMHNcIZ/P39cd+a4cOH19NFiQYKC9Hq1cjUtEV9vSRWEJ45g/7+Gy1ejJyd0fHjXy9bsrJIXx9ZWyMnJ+TlhTgclJT0nY6FHz9+xBeV7du3i2r6CCGUlJS0e/duUju7Brdv3x4yZAiOzpo/fz7VzbmBvH//HgshAOjWrdu1a9eou54+feqI847+S15eHqUkm1KHhsdDvr4IB2UxmcjODtV7hvrKvXuob9+v39qRI1Ejo/vqqRBD8fbt2zVr1uTjvm3/KEm8UJOXl3dycmpsVcBbt24NHToUfzSLFi0SQQdq2uFyuX/99Vfnzp3ZbPagQYNodG1xOBxTU1P8xVNXV1+/fj0VA2xpaSmCZKeSkpKqqipR+m+fP3+OX2CdP6vWDpfLDQgIOHz48P79+w8dOiRUW1Syn5SU1PLly/ft2ycoBWvkiDaLmBg0cOBXV6GdHaqzHAj2CuLKeADIzAw1oIZqDT58+DB27Fj8Eqytret0jF+5coV6jYJBsIIlTOG/yYq1+fLly08//YQfOXz4cCrkjIAQevHiBZXbaW1t/a2LaVFR0ciRI/HDunTp4u/vT11N8MURL6BxJmH9628xwufzY2Njjx07Nnv27L5906nUQQCkqoqsrdG+fejRI8Tlot69EVWvpFcvsU5agqiurl67dq2VlZWMjEyT20FXVFRcu3Zt8eLFeCOVAm9h46XL1KlT/f39m1Ng7/nz57gShI6OTp31uptFaSn69Vc0cSKaMAEdOEBzK/NmI8mCECFkZYUsLZGHBxo48N9LWI1/FhYP+vTpM3Xq1PXr13t6enI4nOTkZOwzKS8vx0Ff48aNE2VS3/79+zt06LBu3braMWZtlvj4+NmzZ2O9MWLEiIZUcv8WHA4HN8iCens2CKbJ4VjTpqfJFRcjV1ckK/s1e93V9ZsVrNPSkJ0dYjAQAPrhB+Tr2+QNpHp6SNQAd86gVgZN1nLYedutWzc2m92pUydnZ2d6a803n+rq6rCwsHfv3uE1TVlZ2a5du+bOnbt3797k5GQrKyvsGNy4cSPNvfIQQghFRETgWn9Yq6uqqkpwjhOu9QcAY8eOFfdc6ITL5VKxjlwud9asWbT3Na1NaWnp2rVrcaAKflfNzc2FUqqHy0W7diFZWcRkogcP0IULaORIZGmJxo5Fjx+ja9eQqip14US1crYbDnYVYl1XZ+osn89fvny5YMvTeurW1E9ISAgubT958uS//vqL5OdT8Hg86lPAzYTq3O60sLBQV1d3c3OjKifjUmTYf4truSW2qpbtCQno5Em0YAHS0/vPanDcOGRsjBwcvsbxEUFIC5WVlYsXL2az2YaGhs1PsUYIVVdXR0REODo6ysjI4I5f1tbWvr6+dLmms7Ozx4wZAwCysrI+9EYM//rr1/ZI1dVozhzk70/n4M1GwgVhYiKSk/s3hzAnBz1+jAIC0PbtaN48NHQo6tABmZl5Qi1kZGS6d++Ow+W1tbVnzpwputIICGVlZYkx/l4s8Pn8U6dOL0C7FwAAIABJREFUUTdTUlI2b968atWqyMjI/Px8Z2fnH374gc1m9+zZ08fHp/lXdJw9SPVsqF0b5u7du1Q1mtGjR9Oz5nv//mvNMlzCISkJJSQgR0dka4v270eVlcjbG8nJfRWNO3Y0s+4f+qcEKy5aWKMxI+b58+fm5ub4Zfbv35+Wk/WXL182bNiAPy8jIyP8edX4fAsKCv7880/KQ3v16tVVq1b9+eefwnZh3bt3z9HR0dXVFRdUuHXr1pUrV6qrqzdu3Ojn5zdkyJBBgwaFC7kjEIfDYTKZDAZDslOb7ty5g79XWlpa4p4LnaSlpbm4uEREREybNs3GxqZRsU/N5NKlSwCgpKQk9Kjs2Fjk4YGSk1H//gjvjHz8iHr2RO/fIxkZZGZGVw1+wa6blpaW37rI1t9qoiFkZ2c7OTnh/cTJkye3LvUibNLT06mK3MbGxrW3DlNSUqilNs4kpDYQLSwsqKJHrZT0dBQcjBwdkYkJcnFBxsYoOxv164eKi4kgpIHS0lJbW1s2m929e/facd3NIS0tDYdueXt7N+Hp7969q6cig7BSCvX1/41RjopCU6fSMyxNSJogLC1Ffn7o8WNE1fu4fBnVHydSXl7+5s2b4OBgNzc3e3t7CwsLfX19vBErLy8/fvx43KLAxcVFBPNvsxw+fHjJkiX79+/HUbJOTk7Pnj1LS0sbOnTo8ePHscfJxcWF3uQEwZ4NVPeIhIQEKjnewMCgyeEN3+TOHdS7N+rZE6Wlod690bNnqLAQ7d+PZs9GDx8iJhPZ2KBGhsLWT53dI+p87TQajY6Otra2xiuwFStW7NmzZ8mSJVQ/lR07dri6uuIa+o8ePcLdsdzc3L6baN5MioqK9u/f7+LiwufzqWJi5eXl9vb24eHhcXFxonFp4p31vG+1vpIIrl27Ru2vNaR8COG7vHr1CgB69+4tInsnTqBt2/69aWODHj9GQpBSNZJpBRMlCgsLa7SaaE7hyjt37gwYMIDNZnfp0mX//v3iaUXdUrl79y6OTMZlpev8zeJ+ElTUSWtPF68Nj4eMjRFCyNMTbdpEBGFzKSgo+PHHH9lstrGxcT1F+JrGmjVr8FexCfVyeTxenz595OXl6z+fnDlzBpcvHjZsmGBjlUaQl4euXUNbt6JRo9C1a0hX99+7PnxAI0Y0ZUyhIWmCcN48BIA2b27uOEuWLFFQUAgICEAI3b9/HwDYbLborx8JCQlbt26tp8O1xHDz5s1jx46dO3fu06dPgjuUP/30U1JS0tatW+ssFkwLgl4yXV1dXKdYWVlZiHXSqqpQcjI6eBAJ6p9evVBeHhIo4U0viYmJVOW9jh074nRBGRmZ9evXNyfgvn5u3749aNCgsLAwf3//I0eOhIWFxcfH44YHHA4HC0IXFxfsKiwsLBwzZoyQZkLB4/FwvCgupRAfH29nZzd9+nRRNlDGoQeSXbIYVwbHCNvp2kYIDw8HADMzMxHZO3z4PyeoRYuQ0ARARkbG9OnT8bdlxIgRCQkJ9XcdbDKFhYUbNmzQ1tZms9ljx459/fp188eUGMrKylxdXfEVsH379oLi/NGjR1Rno86dO9eZly4ZYEHI46ERIxCbLe7ZtGYyMzPHjBnDZrMHDx5cf83zJpCbm4vXMHJyck1emdfZyKcGL168wP5wbW3thtYb//z5X6czk/lvRPK6dWjAAESlTF++jOztmzZzISFRgtDDAwEgJSX0jaSwRjB37lwA+O233/BN3BD80qVLzR23wcTGxo4bN05TU3PDhg1U5Q8JBv8sq6qqUlJSqGpyCQkJVlZWornwhISE6Orqamlp1dl2TyisXYsE3Y+Wlkj4q5OwsDAjIyMVFRUpKan6O2fQBRbV1OcbGxuL9+QoQbhixQpqqWdiYiLs+dRJQEDAnj17RGbOxMQEAHCJfEnFz8+PEoTHjh0T93QkgatXrwKAlZWViOzduYNsbb/+zeejfv1Q0/bIG0xAQAAu4SYvL081kp4wYUIT+r7Uz//+979BgwbhwJOdO3fis9PFixepB6Snp3t4eBw5ckTy2qt+l/j4eAsLC0qcX7t2zcbGBtftEHs/CREwfPjXP6Ki/uPOITSK1NRUXGRuxIgRTfSt1QtuBQcApqamtA9eg+zsbFwES0ZGxt3dvfYDysvLIyIi9u7dO3ny5FITk/+kpcrJoeHD0caN6MoVlJ2N/v4bjR+PIiPRzZtowADUsPbXIkNyBOGjR1+LZvv50TAa3ovV0tLClwp3d3cAmDhxIg1DN4yMjIyAgADJPvPWT1pamqWlpSjrwt24cQMAhgwZIiJ7Bw58TS/GGBkhkSj/xMREvN0lAlv1QAlCZ2fnmzdvIoSKi4tHjRolsgkEBweHhoaGhYWVl5c7OzuLMhkMr7c43y3u35rx8fGhBOHKlSvFPR1J4MyZMwAwe/ZsEdnj89HkyWjrVhQSghYuRHV1kaadzMzMWbNmtWvXTkZGpkbXQXopLi52cnLCrsLFixfv3r175cqVeOeiurp67ty59+7dCwkJmTRpkpAm0JLBKd+4oBpuYqSsrLx9+3ZhlNoitGoCAwOpvz9//rxjx44tW7bExMRs2rSJzWZbWVkJIzOivLxcS0sLX1yWL19O+/i1EUwptLOzKysr+/LlS0hIiKurq4WFBc7HwcSZm6OOHZG1NXJ1RRwOqh3f9/Qp2rED7duHWl4ldgkRhF++IB0dBIDWrqVtzN69ewPAhQsXEEL5+fkKCgoMBkN4gYsEzJcvX168ePHgwYOpU6fGCy1+sk7u3r0LAKNHjxaRvfR01Ls3evsWcbnI0xPNnCkasykpKQDQqVMn0ZirQXJyclxcHBIQhOHh4XZ2dhUVFUeOHNm1a5fIZnLy5MmSkpJ9+/bZ29sfO3ZMlLUHcZIq/empLYnDhw9T10gRRAK3BY4ePQoAy5YtE51JHg9dv46OHGlOQdHGEh8fj+MSRWDryZMn5ubmjx49unz58vbt21+8eJGenk7VtsnOzh45cqQIptEyycnJsbe39/T0XLVqVWaT+zgTJJf9+/c7ODgcPHgQ31y5ciVOxR86dCgO+S4pKRGG3b/++ou6uDShosz//ve/WbNm7d69+9q1a41q7ebn54ebrNToe8FisYyNjZcvX+7n55dBd3CsKJGC1k91dfWqVdmfP7PNzcHNjbZhly5dunr1ai8vr+nTp6uqqtrY2Pj6+p48eXL37t202WgYCQkJPj4+GzZsEGyuKqmUlJRkZGTcunVLU1Nz//79ALBy5co+ffqIe15CgM2GwEDYtw9ycmDgQDh9WtwTEgUFBQXFxcVeXl4vXrwAADc3t02bNn3+/HnZsmXdunVbv369yGayaNEiANiwYYPILFLgojL5+fmiNy0yKioqqL9jY2PFOBOJobCwEAAEuzMLHSYTrKxEZw4A/nmZNZZcQmLw4MFhYWFSUlLZ2dlWVlalpaWxsbHl5eW6uroeHh6nTp3au3evCKbRMlFXV/fy8hL3LAgtl549ewJAt27dPn/+nJ2d/eeff+LjGhoapaWllEuNXng83oEDB6SlpblcLgAMGDCgsSN0797d2to6Jibm9OnTVGPqhmBnZ2dkZLR69WqE0KtXr/r27Tt8+HAzMzMzM7P27ds3dhotEEkQhHv27HnzJmjSJC8fn+HS0rQNu2DBgs2bN3M4nISEhK5duzo4OPj6+h4/fpzKuhY21dXVly5d8vLyevPmzYIFC0RgsSVgYGBgYGBgJfJViHjo3RtOnhT3JERKv379AICq4oOxtbW1tbUV04zEQFsQhOXl5dTfmZmZOTk5OD2M0GSwUqJ6sksqIta9UlJSAIA3W1VVVc3MzPBxR0fHefPmWVtbjx49WprGhQWBICmMGjXKysqqrKzs06dP2dnZ+OCHDx+qq6s7duwoJKM+Pj7l5eUaGho8Hq+0tBSH8jUKNps9b968plkfMGBAREREVlaWuro6LtIuSTDFPYHmcuvWLU9Pz5KS4t9+k2az6RxZRUVl1qxZCKETJ04AwNChQ/v27cvj8TgcDp1mvg2fzz979uySJUtSU1P37t3bFtyDBEJboC0IQkEPIQDExcWJayYSgxg8hOJAvC8zNzc3PT399u3bAIArzhMIhDrBPxAFBYVu3bqNGzcOAL58+bJq1aqDBw/iKkTCwN/fHw/OYrEMDAxE456pgaampuSpQWjtgvDDhw+Ojo4IIRcXF1NTU9rHd3BwAIATJ05UVlYCwKZNm1RUVASr5wkVGRmZS5cu2dra4uabBAJBMujcuXOfPn1ws1NJRdBDCCRqlA6UlJSGDRumo6Mj7okIF/EKwpiYmBcvXrx7987Ozm7x4sWbNm0i7kECoX6ysrLev3//+PHj5cuXHzx4sEePHkIydO/eveTkZOomLv7ZKKKjo3HDJ1rnJSG04pDR0tLSxYsXFxcXT5kyZcmSJcIwYWpqam5urqioeOfOnUmTJllbW7u4uERGRn748AF3EiMQCITGoqiomJOTk5ubK+6JCBHiIaSdzMzM5ORkRUVFcU9EuBQVFYH4BOHo0aPFYpdAaL1kZ2cnJSWdP39eRkbGxcUFAJydnYVR+uHIkSOCN42NjRs7QlBQUHBwcE5OTp8+fWxsbKju9gRo1R7CdevWxcfHd+/e/cCBA8KzsmTJklevXp0+fRoAlJSUpkyZgv4p/00gEAhNALfbluyQUeIhpB2slHAbAAmmjUTGEggSg5GR0eTJk/39/c/9gzDU4KtXryIjIwWPNNbK8uXL7ezskpOT09LSDAwMiPO/Bq1AED569Ejw5osXLzgcTlFR0bRp09hstre3t1AD/WfMmKGsrBwZGYlrYS9cuBAAzp07h4NICZKEjIzMkCFD9PT0xD0R4cJisYYMGSKZhVtbCXixixe+kkoNDyERhM2nTQlCia+dQyAQGoVgtwkAYLFYvXr1atQIeXl5VVVVAKCqqtquXbtu3brROb/WT0sXhGfOnLl69aqnp2dZWRkA7Nix48KFC8nJyVOnTh0+fPjDhw+7du0q1AkoKipOmTIFAAIDAwHAyMjI2Ng4Pz//+vXrQrVLEAsfP37Eqy7J5uPHj5mZmeKeRdulLQjCGh7CrKwsqgwdoWkUFxdDGxCEKioqFhYWEp8qSSAQGk5KSkpoaKjgka5du+KugI3izZs3T548efLkSVZWFn2zkxBauiAcN25cfn5+p06d5OTkSkpKVq9evWvXLnt7e1VV1czMTNHUF8JewaCgILzEsbOzAwB/f38RmCYQCJIHDhktKCgQ90SESA1BCCSNsNngvSqJd519+vQpNjZW4lMlCQRCwzly5AiPxxM80rQop4cPH4aGhoaGhn78+JGmqUkOLV0QlpaW7ty5s0OHDmlpaTt27GjXrl14ePiiRYuMjIxEVtalV69e/fr1KywsvHHjBgBMmzZNWVn5yZMn7969E80ECASCJKGsrMxkMktKSmpc4SSJGiGjQKJGmwdCqLS0FACUlJTEPRfhgh2hEq97CQRCA+HxeJcvX65xsAkdCAHA3t7e1dXV1dV10KBBdExNomjpglBfX19dXX3QoEGdO3feu3cvAAwePHjz5s3R0dEpKSkim4agV1BBQWHGjBkAEBAQILIJEAgEiYHJZCopKfH5/JKSEnHPRVjUFoSpqalimYlkUFpayuPxFBQUcCN1CaaNRMYSCIQGwmKxJkyYUOOg8JpbtFlauiAUBCGUmpoqJyfXrVu3rl27pqWlicz01KlTVVRUnj59ir2CdnZ2ixYtwiqRQCAQGovER40uXboUN1qUkZHp2LHjwIED169fL+5JtWLw3kFbkEltpHYOgUBoOAcPHly6dKm9vX2nTp0AQFdXNzAwcP78+Y3K3lqyZAl+OgDY2to2tiaNxNNq9hqTk5MPHDjQs2fPBw8eqKiolJeXDx48WGTW5eXlp0+ffurUqbCwsB49evTs2dPBwYH6YmHKysr4fL7Ex/MQCITms2PHjqKiIg0NDXFPRFjY29tPmzZNQUGBJIPRgrKy8v79+7HGlmxIyCiBQKjBhQsXPn782KtXr5s3b2ZlZf3vf/8bOXKkrq7u7NmzBw8e3L1794YMMm7cOOpvc3NzoU22tdJqBKGenh6uOWtvb19eXi76q8WyZctmz57du3fvqqqqp0+fRkZGjhgxYujQofjewsLC6dOnq6urBwcHi3hiBAKh1dG+ffuYmJiEhIR+/frhI9XV1bNnz87MzAwPDxfv3OiiQ4cO4p6C5JCVlZWWlmZra4tvpqWlrV+/XkVFpayszMPDo3379uKdHo0QDyGBQKiBsbFxRUVFXl6elJTUy5cvly1bBgBZWVkIIRxuQ2g+rW+7UVpaWix7h7q6ujiHlcFgJCYmPnnyRPBbuH379iVLloh+VgQCodWBEEpMTORwOIKdJ7y9vS0tLduCC4jQBOLj4x88eHDr1i0ulwsAFy9enDhxore3d9++fW/duiXu2dFGdXV1eXk5i8USanthAoHQujAyMlqwYMGaNWtYLJaamhoAcDgcR0dHPT09SdoOEy9k8dFosCLduXMnABw7duzatWsPHz6sqqoaM2aMuKdGaBZMJpPFYkn8ipzBYLBYLIkvTdGSYTAYUlJShw8fZrFY169fDwgISEtLu3v37oIFC8Q9NUILRUFBYdu2bYqKiqmpqYcOHRo6dOiZM2euXbv2+vXr4cOHi3t2tIHjRZWUlBgMhrjnQiAQWhBMJpPJZCooKIwfPx4Axo0bFxQUJC8vf/fuXXFPTUIgi8KmMH36dPxHjx49Kioqpk2b5uvrK94pEZrPsGHDAgMDTU1NqSOZmZlxcXGjR48W46xoR0dH5/r169ra2vgmh8OhCkKamppqamqKb2ptCFypuHv37pWVlXw+f8mSJdu2bWOxWOKeF6GFgjNeTExMKioqbG1tU1JSDAwM3rx5k52dXVhYqKurK+4J0kNVVZWBgQGJFyUQCHVSWloaFhaWmZk5ZMgQHR2dDx8+zJ07V9yTkhBY27ZtE/ccWjEMBuPhw4dPnjy5c+fOlStX3r5927FjR1K5qJVy9uzZW7duJScnGxgYKCgo+Pn5+fv7//3334sWLRL31Ojkxo0bly9fTk5Obt++vYaGxocPH6qrqwHg0KFDFhYW6urq4p5g20JKSio+Pv7s2bPR0dF///33+/fvZWVlBw4cKO55EVooUlJSysrKGzdu3Lhx4/Tp07W0tAIDAy0tLcU9L3pQUlJSU1PDATgUr169unDhQv/+/cmOCYHQxqmqqkpISBg9erS/v//NmzdtbGxIeRi6YCCExD2HVsypU6d0dHSw/zozM3PVqlWkqEzrJTc39+DBg3369LGxsfny5YuOjg4AjBw58v79++KeGp1UVFQcOHBAWVnZ0dHx48ePnTt3BoDU1NStW7c2qoIzgRZwJuFPP/2Eb44aNerevXtinRGhRZOSknLr1q2SkpKsrKwZM2acPn169OjRNjY24p4XPXh7e8fExHTq1GnNmjXS0tIAUF5ePmvWrIKCghs3bpAi3gQCgSAkiCBsFjj9XUZGBgC4XG5aWpq+vr64J0VoItnZ2QwGIzs7m81m79u3b/fu3SCJgrC0tDQ3N7esrKxHjx7Lli3z9PQEAGdn53Hjxo0cOVLcs2tzFBcX83g8qkLV8+fPTUxMxDslQkumtLT0y5cv+vr69+7d+/Dhg7Gx8aBBg8Q9Kdp48+bN5cuXu3XrZmVllZaW1rNnz+3bt/fv39/b2zsoKIgIQgKBQBASRBASCPUheYKwNoWFhfPmzQsJCSGFHAgEghjJz89XUlIqKCioqKi4ffu2iYnJgQMH/P39ra2tiSAkEAgE4SHhBRUJhCbA5/MF+wFIMPn5+QDg5+c3b948ogYJBIJ4UVNTk5aW7tChg66u7sKFC9etWzdkyJCrV6/m5+c/evRI3LMjEAgEiYUIQgKhJgUFBc7Ozg8fPnRwcCgpKcH/i3tSQmHlypXV1dX37t2bNm2auOdCIBAIXykvL09OTl60aJGKikphYSGPx5PUkzCBQCC0BEjIKIFAIBAIhBZESkrK1atXV61ahW/+/PPPf/75J+lWTyAQCEKCCEICgUAgEAgEAoFAaKOQkFECgUAgEAgEAoFAaKMQQUggEAgEAoFAIBAIbRQiCAkEAoFAIBAIBAKhjUIEIYFAIBAIBAKBQCC0UYggJBAIBAKBQCAQCIQ2ChGEBAKBQCAQCAQCgdBGIYKQQCAQCAQCgUAgENooRBASCAQCgUAgEAgEQhuFCEICgUAgEAgEAoFAaKMQQUggEAgEAoFAIBAIbRQiCAkEAoFAIBAIBAKhjUIEIYFAIBAIBAKBQCC0UYggJBAIBAKBQCAQCIQ2ChGEBAKBQCAQCAQCgdBGIYKQQCAQCAQCgUAgENooRBASCAQCgUAgEAgEQhuFCEICgUAgEAgEwv/bu/Owqsq1j+M3MwpywIF5g6JgIo6ooeaQL2pp5JiKszllHT0OlZ20HPN41JNjzlpKmjhVauaQaY5pgBMmOCCwEVARBGSGvd8/doc85ZTsAVnfz+Ufay8X93OvzOvyt5+1ngeAQhEIAQAAAEChCIQAAAAAoFAEQgAAAABQKAIhAAAAACgUgRAAAAAAFIpACAAAAAAKRSAEAAAAAIUiEAIAAACAQhEIAQAAAEChCIQAAAAAoFAEQgAAAABQKAIhAAAAACgUgRAAAAAAFIpACAAAAAAKRSAEAAAAAIUiEAIAAACAQhEIAQAAAEChCIQAAAAAoFAEQgAAAABQKAIhAAAAACgUgRAAAAAAFIpACAAAAAAKRSAEAAAAAIUiEAIAAACAQhEIAQAAAEChCIQAAAAAoFAEQgAAAABQKAIhAAAAACgUgRAAAAAAFIpACAAAAAAKRSAEAAAAAIUiEAIAAACAQhEIAQAAAEChCIQAAAAAoFAEQgAAAABQKAIhAAAAACgUgRAAAAAAFIpACAAAAAAKRSAEAAAAAIUiEAIAAACAQhEIAQAAAEChCIQAAAAAoFAEQgAAAABQKAIhAAAAACgUgRAAAAAAFIpACAAAAAAKRSAEAAAAAIUiEAIAAACAQhEIAQAAAEChCIQAAAAAoFAEQgAAAABQKAIhAAAAACgUgRAAAAAAFIpACAAAAAAKRSAEAAAAAIUiEAIAAACAQhEIAQAAAEChCIQAAAAAoFAEQgAAAABQKAIhAAAAACgUgRAAAAAAFIpACAAAAAAKRSAEAAAAAIUiEAIAAACAQhEIAQAAAEChCIQAAAAAoFAEQgAAAABQKEtTNwA8x+Lj493d3a2trUWkuLj45s2buvMeHh6WlvzlAgAAQHlnMX36dFP3ADyXDh8+/N133xUVFVWpUsXOzi4mJuadd97Jysq6cOHCiy++aGVlZeoGAQAAgCdgEgN4Rv7+/ps3b65WrdrLL78sIikpKcHBwe+//76p+wIAAACeFu8QAs+osLBwwoQJtWrVKioq6t27d3p6+rFjx956661FixZpNBpTdwcAAAA8mZlWqzV1D0BFoNFoCgoKbG1t33333bZt23br1s3UHQEAAABPwCOjgH4UFRVVqlRJRGrXrp2cnGzqdgAAAIAnIxACZZWbmztkyJDXXnvt4MGDvr6+P//884YNG0zdFAAAAPBkPDIK6E16evrdu3ddXFy+/vrrnj17VqlSxdQdAQAAAI/DDCGgN1WrVq1ateqQIUMOHjxYXFw8fPhwU3cEAAAAPA6rjAJ6FhoaKiLr1q1jrVEAAACUcwRCQM86derk7e0dHx//448/mroXAAAA4HEIhICemZubDxkyRETWrl1r6l4AAACAx2FRGUD/srKyAgMDc3JyDh06VK9ePVO3AwAAADwcM4SA/jk4OLzxxhsiwv4TAAAAKM+YIQQMIi4urm3btjY2NpGRkY6OjqZuBwAAAHgIZggBg/Dx8Wnbtm1BQcHOnTtN3QsAAADwcMwQAoZy+PDhUaNGFRcXX7t2zcLCwtTtAAAAAH/EDCFgKO3bt7e0tIyPj9+1a5epewEAAAAegkAIGIqZmdnbb78tIkuWLDF1LwAAAMBD8MgoYEDZ2dkqlSozM/PcuXONGjUydTsAAADA/2CGEDCgKlWqDBs2TESWLl1q6l4AAACAP2KGEDCsGzdu+Pr6WlpaJiYmOjs7m7odAAAA4HfMEAKGVatWrS5duhQUFKxdu9bUvQAAAAD/w2L69Omm7gGo4GrUqBEWFhYVFSUiSUlJmZmZFhYWdnZ25uZ8IwMAAABT4pFRwOBiY2ObNGliZ2eXlpb24HknJycfHx8fHx83Nzd3d/cHj03VKgAAABSFQAgYVn5+flBQ0Pnz51u0aPHSSy+p1Wq1Wp2QkJCamvqov30ODg4qlcrb21ulUqlUKi8vL92xh4eHtbW1kfsHAABABUYgBAxr5MiRa9eu9fX1jYyMrFKlSun5wsLCtLS0lJSUuLi4uLi45ORk3fH169fv3bv30FLz58/v1atXrVq1jNU7AAAAKjgCIWBAW7du7du3r62t7alTpxo3bvyUP5WRkaFWqxMTExMSEnQziomJib/++mtmZmZoaGhYWJhBewYAAIByEAgBQ7l27VpgYGBWVtbq1atHjhxZer5Hjx4ODg5/eCLUzs7u8dUSExNr165tZmZ248YNDw8PA/cOAAAARSAQAgZRUFDQqlWrqKioPn36hIeHl57Pz8+vXLnyn//e2dra/mFdGd2xl5eXpaWl7prevXvv2LFj2rRpLA4MAAAAvSAQAgbxzjvvLF++vE6dOpGRkQ4ODqXni4qKfvrppz8/EZqXl/fQOpMmTVqwYIHu+NixY23btnV2dk5ISLC1tTXGbQAAAKBCszR1A0AFtGPHjuXLl9vY2ISHhz+YBkXEysoqODj4zz9y+/bt0nCYmJhYeuDt7V16TZs2bZo1axYREbF169bBgwcb/DYAAABQ0TFDCOhZYmJikyZN0tPTly0UBEsFAAAgAElEQVRb9s477+i3+BdffDFs2LCmTZtGRkbqtzIAAAAUiEAI6FNRUVG7du1OnTrVq1ev7du3671+QUGBt7f3rVu3jh8/3rp1a73XBwAAgKKYm7oBoEJ5//33T5065eXltXr1akPUt7Gx0S1YumTJEkPUBwAAgKIwQwjozXfffRcSEmJpafnTTz+1bNnSQKOkpKTUrFlTo9HExcWpVCoDjQIAAAAlYIYQ0A+1Wj1kyBCtVvvvf//bcGlQRNzc3Hr16lVcXLxixQrDjQIAAAAlYIYQ0IPi4uL27dufOHGia9euu3fvNjMzM+hwP//8c8uWLatWrapWqytXrmzQsQAAAFCBMUMI6MGUKVNOnDjh6em5YcMGQ6dBEQkKCnrxxRfT09M3b95s6LEAAABQgREIgbLat2/fggULLC0tt2zZUq1aNeMMOnbsWBFZvHgxk/wAAAB4ZgRCoExSU1OHDRum0Whmz55tzH0g+vTp4+HhER0dfeTIEaMNCgAAgAqGQAiUyaRJk1JTU1955ZX33nvPmONaWVmNGjVK2H8CAAAAZUAgBMqkWrVqNjY2c+bMMTc39t+mMWPG2Nra7tq1Ky4uzshDAwAAoGIgEAJlkpGRUVBQsGXLFt3HlJSUjz76KDw83AhD16hRo2/fvhqNZvny5UYYDgAAABUP204AZRIVFRUYGOjk5KRWq+3s7DZt2jRw4MCAgIALFy4YYbnRs2fPNm3a1NHRUa1W29vbG3o4AAAAVDAEwt9lZWUVFxeXpULlypVtbW311Q+eF61btz558uTKlStHjx5dVFRUq1atmzdvHj58uH379kYYvU2bNsePH1++fPmYMWOMMBwAAAAqEgLhb7766qtPPvnk0qVLZSny4Ycfdu7cuW3btvrqCs+F8PDwfv36+fv7R0dHm5mZzZw5c9q0aT169Ni5c6cRRt+2bVufPn2cnZ0//PDDmjVrqlQqlUpVo0YNIwwNAACA5x2BUETk22+/7d69u729vZWV1TMXKSkpycvLc3FxiYmJsbOz02N7KOeKi4tr1aqVlJR08ODB4ODgO3fueHl5FRUVXblyxcfHx9Cjnzt3rlWrVra2thkZGaUnbWxsPDw83Nzc3N3dfXx8fHx8dMd+fn5VqlQxdEsAAAB4XhAIpbCwMCAg4OrVq0uXLv373//+zHU0Gk3Lli3PnDnz8ccfz5gxQ48dovybM2fOlClTQkJCdu3aJSJDhw7dsGHDu+++O3/+fIOOe//+/ebNm8fExLRu3bpJkyYJCQmJiYlqtTo9Pf1RP+Ls7KybRfT29vby8io9dnV1NcJLjwAAAChXCIQyd+7cf/7zn/7+/ufOnSvLDKGInDp1qnXr1ra2tr/++mvNmjX11CCeA2lpaV5eXvn5+TExMX5+fkZb62Xw4MFhYWENGjQ4ffp0pUqVSs/n5+cnJyfHxcUlJyenpKTExcXpjhMSEnJych5aauXKlSEhIe7u7obrFgAAAOWN0gPhrVu3/Pz8srKy9u3b17lzZ93JPXv2/KW3vz799FNHR0fd8YABAzZv3tynTx/jbDyA8mP48OHr168fP378woULxShrvaxbt27EiBF2dnYREREvvPDC0/yIVqtNTU3VzSKq1erSGcUrV67k5OSMGTNm6dKlBuoWAAAA5ZDSA+GwYcO++OKL7t27f/3116UnZ8+e/dFHHz19kaSkJA8PD93xzZs369atm5OTc+TIkXbt2um5XZRj0dHRDRs2tLe3T0pKcnBw0K314ufnFxMTY4hHMS9dutSiRYvc3NyNGzcOGjSo9Hz37t1dXFwefCLU09PT2tr6idUaNGhgZ2eXlJT0t7/9Te/dAgAAoHxSdCCMiopq3ry5paVldHS0r69v6fnz589HRkY+fZ1+/fpVrly59KNukcnGjRtHRERYWFjos2OUb+3bt//pp5+WLFkyduzY4uJiHx8ftVq9f//+Tp066XegvLy8oKCgCxcuDB8+fO3ataXn79y54+zs/OfrnZycSteVeXCNmZo1a5qbm+uuCQ4OPnTo0MKFC8ePH6/fbgEAAFBuKTcQarXadu3aHTt2bPLkyXPnztVj5by8PH9///j4+NWrV48cOVKPlVHO7dy5s1evXr6+vjExMebm5rrXU7t27bpnzx79DvTmm29+/vnn9evXP3PmzINfRuTn5//www9/eCI0OTn5URtszpgx4+OPP9Yd79q1q1u3brVq1bp69SpfZAAAACiEcgPhpk2bBg4c6OLiEhsbq/dn5HQb0zk7O8fGxpa+XggjycmRFSvkyhXx9ZUxY8SQa7r8QUlJia+v740bN7777rsuXbpkZGR4enrm5eVdvny5bt26+hply5YtoaGhtra2p0+fbtiw4dN0lZKSUvq6oC4oqtXqxMTEBQsWDBs2THeZRqPx8/O7fv36rl27QkJC9NUtAAAAyjOL6dOnm7oHE8jLy+vRo0dmZubSpUuDgoL0Xj8gIODIkSO//vprcXGx3h8XxONoNNKxo7RpI0OHyu3bMmGCDBsmxtpNwdzcvLCw8ODBg3fv3h04cGClSpXi4+OjoqLMzc1fffVVvQxx9erVkJCQwsLC1atXv/LKK0/ZlYODg5eXV4MGDVq3bt2lS5fQ0NC33npr8uTJTZo0Kb3MzMyspKRk//79d+7cGTx4sF66BQAAQDmn0BnCjz/+eNasWU2bNv3ll19K36HSr3PnzjVr1szc3PzixYt6nB3CExw/LkuWyNatv3184w0ZN07atDHa+Pfu3fP09MzNzb106VK9evV0i7XY29ur1eqyT0Tn5+e3atXq7Nmzffv23bJli14aflB2dranp2dWVtaFCxcaNGig9/oAAAAobwyShco5tVr9n//8x8zMbNGiRQZKgyLSuHHjYcOGFRUVvfvuuwYaAg+RkCC1a//+0ddX4uONOb6jo+OgQYO0Wu2yZctEpH79+u3bt3d2dr5x40bZi48fP/7s2bO+vr6rV68ue7U/q1Klim7B0s8++8wQ9QEAAFDeKHGGsG/fvlu3bu3fv/+mTZsMOtDt27f9/PwyMzP37t2rrycG8QQ//igbN8oXX/z2cehQGThQgoON2cLly5fr169fqVIltVpdtWrVW7du1ahRo+xfPej2sbC1tT158uSDj3rq19WrV1944QVbW9vExMRq1aoZaBQAAACUE4qbITxx4sS2bdsqVao0Z84cQ4/l7Ow8depUEZk4cWJRUZGhh4OIyEsvSVSUnD0rInL2rERFGfN5UZ169eoFBwfn5uZ+8cUXIuLi4lL2NHj9+nXdirWLFi0yXBoUEV9f306dOuXm5q5fv95wowAAAKCcUFYg1Gg048eP12q1H3zwgbe3txFGHDduXN26dWNiYngGz0isrWXXLlm4UF57TRYulF275MoVefNN2b/fmF2MGzdORJYsWVJSUlL2agUFBX379s3MzHzjjTdGjx5d9oKPp2t+2bJlj9qsAgAAABWGsh4ZXbNmzahRo1QqVUxMzIO7txnUnj17QkJCHB0dr169Wr16deMMCikpETMzMTeXBQvkvfekUydjZkKtVluvXr3Y2Nh58+YNHDjQ1dXVrAwrnY4bN27p0qW1a9eOjIzU+xYpf6bVav39/WNiYnbs2NGzZ09DDwcAAAATeg4DYUmJPKJnrZlZyaNvJy8vz8/PLzU1NTw8vE+fPgbr7yFeffXVffv2jRkz5rPPPnvUlJGZmZnFY/4sLCyMtndCRfD55zJjhixZIq+/LvfuiUol9+/LhQtixJUzhwwZsnfv3rS0NBGxtrauVq2au7u7j4+Pj4+Pm5ub7rh27dpP3KZyz549r7/+uqWl5bFjx1588UWj9C7Lli0bO3Zsu3btjhw5YpwRAQAAYBLlPhAmJ8v48XL/vuTnS69e8s470qqVnDr10GvjWreufeLEoyqNGzduyZIlrVq1On78eFmma57B5cuXGzVqpNFo3nvvvblz5z70GnNz8xKN5pElVq+Wnj1l/Hi5e1cKCqRjR5k8mYj4SAsXysSJ8n//Jz/8ICLy9tuyYoWMGSPLlxtn/CtXrjRr1iw7O7tOnTrZ2dm3bt161JVOTk4qlcrb29vLy0ulUpUeu7m5WVpaqtXqxo0bp6enL1myZOzYscZpXkTu37/v6emZmZkZERERGBhotHEBAABgZOU+EL76qkyYIJ06SVGRdOsmEyfKzJly8uRDr73eqlXdR/yWiFhaWhYVFZ0+fbpZs2YGa/eRxo8fv3jxYj8/v+vXrz/0AgsLi4LHvG+2apX8+KOEhEi/fqLRyIAB0r279O1rqHafd9nZ4ukpWVly/rw0bChXrsgLL0ilSqJWS9Wqhh68oKCgVatWUVFRffr0CQ8PF5H8/PzExES1Wq1WqxMSEh48zsvLe2gRS0tLNze3/Pz8O3fu9OjRY8eOHUb+FmPixIkLFy588803161bZ8xxAQAAYEzlOxAWFEjDhhIb+9vHb76RI0dk0aJnqDRy5Mi1a9eOGDFizZo1+uzwqWVkZNSpUyc9Pf3EiROtWrV6lhI1a/6+pd7Ro7JmjYSF6a/BCmfsWFm2TEaNklWrREQ6d5YDB2T+fDH8tpBvv/32ihUr6tSpExkZ6eDg8PiLMzIykpOTU1JS4uLi4uLiSo8TEhJKSkqcnJzy8vIuXrxYp04dQ7f9B/Hx8XXq1LG0tExISHBxcTHy6AAAADCO8r3KaHa22Nn9/rFKFcnMfLZKDRo0sLa2btSokX4a++ucnJx0r405OTk9y88XF8uDWxc4ODzzfwqlGDdOzM0lLEzu3v3to4gsWyb6WPbzMbZv375ixQobG5vw8PAnpkERcXJyql+/fnBw8KhRo+bOnbtx48aDBw9ev349Ly8vLi6uYcOG+fn527Zt01188+bNqVOnfv/99wa9BZ2aNWt27dq1oKBg7dq1RhgOAAAAJlG+ZwhFpE4dOXdO7O1FRBYsEK1WrK3lxo2HXnujevXFaWkP/a3Lly8fOHDAxcXlypUrT/PPdL3bsWNH7969HRwcQkNDbW1t/3yBubn5p495h7BfPxk5Ug4dEmdnEZH16+XqVfnXvwzWb4XQpYt8/73MnSuTJ4tWK/XqSWys7NwpPXoYaMCEhISmTZump6d/9tlnb7/9dtkLHjx4sFOnTh4eHjdu3LCysjLyWi+HDh0KDg52d3ePj4+3srIywogAAAAwNm05t3q19vXXtfv3a9es0QYGajMytC1bakUe+ut669aPudPmzZuLyJQpU4x/E/n5+bpH/vo++q0/c3PzR92XVkS7erV261Ztx47a77/XbtyobdJEm5xs/Bt5znz/vVZE6+GhLSzUarXaxYtLatT4acQIA41WWFgYFBQkIr169dJj2QYNGohIeHi4VqvNzs7WbTsRERGhxyEeo2HDhiLy1VdfGWc4AAAAGFm5nyEUkYgIOX5cqlaV7t3FwUG2bpXk5IdemGhvv/P+/UeVad68eZs2baytraOjo438RtYnn3wyderU+vXrb9y48ejRow+9xtzMbNxj/iyCgyUgQC5elCNHxM5OunWTatUM1W6FodVK/fpy+bJs2ya9exdlZ9fy8bmZlnb+/HldztGvtClTen33XVJ2dmRk5BM3k3h6K1euHDNmTOvWrY8fPy6GX+ulqKjowcnA1atXjx49ulatWh988IFKpfLy8vLy8qpSpcpfrXn37l1XV9fSM6mpqfb29va6mX8AAACYzvMQCPVn8ODBYWFhvXr12r59u9EGTU1NrVu3blZW1oEDBzp27Gi0cSEiJStW3AwL2+zt/cFXX8l/13odPny4/t+L+/Zb6dGjuFKlm0ePeut1n4bc3FyVSpWenn7mzJnmzZsbdK0XjUbzyiuv1KtXb/78+dbW1iJy7Nixzp07W1hY3H/gqxZHR8cHt8rw9/d3dXX18PBwcXGxtLT8Q827d+/GxsaePHny5ZdfDgwMzMvLGzZsWLVq1dRq9eDBg3v37q3fWwAAAMBfoqxAaJJsZpIUCp3SNPXLL780a9bs+vXrfn5+VlZWiYmJzrq3MfVCrZYmTeTuXVm4UMaP11vZ/3rvvfcWLFgwePDgDRs2iEi3bt127do1e/bsKVOm6HegWbNmffzxx66urufOnXNxccnIyGjatGl8fHyrVq3q1aun2yojPj7+D1tlNGvW7ObNmyJiYWHh7Ozs6enp4eHh7u7u4eHh6enZqFGjsLCwixcvzpw508vLKzc3NzY2tkmTJikpKW+++aZxFsgBAADAoygrEMoDT2+eO3fuz7MZehcZGdmiRQtLS8tLly4Zf+cAiMikSZM+/fTToUOHfv755yISEhKyZ8+eOXPm/POf/9TPAMXF0r69nDghXbvK7t1igN0CExISateubWFhkZCQ4OrqaqC1Xo4ePdqhQwetVrtv3z7d1yW9evXauXNn8+bNjx8/rpsw1ElLS3twT8X8/PyEhISkpKTbt29r/rQw0v79+69everq6urj47Nq1aquXbvqNgKNjo6eP3++LuICAADAZEz6BqMJlK7vsmLFCkOPpdFoWrduLSIffvihocfCo8THx1tYWNjY2KSmpmq12gMHDoiIu7t7oW6lmbJ7/32tiFal0qal6afgw/To0UNEZsyYofuo97Vebt++7eHhISIfffSR7szixYtFxNHRMS4u7imLFBYWJiYm/vzzz9u3b1+8ePHkyZMHDRqUkZFReoFGo9Ed5OTkdOvWLTIyUl/9AwAA4NkoLhBqtVrdrm5Vq1ZNM+S/4LVarW72w8XFJTMz06AD4fG6desmIrNmzdJ9fHDdzrLau1drbq61tNQeP66Hao92+PBhEXF2ds7Pz9dqtatWrRKRli1b6qW4RqMJCQkRkbZt2xYXF2u12vPnz+s2R9mxY4dehnjQrVu3+vfvv3//fr1XBgAAwF9VvjemN4zevXt37NgxPT199uzZhhslNzd36tSpIjJv3jyT7HyIUuPGjROR5cuXFxYWiohuh8AlS5aUte7NmzJ4sGg08skn8tgtT8quffv2jRo1un37tu5N1EGDBlWrVu3UqVNnzpwpe/F///vfu3fvrlGjxubNm3Xrx/Tp0yc/P3/s2LE9e/Yse/1S8+bN27t3b+fOnf/2t79dvXpVF2sBAABgQop7h1Dn0qVLjRs3FpFz587Vr1/fEENMmTJlzpw5gYGBZ86cMTdXYvAuVxo1anThwoXNmzeHhobm5OSoVKq8vLzY2FgvL69nL3rihPToIc2ayXffGeLVwT9Yt27diBEjAgMDIyIiRGTy5Mnz5s0bOHBgWFhYWcqePn26TZs2xcXF3377rW6ecODAgZs2bWrYsOHp06d184T6kpubm5+fn5SUpPtobm4eEBCgx/oAAAD4qxQaCEXkrbfeWrVqVXBw8MGDB/Ve/MaNG/7+/gUFBUePHn3ppZf0Xh9/1Zo1a0aNGhUUFDR+/Pi9e/d+/fXXPj4+fn5+bm5u7u7uPj4+Pj4+uuMnFMrOljVr5MYNqVdPRoyQO3fExkaqVzfCLRQUFHh5ed2+ffvUqVNBQUGJiYm1a9c2MzO7ceOG7vW/Z1C6jujkyZPnzp0r/9320N7ePiIiom7dunq9AwAAAJQ7yg2E6enpvr6+6enpe/bs6dq1q36L9+7de8eOHYMGDdq4caN+K+PZ5OXlqVSq6tWrX7lyxc7O7sFd9R70QcuW/8rKEm9vUalEpRIvr9+OPTzE2lqKi6VNG3nnHQkKkr175fvvxbi7JuimnUNDQzdv3iz//d9s2rRp06dPf4ZqWq22Z8+e33zzTYsWLY4dO2ZtbR0dHd2iRYu8vLwvv/xywIABeu4eAAAA5Y9yA6GILFy4cOLEiXXq1ImOjraxsdFX2cOHD3fo0KFy5cqXL18u0xOJ0KuoqKjOnTunpaXNmzdvwIABKSkpcXFxcXFxycnJuuPr168vatBgyLFjD/lhc3NxdZWlS2XPHlm//reTnTrJggXSsKHRbiE5OblmzZoiEhcX5+npeezYsbZt2zo7OyckJDzDs52ffvrppEmTnJycoqKiatasmZOT07x588uXL48ePXrlypX67x4AAADlj6IDYXFxcePGjS9duvSf//xn4sSJpefnzZs3Y8aMp69z7do1Nzc33XFJSUlgYOD58+cNsW84nllxcfHLL798/PjxLl267Nmzx+wRr/wV3btnlZgopb/UaklMlIQESUkRGxuZP1/u3ZPSDQz//nfp1Elef914tyHSr1+/8PDwqVOnzpo1S0SaNm2alZW1e/fuevXq/aU6ERERrVu3Lioq2rlzZ/fu3UVkyJAhGzduDAgIOHPmTKVKlQzSPQAAAMoZg+/MXp5ZWlouWrSoY8eOM2bM6N+/v6urq+58YWFhbm7u09d5cDPuNWvWnD9/XqVSTZgwQc/togw++uij48ePe3p6btiw4VFpUESsHB3F0fEhk37FxZKWJidPSkzM7ydTUuS//88Yzbhx48LDw1etWjVlyhRbW9vdu3e7ubn91VWL7t2717dv38LCwgkTJujS4Pr16zdu3GhnZ7d161bSIAAAgHIoeoZQp2vXrnv37h01alTpIvhFRUW6/QmeUuXKlXUZ4969e35+fnfu3Nm2bVvv3r0N0i7+un379nXt2tXc3Pzw4cNlWuMnO1uCguSbb8TXV375RUaPll9+EQsL/XX6VFq0aPHLL7+sX79+2LBhz1Zh7969PXv2bNiw4fHjx62trS9dutSiRYvc3NwNGzYMHjxYv90CAACgPCMQyrVr1wICAoqKik6fPt2sWbOylJowYcKiRYteeumlo0ePPmYaCsZ069atxo0bp6am/utf//rggw/KWu7SJZkxQ+7fl+rVZfZsMcU7omFhYYMHD27cuPHZs2efuUhkZKSTk5OPj09+fn5QUND58+eHDRu2vvT1SAAAACgDgVBEZNKkSZ9++qmPj8/AgQOfuUhaWtrq1as1Gs2ZM2cCAwP12B6eWUmJjBr17/XrP+jcufPevXsrxoaQhYWF3t7eqamp69ev7969u5OTU1mqDR8+fP369X5+fhEREVWqVNFXkwAAAHguEAhFRO7du9elS5ebN28mJiaWpU6HDh0CAgIWL16sr8ZQRtOmycyZ8tprG9ate9XZ2dnU7ehN9+7dT548eefOHRGxtbXVbaX4hz0Va9asaWdn9/g64eHh/fr1s7W1/fnnnxs1amSU3gEAAFCOEAh/t3Llylu3bpWlQufOnYOCgvTVD8royBEJDhatVvbvl+BgU3ejP6W7BdaqVevu3btZWVkPvczMzMzV1dXLy0ulUqlUKm9v79JjFxcXEbl27VpgYGBWVtbatWuHDx9u3JsAAABAuUAgRMV0+7Y0biwpKTJ9ukybZupu9CcnJ6dFixa//vrriBEj1qxZIyKZmZlqtTo+Pl79X7rj5OTkoqKihxaxtbX18vLKyMi4c+fOgAEDvvzyS+PeBAAAAMoLAiEqII1GXn1VDhyQdu3k0CHjrwNqQEOHDt2wYUP9+vXPnDlTuXLlx1+ckZERFxcXFxeXnJyckpJSepyamqrVamvUqJGbm3v9+nXdhCEAAAAUiECICuiTT2TqVHF2lrNnxd3d1N3oz5YtW0JDQ+3s7M6cOePv7//MdXJzcxMSEvr373/u3LmVK1eOHj1aj00CAADgOUIgREVz7Jh06CAajXz/vXTqZOpu9Ofq1auBgYHZ2dmff/750KFDy15Qt6KMv79/dHQ0u6QAAAAoU0VYhR940NSpUlwsH3xQodJgfn5+nz59srOz+/Xrp5c0KCK9evVSqVS//vrrDz/8oJeCAAAAeO5YTJ8+3dQ9AFJSUjJgwIDTp0/HxMTcunUrNzfXysrqie/I6aSmSmzsb4+GJiVJx46Slyfz54ulpYjI6dNSrZpYWRmye8ObM2dORESEs7Pz7t27ra2t9VLT3Nw8Ly/vxx9/zMjI6N+/v15qAgAA4PnCI6MoF5KSklQq1R9OVqpUydvbW6VSNW8+0dr6FW9vUalEpRIvL7G1/f2yTZtk5EiJjJR69WTlSikslPXr5cQJ0W3CFxws69aJt7cRb0bfvv322zFjxlSrVm3Lli3169fXY+X09HSVSpWXlxcTE+Pn56fHygAAAHguWJq6AUBExNHRcdOmTbpdExISEhITE9VqdUZGRkxMTExMTFHR1CNH/ud6F5ffw2HdutKnj4wdKwcPmqZ5g4qPj3///fdFZPLkyfpNgyJStWrV0NDQdevWLV++fNGiRfotDgAAgPKPGUKUX/fv39eFw7t3G8TGeiYmSkKCqNWSlCSFhb9f9tlnkp0tKSnSuLHk5/82Q9iw4W+PjO7fLydPPq8zhIWFhSEhIRcvXgwJCVm1apUhhoiOjm7YsKG9vX1SUpKDg4MhhgAAAEC5xQwhyi97e/v69ev/eVpMq5XU1N/DoYODZGfLzJnStq2EhkqlSiIiM2b8dnDtmtH71p8ZM2ZcvHixZs2a8+fPN9AQAQEBnTt31mq133777aBBgww0CgAAAMonVhnF88fMTNzcJChI3nhDJkwQ3Y4JDg4yebIsXvzbNc7O4uoqrq6ipxVYTODgwYNffPGFtbX1qlWrDDp3949//OPChQsrV67UaDSGGwUAAADlEIEQFUdoqNSrZ+om9OTmzZv/+Mc/tFrttGnTGjRoYNCxOnbs6O3tfePGjcOHDxt0IAAAAJQ3vEOICkirldKN1h88fo5s37594sSJnTp1Wrt2rRGGW7FixaxZs9q1a/fVV18ZYTgAAACUEwRCwJS++eab7t27P3gmIiJi//79kyZNunjxYt26dY2z0EtWVlZgYGBubu6RI0d8fX2NMCIAAADKAx4ZBUxmzZo1+/fvnz9/fkFBge5MTk7OzJkzf/jhh8LCwubNmxtt2U8HB4fevXtrtdr169cbZ0QAAACUBwRCwGRatGjh7e3t5uZWUFBw9uxZEZk7d+7o0aPt7e2N3/bDUxcAAANRSURBVMzIkSPNzMy2bt167949448OAAAAkyAQAiZTu3btSZMmvfbaa/fv34+Ojj579mxCQkLXrl1N0oyPj0/btm3z8vK2bNlikgYAAABgfARCwGTs7e2trKwcHR3d3d0HDBgwadIkf3//L7/8Mi0t7cCBA8bvZ/jw4SJy4cIF4w8NAAAAk2BjesD0cnJyEhISJkyYoPtoYWFhkqdGO3TocODAgYCAgMOHD7/88su6k8XFxYcOHcrMzOzQoUP16tWN3xUAAAAMh0AImF56evrRo0ffeust3ccff/zxpZdeMn4b5ubmAQEBq1atio2NjYqKGjdunJWV1fvvv+/j4+Pg4NCzZ8+DBw/a2NgYvzEAAAAYCNtOAPgf8fHxS5Ys6dKlS5s2be7du+fk5GRtba3Vajt27Lh582ZnZ2dTNwgAAAC94R1CAH80e/ZsT09PtVodFhZmbW29bdu2kJCQV155hTQIAABQwTBDCODJbt26NXTo0NWrV6tUKlP3AgAAAL1hhhDAwxUXF2dnZ1+8eFGr1bq4uLi6uqanp5u6KQAAAOgTi8oAeLjr16+Hh4e7urrOnj3b2trazc2tQYMGpm4KAAAA+sQjowCeTKPRmJvzQAEAAEBFQyAEAAAAAIXiK38AAAAAUCgCIQAAAAAoFIEQAAAAABSKQAgAAAAACkUgBAAAAACFIhACAAAAgEIRCAEAAABAoQiEAAAAAKBQBEIAAAAAUCgCIQAAAAAoFIEQAAAAABSKQAgAAAAACkUgBAAAAACFIhACAAAAgEIRCAEAAABAoQiEAAAAAKBQBEIAAAAAUCgCIQAAAAAoFIEQAAAAABSKQAgAAAAACkUgBAAAAACFIhACAAAAgEIRCAEAAABAoQiEAAAAAKBQBEIAAAAAUCgCIQAAAAAoFIEQAAAAABSKQAgAAAAACkUgBAAAAACFIhACAAAAgEIRCAEAAABAoQiEAAAAAKBQBEIAAAAAUCgCIQAAAAAoFIEQAAAAABSKQAgAAAAACkUgBAAAAACFIhACAAAAgEIRCAEAAABAoQiEAAAAAKBQBEIAAAAAUCgCIQAAAAAoFIEQAAAAABSKQAgAAAAACkUgBAAAAACFIhACAAAAgEIRCAEAAABAof4fR0cWtL2iGuEAAAEvelRYdHJka2l0UEtMIHJka2l0IDIwMjAuMDkuMQAAeJx7v2/tPQYg4AFiRgYI4ANifiBuYGRzyADSzMzIDA0QgwVGM+iAaAZGCRYdkHYtDHFmHOJMMHEMg9EE4DpYITq4GRgzmBiZEpiYM5iAssysCaxsDKzsGUzsHAzsnAmcXBlMXNwJ3DwZTDy8QG8lcDEliDCxMfBwczExsjGzsnNyMYnDfMrAJ6Zq4pASOGk/iHPr/2b7P+FXwOzSTU127wuMwOytcyfv57efDWafnrJl/zQjnQMgdmWF8AGtuoZ9IPaiubkHXH1cwGre/WE6MIltrj2I3bPM5wC3MI8DiC0cr7xfi/c+WDzNndne+7Y7mM327qu97ZelYPbCBS4Oj4NW24HYT5P6HRZuiwSLiwEAD9hHnwiZRTAAAAE3elRYdE1PTCByZGtpdCAyMDIwLjA5LjEAAHicnZQxbgQhDEX7OYUvEORvGwx1JlWUFFvkDulzf4WBGcQqibQBUXyP8dO3QbPRsW776+cXjSX7thGMEIn4111KoQ9h5q2dDxmWq3hCgCmObxxqlumZ/kLMu1EQjJN0Soq5rFE4SIx6UDik4rxEOfqIql0JZM1LrTVwm4tUL5wmyu0nBZO2iSJB2aR3JBGLHWlwiHeKp5Qf96J3XtiS99sSuaP8ywuKe385yNkf9yITpd6vZule3CQteakVEXL24Zl9jYJQBHrOpaS4RtHALtprVW2xIwuW+ewoR7bHpxsHBW3U0gNpbwA90PY4z4y1GowgjaAqn4M8aqoqI5MJfAVVAdexqiAjI3QM98zo8Wu6Mt0Yrpph9I3o5X3fvgHgxsBc8XTh4AAAARZ6VFh0U01JTEVTIHJka2l0IDIwMjAuMDkuMQAAeJxlkL1uQjEMRl+lI1TB8n+ce6fubdW9YoBbNq5ACIZKPHwd6IKYnBwff44y0TTxtPh+HXS9vFX5r5w1WxPl2dYv1wVDkFYvKwJSISojgaKzdeIWTcuIwGbsUVYI3iqKl7H7JneNieOOlLC2suLU0FoiBkFl6ZNs2FySCVTiap1V9xp3D9W9lQxjr36zqFWvhYEivJNcLkE3qSr3eATL1RglkwKzk49vTCI9p7lxGQWw5n4EEXWqZVTQnOAkYagqZVk258P8dTocB4Wfyzz/vm+2uz3k58F82L9l82Nz/LzM290JdPBHx58dGeLRiWeHB5JHKe9Pll3/AGGXdIGIrtn6AAABY3pUWHRyZGtpdFBLTDEgcmRraXQgMjAyMC4wOS4xAAB4nHu/b+09BiDgAWJGBggQBGJhIG5gZGPIANLMzECGBojBwuYAo2ES+BhQtQwJQJqJCU6TYQaDDohmYJRg1AG5UoubgVGDiZFJgYk5g4mZJYGFNYOJlS2BjT2DiZ2DgYOTgZOLgYs7g4mbJ4GHN4OJly+Bjz+DiUGAgZ9RgZ0pgZ8rQYSZnRGolpOLn42ZhZWNnYmNm4eXj59LHBYIDII9OVP2vmCUOQDinNGYY3N5X8F+EPud/hZ7Ddn9+0Ds85EyDhO37wKL2y+pdHib0wtm54R1OfwwX20HYv/PCnJw3bfVHsSWdXxlv7C4A8z+f6vcPuq0kAPY/Fz2/QLLJcDsmTFH9rscXwRWs4TF5oBt2TMw20+++cCttEgw23ZR2YEdNYpgu56dljkQmjsFzL6lvHS/Skgo2G08Ulv3m99wA7tfDABAm1fNdyNaHwAAAVN6VFh0TU9MMSByZGtpdCAyMDIwLjA5LjEAAHicnVQ5bsQwDOz1Cn4gAi8drLOpgqTYIn9In/8jlGRrDWzSUBDgkcYcDynRCca4396/f2APvqUE1IAMAP+cZgZfjIjJX3/BjI3LQJxVWh0avucDXuE/ietMRwQZLT1T1JgKZcXWloo0kZiK56GCQ8X1Sm8xFcmGWJcKoVpMRbPw9OKxIhqsi2TurR8VIgt6odwFaSGsFaMnbbXzqjObxKrr51vYcKlILxxToVxEj4y4UdALZ0/jqG5HLDEVzWhn93S1YEaSe5GjA6pgj2akOm/JuLvFYj3tsX5EvLww9lg3jh6k2T2eG2G93t37swo94zQRzylpPRaZJtK5NRaOymYc1c04aptx1DfjyDZjQHguHBHtBQHxGeOItgNHtB04ou3AET0c0Pgnn8zapu1ALsz+6AfA2+ct/QLgJuiNjpMAkwAAAR96VFh0U01JTEVTMSByZGtpdCAyMDIwLjA5LjEAAHicLY87bwIxEIT/SkpAPmtf3genFNG1SZQ+SnEQOk4gFIpI/PisIa7sb3ZnxtPqecI97fuhafq/Ia0/N1v8erqtBqhgJEZWBqrCplTGZBigJCXlEBBMiFXADDtiY9IoYy4IhZchtebGZeQaANo6QRDPGalMDK3kFosg9hlyMy+5hOHWnZ0hFaygCtzzQx29FaoUrFrGDG0U2SoRe8s6mdBYoNuQYfYas3/ww9ghw5NIhbBmGe4SRN2IqzfuDaEqg0PcF0Ui5N66RYu7e4/TPkbgTA8mKJ6M83MarazL/HNaPi6n8xa1fl+X5fd13h2OddPfy+n4kvLbfH6/LrvDpeLtD4ufWNSw7ssmAAABoXpUWHRyZGtpdFBLTDIgcmRraXQgMjAyMC4wOS4xAAB4nHu/b+09BiDgAWJGBggQBWJxIG5gZHPIANLMzECGBojBwqADohkYJdh1QKq1YOLoNFwdB6o6uDgnVJwhAUgxMRE0hxGHOUw4xJlxiLOg2svNwJjBxMjEwMicwMySwcTCmsDKxsDKnsHEzsHAwsnAycXAxZ3BxM2TwMPLwMOXwcTHz8AnkCAgyCAglMEkJMzALcLAzpAgxJUgwswgwszGwM7KAgwsTi5uEaAfePgEhLi4xWHByiBa+6jpwM4F7fYgzut/+gfKbt8FszuKtA4sP+LnAGLXde3fv+tCBljcefGp/V+6xPaD2BvErQ+YNywEs4WXOBxgn25wAMRet6L1QIXJg30g9tbuDQfuuPeB1azbv2Lf5B1bwGzDmEb71b9twOzvJT32zAlOYPO7GGQc9mdtB7Pf9Cg6ePdYg91gapXj4P1eGiyuxLDQIdloCZh9eW2aQ3JOMtic/cWzHbw1b4PZR54LOLz/cBzMTormdkg+7wx2G+dn+X3Si0+B9YoBAG/ObGtLTR+JAAABnXpUWHRNT0wyIHJka2l0IDIwMjAuMDkuMQAAeJydVTtu5TAM7H0KXSACh6Q+rPO2CnaLFHuH9Lk/Qkm2noLNAu/JUDGEycFwRNpHaM/77e3jM8yHb8cRGIElBPrxmFn4y0R0ePqLRjJCq0SkZLkhiv6Wwmv4H8V6OgvHok7bWYqI7LLkLNxqJbJQXVje/2XBgsvCgqiWddQame5pQUxVmi8v5KhityPj7obzcUbZY5FI8IrhczJ+3Je6sGhEyXV0pFJsT0uKCQWjIwD5cS22sFAU7rPWbgvY09ImFpQHX5HK2yxWzol1d/dmN3DzNA0+9eZ2WRIrD2RC+ri7WFgkZsM5sZlsb3ZDiiSQ0RFXfmIb+bsWUh13VGvd28agsaY+sT4vBfzEBsg3dznrObspM+3eEUq/6baXydLjWvTO4m7AJQx3zy/N01rQreYRcO8VM9AZOEozzVGeb3L/ct6DOtNq31dcNaArcNRG6wwQMBU4wlTgCLoGU4EjTAUtmAocoa6BzRoLfFfA7f92aRsFuNLu2lrOpeB3CL/+3I4vZhYcZsPC3b4AAAF6elRYdFNNSUxFUzIgcmRraXQgMjAyMC4wOS4xAAB4nG1Ru27cMBD8lZR3AW+xTy5XqoK0SZA+uOKsuLNwhhEXAfzx2dW5ic6NRhwOZ4fDhZbDr8+Tn48LL4evi+SHjxtH5xvyO0qiJOr7eiRSYpw/vR1OChhI1gjQwqLNJwbXCEnGRbhvTO/C2gRY0C0ZAo2u1BACQ+VG2RCidsL8GWgjOQThsOGtDhCN5HIOYZeSuQzOyUWFC6eZW7D7dpCsM1K7ubaZQcWtQmlZFmGsLI0hBFXbLNCD0NKlYxC32QCF2PMID/bYFKjSa/QYQ1zarDDMM1bmc2IuW857WREVQIshd01GtoZk6yM4qykRdyqf2iTMvNVeXiKdslnyPrTGZfZq1sDIcTMnIvN2bJc/1/Xny/V5Yvj9uq5/v10eHp8gHw/W69OX3Px+ef7xuj48voBPhP+Lcn2nool2VvSBF0+kO5Xeq2SivlP1e5VONHaqca8aE+/S8wfp4+0fRqG6m8FFITEAAAFSelRYdHJka2l0UEtMMyByZGtpdCAyMDIwLjA5LjEAAHice79v7T0GIOABYkYGCBAAYkEgbmBkY0gAiTNDaCYmNgcNIM3MwuaQAaKZGWECDDogmoFRglUHZIoWhgLcOphgOtDEmXGIs6CKcwOdzcjEwMScwcTMksDCysDClsHExp7AzpHBxMGZwMnFwMmdwcTNw8DNm8DLx8DLn8HEz5TAz54gwsTGxM/OxsLMxsHJzcvPLg7zPoPAlVyHAzPXWh8AcVZEXtxffUAEzN7Hsnf/Mq3Q/SD2KwmdA6ua/G1B7NJ3igd2ph+1B+tuzT8wwUbaAcR8GTVl/68FnGD2PNco226DRWA1uiHb7I9xfQCzFx3QcNjzqRbMvujQ4DDz8hmImmlyDmIn/+8DsXfo5DisY94ItjdyVa99w5leMPvuqiJ7oRg1sNuuat/Ye/BGKli9GADbJFOwZK+xgwAAAUl6VFh0TU9MMyByZGtpdCAyMDIwLjA5LjEAAHicnVI7TgUxDOz3FL4AkX9x4pqlQlC8gjvQc3/hTfZFWQHiKVGKmXU8O/5scJzb/vr5BePwvm1ABlQA8Nfr7vDBiLjF8ydJiFYOxMmF86GB8Q0RnuEvifk2FUomOXcVYeVVFS3dC6YqXNZUOFl1PHMzLqtkwVYHpay8WJGkor2nnALYpHL7qUITzpe+UO4VcSBcm1HrhkiviK3QmkrMiFU7qiXLmkrMSKz2XHfGNRWNjOLdiyHOM/qnu3zxoqWpxNapu615kWTeHMS0pLI/7kUu3SVsfTlmjqWuTtq1VxRb7K6Pe9Fp6zActI0NxKxLW0et1dQJt1pPIu1n3Im2fT8jgWwmZTwLVEckkI+IA+E9EohoJnx/FoiGg4PoIAqUZ2IjxybXB7k7eAN4ed+3b+Gu2ha5Jze0AAABLHpUWHRTTUlMRVMzIHJka2l0IDIwMjAuMDkuMQAAeJxlUD1PQkEQ/CuWaI7Nft3HPipDq8beWMCTjhcIkcKEH+/cw0ao5m52dmbu1utRxnHx8TTkz8dx1OtZce5of+hA0YfLYmnEXGpaKoWp17RaChXLOXcKjOaZ8lqqpCVTMy1NwSmVFqyJiTOXInXmsrG0JJRd1cAYVVePpASnfLWX3PcUyA73bmDaAluKEEkrxKm7g2g1G2yQZaUWZEVINCw5atcQSAqzSpd4raG9okfkBsqoIMgSIq0pZjAW7j69BNfWs8PnLTSPsN6PMcLzZydFjZIe0+b7ML2fDsch09d5mn5eNtvdnvDFNB32zxi+bo5v52m7O1EehP+LcL9T6SB6o9J7lQ3iNyq/V/nlF32XfJkb2c6pAAABSHpUWHRyZGtpdFBLTDQgcmRraXQgMjAyMC4wOS4xAAB4nHu/b+09BiDgAWJGBgjgB2IBIG5gZHPIANLMzECGBojBwqADohkYJZh0QKq1cCtghimAiLMzgGlmNoYEIM3EBKGB+lD4TExw/Syo+uHijBBxbgbGDCZGJgZG5gRmlgwmFlYGFrYENnYFdg4GDk4GTi4GLm4Gdh4GNt4MJl4+Bl6GBG5OBhGgLbxsLMyMzFyc3OIw3zLwe1pbHbhzrG0/iJPdGHWAy3CHHYg9oWLOgWNyivYg9vVwmQMOMQfA7PYF5fsVbs4Fsw2fNFn3i0g4gE0SeLvv13nzfWBzZjfaN3x5CmZPfvfXfs9SQ7D6/2mxDl/tboLNd38x24FhRy5YvN59ssM1Nh6wG9arH7K3D3gHZqtP377/r+dKMPuEaN7+Pj79AyC2GABiSktiX6ttmQAAAUJ6VFh0TU9MNCByZGtpdCAyMDIwLjA5LjEAAHicnZQ9UsQwDIX7nEIXwKM/y1bNUjFssQV3oOf+g2InxgwwsPa4eC+OvsiWnA32cbs8v73DGHzZNqAMZAD443R3eGVE3OL1B04eT3ZFCTPvCjDFKsIj/IaYZ6NIUlLqsZK9rFE01XrGGrtOlNt3Ck2aJwon1b4PShm5ruWCya1Ip7AIL1FahGKrS5Ji8v8dyZdc1AS7ouJ5LZeoL7EdPHWaKNd7KF7lOFNzWesXiH7JZ32VVdYo0S+Fj304V1ulGHLu55K14OrpZrLa71E1mrvuj0rrVGlKKu00Qgnz2o723hXEfheK5Du6jgaF2qXibrj1Iw2jw4TK47VQNlbClGFC1WFC+TAOhKcJRTQMAfEUQzLRSM+Phoq/3RnTEzsMf6JfAJ6ul+0DFXTNQZ6SC8MAAAEqelRYdFNNSUxFUzQgcmRraXQgMjAyMC4wOS4xAAB4nG2QvW4CMRCEXyXlEZmV98/2HlVEm6D0EQUQOk4gFIpIPHzGpApEsuTV59nZWe94N3w8j7Ke7W6FohhWw3IpODMAA2DcvH66DnOhyNFqmjNll7C0mCsZG3vKpB7VAsiotagCVCS0FiAhM2klMXmWJiCZolQ8A4kqoy9Tzhph8BBCm/hNZkUza0LFNTxjJoazeOkIYlbuKJo26yNDQ9GJXB7GICamuaUFYlXpIKR5iw5KFmvdx61Eje7jXOCDBVvhW1AmU9NOVMTrb3TNWRK2qupS0ixtvo7T+/l4GoU+L9P0/brZ7g+Ej6XpeHjB49vmtLpM2/2ZZPS/Gn/U6Mh3RvyPk41sdyp7VPH1B/A/eVgAsLMQAAABk3pUWHRyZGtpdFBLTDUgcmRraXQgMjAyMC4wOS4xAAB4nHu/b+09BiDgAWJGBggQBWIxIG5gZHPIANLMzECGBojBgk4z6IBoBkYJRh2Qbi0McSYc4sw4xFlwiLPiEGfHIc6GQ5wLhzgnRJybgTGDiZFJgYk5gwkoy8yawMrGwMqewcTOwcDOmcDJxcDJncHEzcPAyJvAy8fAy5/BxC/AwC+YICjEICicwSQswiDMkMDNlCDCxMYgLMjPy8jGzMrOyc0kDgtcBtE7cw/aezvs3A/i1M72sL82h3sfiH063Hz/xPNP94LYTH927N+eP9MexP6+RGf/YyF5BxD7cpXNgci2OWDxR0rZB1o1lcDi08/nHoj63QTWa/Js2YE6JWUwu0Pd/kDan/Vgu3xqCg5YRWodALFzQvft5xDeDBafz+W0/06+Lljc7fsx+xKnyWDzA/2C7Se1y4LNL61wdrikPQEsbnG2xOFjgRRYvFmhyOEjmy3Y/av5Vjr83x0KZh847eDwNmwP2PxzxfkO5wWNwOaLAQB7dms56n2pVAAAAZZ6VFh0TU9MNSByZGtpdCAyMDIwLjA5LjEAAHicnZQ9boQwEIV7TuELxJoZz5/rkCpKii1yh/S5v+If8II2kVgjCj9gHp/Hz15CvW7r+/dPGBetyxIIA1EI8Oedcw5fBABL/R6jIGkZvGBktlSfQSxvIbyG/yyO99IrzFNzgYhJ51xKraHS5oJ1BjMudR5CfW6Y3WZZ1JhbRyO7yMHl9uiCj+PmQjGzQGchIp9jSZUFOouk5NdZ6ORiKN7nBtr9nmeRSCjbSkPKT7Ckc19cva9WMs6zfTEx7X6qaNdZ+JwXN9hYcp7sS8muOHQWQ4DrLHJ3qbtReupKdgVkiqVUOMmeXUW+zmIHlxRBeN9HNLmni0th2bLLmPA6i55cyvmynwzOOsdSsusJuguBX88LnmcESLwlx07dfaovxq225sUpXe9LHi7YNjh1QW2HYRepRXx7wy1jOIQOoW3F78JHjbf/4C4QdlFGiEcxCMoI7wRF8PiMA8pR6KjRgIOgCh+i/DQfBMFeU0Y0CKiD4U4wevARwtvnuvwCX5Aao6dOEIAAAAF5elRYdFNNSUxFUzUgcmRraXQgMjAyMC4wOS4xAAB4nG2SP0/DQAzFvwpji9LT+d/Zvk7sgNgRQyndiIoqOiD1w+O7ZGna6dkvv3PeOdnDfrXZ4371/ljhY90VZ6VZORRDZe511jIr5CggCv94uKwgCWChYQOJWVGHbU5qVDCsnIAK8bCNSqGATR4gAobZTgj4AAncFDtWlMlxwMQmQmFhcpZAENEkempIPBcitd4riLW5uZBLCUsSQhTNIjfsWIyxot5ikrL5dFJUwsJUCugUyIpbh9zzlCggV2mUZveIEDcWZmyxJXO/sKEwtFRcQraUsnAsIAjs96dGZGoA0ETEjuYVGYvHlEhtlLlZmGMd0ucAMvfd6nws3sM9jGFMXg+73+P4djr+VE5f53H8e959Hr5TfMU0Hr+f4uHL7uf1PH4eTglquWbKLYPVrhm7ZSh+gWso+huKK+CCwltKKiyCw53kWmERHe5kLxUW4eFOesgVF/HxTny//ANQRtYZK9VUMQAAAPt6VFh0cmRraXRQS0w2IHJka2l0IDIwMjAuMDkuMQAAeJydjz1uwzAMhSXqx5YtSwEaJEPmpp16BQsZcohOBLpwzBEy9QQ5QzrlCrWADLlFgZwgV4gM17DWhMADP1GPlHj7Pf2xFDaJsyFmSS9Je64DpSxEDu89yMdunmkeC0OuGSfggCAIhESpCJRGXRAUJZaGwFRY1QS1RdsQWIfOEzQMHaBX6A3OpWaNdcC1kMo70LoojVe6qq3zZjku32+/C4efy3E4hnDdHruBYztxSLyO/9xNHLvMEyfexdXi+y2rt5m/zeaEbH7I3h09IeP28+M0/rP7Om9ee1rcAUt2RnLJHMv1AAAA8npUWHRNT0w2IHJka2l0IDIwMjAuMDkuMQAAeJytUzEOwjAM3POK+0AiOyVtPVMmBAMDf2Dn/8KuipsBliaRpZ596elstwF2Hsv19YafvIQAHsEC0M8QETwzEQW7P6SpkJYRKWnNEDZ0xj+JOjaV7d3IKYscVOFUOqiQd5RTkZkPqcQ+Kl06il2mG33TaNj07gUtXnwuTSq+IzTsCH1UunSELtOt/oCWTVOH70WHmdewRB/DWrJE0ckZRcUZRaMziiZnFM3OKBJnBExfRhHzl1HE7kARuwNF7A4scQeK2B3YHa6YvQW7M1aJOViTG3C5L+EDVdHc2x8Hh4wAAACVelRYdFNNSUxFUzYgcmRraXQgMjAyMC4wOS4xAAB4nF2P0Q3DMAhEV+lnItnIgHFBHgdliwxf7EZN3A+Q3nEcwtGd3J2jvEZtjnsoXF/nxvCWlBFYhfXIKKnzYDIrNXUEeVCZZgIxLZp6/ufVnXmB4Y1G2Or3Tszv8Vi9aQb/cldanI+Icn1CBZtdF6aG0NQ4lJb28wNGhzjwJHn9LgAAATR6VFh0cmRraXRQS0w3IHJka2l0IDIwMjAuMDkuMQAAeJx7v2/tPQYg4AFiRgYI4ANifiBuYGRzyADSzMzIDA0QgwVGM+iAaAZGCRYdkHYtDHFmHOJMEHF2iDgzwlw0i+AaWCEauBkYM5gYmRKYmDOYgLLMrAmsbAys7BlM7BwM7JwJnFwZTFzcCdw8GUw8vEBfJXAxJYgwsTHwcHMxMbIxs7JzcjGJwzzKwCemauKQEjhpP4hz6/9m+z/hV8Ds0k1Ndu8LjMDsrXMn7+e3nw1mn56yZf80I50DIHZlhfABrbqGfSD2orm5B1x9XMBq3v1hOjCJba49iN2zzOcAtzCPA4gtHK+8X4v3Plg8zZ3Z3vu2O5jN9u6rve2XpWD2wgUuDo+DVtuB2E+T+h0WbosEi4sBALwlR187j0mQAAABOnpUWHRNT0w3IHJka2l0IDIwMjAuMDkuMQAAeJydlDtuBCEMhvs5hS8Q5BfY1JlUUbbYIndIn/srPGYQqyTSLIji9xh/+m3QbFDXfX//+oaxeN82IAWKAPjnzjnDJyPi1s4HJ/UiXiiQCtVvGEoW4RX+Q8y7USgoJu6UFD2vUTBwjFIpGFI2XKLUPqJIV0y85qXUKmGbCxcvmCbK/TeFJq0ThYOgcu+IIy12JMGIrVMsJb/uRR68oCbrt8X8QHnKC2Wz/nLI3a574YlS7lecuxdTnqd7e+a9ROKjD3O0pY6Kg8wkx1xyimsUCWgsvVZE0xpFgzoeHXlEvT7dOCjURs094PYGqAfSHueR0VZDI0gjKMrmwEdNUXlkHAjPoCii81hRxCPDUId7ZKT+ms5MN0ZnzTD6AfB227cf9ynAZ71qcDMAAAEYelRYdFNNSUxFUzcgcmRraXQgMjAyMC4wOS4xAAB4nGWQvW5CMQxGX6UjVMHyv5N7p+5t1b1igFs2bkGoDJV4+DrQBTE5OT7+HGWiaeJp8fk86Hp5rfJfeb38ztZEebb102XBUEnDy4qAVIjKSKDobJ241aZlRGAz9lpWCN4CxcvYfZObxsT1hpQwWllxamgtEYOgsvRJNmwuyQSCOKyzcI9681DdW8kw9vCrRS08CgPV6p3kcql0lUK5xyNYrsZaMqlidvLxjUmk5zQ3LqMARu5HEFGnKKOC5gQnqYaqUpZl83OYP06H46DwdZ7n39fNdreH/DyYD/uXbL5tju/nebs7gQ5+7/ijI0O9d+qjwwPJvZT3B8suf3QGdIxEmHkOAAAAq3pUWHRyZGtpdFBLTDggcmRraXQgMjAyMC4wOS4xAAB4nHu/b+09BiDgAWJGBgjghOIGRg4GDSDNzMTmAKZZ2B0sQDQjM0wApoCwBDcDowYTI1MCE3MCM4sGEzNrAitbAhu7BhMbRwIHY4IIIxsjBxsrM5M4zBUgJzg4WPYlq0K4B+yfWPQthbAd7B+6LdsPZe9HsA8A2WoHYOyf/hOXIKmxR1LjgGQOWFwMAJRkJsEPHzWGAAAAwXpUWHRNT0w4IHJka2l0IDIwMjAuMDkuMQAAeJytUksOAiEM3XOKXgBSOiLTtbgyjokL7+De+8fSMXXih5BR0oT3Snn0NTio61wO1xvYouIcAGvgx2BmuBAiulo/BEFYEb6iE3yTWIaqxJDmu/5NZdevgiGnWSUGYl6qTN0qvqHS34t/OqKQeIyr5uIbc1njCP7iCH5wBI1euh3J06RRiWyDpioRtDEiKFmZkK2dCMpGBI1WlvXjP05Y00qOAPupuDtx3332464C1AAAAH16VFh0U01JTEVTOCByZGtpdCAyMDIwLjA5LjEAAHicXYw5CsAgEEWvklJBB8dtHMQ+nQcIqSRtTuDhI2QB0/3t/Voabue6N1GqnAQuXTiFwOTIHRqjyghBaQs+EsY7MUAjQrDMxqus//4hAieTXhvBGWQ7DuglZuDbT53sF3QYJX2Vc2YIAAABF3pUWHRyZGtpdFBLTDkgcmRraXQgMjAyMC4wOS4xAAB4nHu/b+09BiDgAWJGBggAsXmBuIGRzSEDSDMzIzM0QAwWBh0QzcAowaID0qYFE2d3sADRjMwYCplQFcLFmVHF4TZxMzBmMDEyJTAxMzCxZDCxsCawsiWwsTOwcWQwcXAycHAlcHFnMHEzJHCxJIgwsTFwc7EwMbKysnBxsInDfMLAY1YvdOCO7q39IE7QbJMDBVdm7QOx856+319sHmMPYl/mkzpge0rAAcT+m39y39zncmDxxXVu9j9PHQezX3xjdDBOsQWzPTZHOwS9WgRmsyy8aJ9iZAo2X+KPikOTyQcw+/hDPrsnnB5g9u+XfvuPX/0BZosBAHvvQavbrQIKAAABF3pUWHRNT0w5IHJka2l0IDIwMjAuMDkuMQAAeJydkj1uBCEMhXtO4QsE+QcwrjOpVtlii9whfe6veGCGEGUjjUAU78n4m4eHAPt6bLfPLxiLtxCAGEgA8Ok2M/hgRAx+/IUjV8NdUVRMeWdg9CrCK/yHmPdBqcyNglGQ0xqFYtXEvbcW5NUsCbX1+t0Ky0R5/KXQpNNE8XuYHPcojLqUxTtUc6NQzAVtotyvUziinAlUrK5lkZiEa8/CyvX6XHiiUCyi2iekqItZOGZVO/95KdezyK/pklg+slRafHX+1rBKz2Ip4QqF2pC4G24pqRtpz+rH5HHMVRkVVzqMqzobGz0GhKPihk7jikaCXc2V/VPNvAO83bfwDRNmpsD2cnt9AAAA/3pUWHRTTUlMRVM5IHJka2l0IDIwMjAuMDkuMQAAeJxlzz1vAjEMBuC/0hFQzvJXYueYunVoq+6I4biycT2EyoDEj69znRBT5DdPbGekcVztNr3u1yPvft72S8VRtVNaOtLLfdUxsFek1BEYaua0jciZqqcOQZAVW0bgplQTghdkpoUpGuUUHQqLRhK+ioqEKozG8RDBLIslglzQPW0ZUNBCmFSPNgIq7ByAjS0AQREzadMNbXmRzbymZYdSWk+SmuWfOCnSMtvROXtjVdvS6zT8ztPXZT73At/Xabq9D4fjCeL7MM2n17j8GM6f1+lwvID29mjs2XBfH019NnL/A3K4X0gBcgVLAAAAw3pUWHRyZGtpdFBLTDEwIHJka2l0IDIwMjAuMDkuMQAAeJxljj0OwjAMhf2TuuFnRNAxEkuEeodELFyFY8DGOdh7BdKVSzCzcQUSpaGRsGS9ly9Ptj+P4QWx1rERci2nvuAeTOICNgorgXNSxjYb+v1AnxSowz5NObSZ88Sx48z/8irzVVyOZInYsDKqsaTESAuijV5Y0mg2KKhFMe3KmelG769NE/JzdLP37v58Fx9mP4YqM1Y+HG8nV2V8mVNxV/j2CwOpIxmuDZoKAAAA2npUWHRNT0wxMCByZGtpdCAyMDIwLjA5LjEAAHicrZM7DsIwDIb3nMIXaGS7TROvUCYEQwfuwIq4v8gDrEh9qAUiD/9vW1/tRDWQzjic70/Qw4MxQJgC5kNE4MaIaFJ/a6OKaWjQOpI+5bDkDo8lRB2ZQtYtUY6wmYLWu0IhGyj4ryjNCuW6nfKXjZqV2x2nFK40zW2UbshL9+NGwBZ9cOuzUKXbuTeazrLjpffNUt9LpxTKBSqG85RcTJvb3pWonJqoem2LxmslqlAbUSP5X+KP0Y9eAE7XwbwAuV6LYPOE2PEAAAC4elRYdFNNSUxFUzEwIHJka2l0IDIwMjAuMDkuMQAAeJxljLsKg0AQRX8lpSu7g+O+3A0WiW0i6YOFEjsXRWIR8OMzECE+mpk5cw/3OhaYF2UR5c/YY8XKiLasGH3pUNVpjiQXCWh0BjU/I+g1JmCJETLMDKHY804Xm66fTcM6lcrFTiGxmTJL9T9cZ4zX7z48xn7wGl5TCJ9b3bQdxISh7y4U3uuhnELTjoDebh17dKR3W8cdHTV/AdS8Tqtg+R8mAAAAwXpUWHRyZGtpdFBLTDExIHJka2l0IDIwMjAuMDkuMQAAeJx7v2/tPQYg4AFiRgYI4ITiBkY2hwwgzcyMzNAAMVgYdEA0A6MEkw5ImxaGODMOcRaYONRAbgbGDCZGpgQmZgYmlgwmFlYGFrYENnYGNo4MJg6GBBFGNgYONhYmRnGY6xg4V6/apbZ6FdcBEOeh27L9oaFb98PYq1etsoWw1Q6Ehobag9gWO0SXwdihoVeBbFEHqHp7JPUOMDUgcZiZYgDfay8cMbZfxAAAAMd6VFh0TU9MMTEgcmRraXQgMjAyMC4wOS4xAAB4nK2RPQ7DIAyFd07hCwTZJiF4Lp2qdsjQO3Tv/VV+JIOUZEhS5OE9gz+MMZDXEh+fL+jiaAyAlMDNEBF4MyKafB5tUikNA1vyfu5yN9hD9JEpA1kWwapGukzJtcG5cxS2kwSqtQnSU5Y1hTrNHUXnsqYc6KVR2LpDvbhGgb/MBc7PZdzq5cJPUxk1V8PlrVSNK5c1M+mxpLzuJDOrSSr0RrRGSrrsPAHur2h+62Z+HEVqjw0AAADBelRYdFNNSUxFUzExIHJka2l0IDIwMjAuMDkuMQAAeJxtj0EPgjAMhf+KRzSjWbdubPPkXY134wGQGwuEyMGEH28hxEhmL6/5+vLa1ljX2f0Q1GO/qF6VWHE3ZQjKG2WbHI3IFaC1thDHfMZekuCGcIskSKfnYqbAeCcdsy8ygJ6IOND+Ys5xUi9rFKzwT2IamFyyF+Wri7eh64OG5xjj+1xWTQv8G8SuPfHwUvbXMVbNACqYrcekHh2KradIPTR9AONqVTBJxU9MAAABO3pUWHRyZGtpdFBLTDEyIHJka2l0IDIwMjAuMDkuMQAAeJx7v2/tPQYg4AFiRgYIEABiQSBuYGRjSACJM7M5aABpZhY2hwwQzcyIEIDQDDogmoFRgkUHZIoWhjgzDnEmmDgOg9FdANfICtHIDXQ1I1MGExNzAjNLBhMLKwMLWwIbOwMbRwYTBycDB1cCF3cGEzdPAg8vAw9fBhMfPwMfYwI3c4IIExsjHw83MxMbCxsHFzezOMz3DAJXrmU5HFT5vx/E4V0s53ArtgvMnvyh2/7ut8tg9t7843tfx9uB2U1n9u7/eWIFmF2169r+oinGB0BsTintAy4cF/aB2GmPGg9omFXDzDwANNMexAbadQBolz3U/P1A8+2h5tsCzbeHmm8PNN8ear490HwHqPkOQPPtoOY7AM0HqxEDAO4TV87z0HyCAAABNnpUWHRNT0wxMiByZGtpdCAyMDIwLjA5LjEAAHicpZM7bsQwDER7n2IuYIHUn3W8VZAUW+QO6XN/hLZkQgsnSNYWVAxFcUCZzxPWdV9eP79gyy/TBM7gAtCPW0Tw4YloWu8Hl7NEFTM7kRDWM3KaJbzgN4txby7exVJLc6HKdM5Fa1sH6pJTPucya4WU2FSJoZxzYRerr00FrvmsSy5lrZ29q5RkcLkfXXjQcXDx6hJSe1GkfG5Gc3SUqjQXqRz+30t46KVPGhcmPRt1OFD3Ry/+4et2XnCBFxgvuMALjBdc4AXGCw68PPU3dl5wgRcYL3iWl2QuvA2MW+A3knwLwoZ4z8StpmdUZctoUCxQVcdArEbAZBkNeA9Usd+vqVqf0TMBHMfAOlDF1kE/5t3Aun4Dbu/L9A1ruNpVWsQJzgAAAQp6VFh0U01JTEVTMTIgcmRraXQgMjAyMC4wOS4xAAB4nG2QPW8CMQyG/0pHrspFcZwv5ybE2lbdK4YjZSM6hGCoxI/HuZKcKJ3svH4SP8omQUo6rb5eo9l2c8V71VzLaNMl4KPdvlxXKJ0jdKIHSYQIYtDS+OBLoIIiEgM3hDqUxFlLXgy94shjABDceoPO8kWem/DLIQSHc+K8t/yYlkFZMhxpjtC4ctEoh1AwI5UNgUpGATTZmZs1mkVfTZtoX8Wa16K1WN2lmlNVakZVaPGpOs2mE+N5yp+n6Rit/L7k/PM27vYHyX8o83RY8/B9PH5c8m5/kib6R8Y/MxjpkaFnRkf4swz+2WavN7TqfJ00i0ysAAABh3pUWHRyZGtpdFBLTDEzIHJka2l0IDIwMjAuMDkuMQAAeJx7v2/tPQYg4AFiRgYIEAZiUSBuYORg0ADSzExsEJqFzQFGZ4BoZka8DKhaqF64WQ7oZkFoBh0QzcAowagDcogWhkFwBUwwBWjizBBxbgZGDSZGJgUm5gwmZpYEFtYMJla2BDb2DCZ2DgUOTg0mDi4FLu4MJm6eBB5eBh6+DCY+/gR+AQZ+wQwmQSEGbkYFdqYEQa4EEWY2Rm4uDnYmNmYWVjYgxcPHL8jFLQ4LJgZhnuZQ+9zHDgdAHMcMQ/vpCw7tB7H3mX+1z7umBmYnPAl0kIxZDGaLC09weDzrwj6wmv5uh67/XfYg9u9r9g45H26B2RFaj+3Fy8LB7PDUS3bvVu8Gsw/qd9lJZNo6gNj9V/z3S7+VAIsf2am9Pys+FWx+fcKn/WJPH4LZUsEP9q9pDQC77WBFwAHjLxPB4iX1kQdsT2yxA7F/Jc848Ni1CmzOUlvuAwXpcyBucBE5cPOYLtguMQByWmH61tYLtAAAAXJ6VFh0TU9MMTMgcmRraXQgMjAyMC4wOS4xAAB4nJ2TQU7EMAxF9z1FLkDkb8dxvGZYIUBiwR3Yc3/hTjKZSgNSlaqLn/7m9dt1trRfn5fX7580L75sW4InRkr05+3u6YuJaNvfp9ykxOP0JJkg3p+FS+kj/Yc43oNSXa4UZGXSA+X5PAXZoW2nBE9haxTJbF57FrbKa5SSlVh6lkJWVilSIb02auprFM7upp1iWO+uWRk9bQV1jRLdAHnnlbraXcpc3Udt6seKzk9d/JkGtL5XffEf7bNmNKaueaU1CmKvjKmzWtoqxZS1n0bWduzu5yMFj3q77b12I3goJGtZJIu30VNRwRqlRDd41OFm9XxFfKBwhmntU8eMtXMUFEG1PnUGlfNZZFJwDcZ9wVcHfRGqTCeUTidUnU4om06oNp1QPh1PoOnEAjcnFGaCUJgJQqEcFzpf07Sf9+HUBDsuZoJQmAnGRzCz3b/T7nHeUnp5v2y/dJUCp5MNtlgAAAFIelRYdFNNSUxFUzEzIHJka2l0IDIwMjAuMDkuMQAAeJxljz9vwkAMxb9KR4Iup7N9/nOJGCrWtnSvGELKRgRCZajEh6/vukGWPL/7+dnebbYw41w/3K42u84LmFdf6wH23dwEukD/0/7lvkrRKCeC0FNMQGZhTFEKlqKhh8iYmMIIsQCrhN7fGDSVMFJELSwVQhXkMObICYkqlJNmwmqRAAWIybh4jdFz2WuFlKXmqmal4FtkkOp4LyQf70wWgbYOSrEiwZu5cA6jDzAAI/U+LkpizRNNltp4c9DUTXBJ3C5RydYcZfQQPxfZwCc2pdQgyAn/LSqWPZ6Y6rF99n5EN4q6qA5GUJbseyJCgeYQiKjvqcAe04Xp57x8Xs+XASh+35bl9206HE9xXevlfHr15/fp8nFbDsdrhAHkgZJnCgewB8qeKbr/AfV9fCAKWr1JAAAA23pUWHRyZGtpdFBLTDE0IHJka2l0IDIwMjAuMDkuMQAAeJyNj7ENwjAQRe2zYztxHFEgWIEt7FGudIOUEWgoGIYVYpcMAAUFlLSsgKMQ2UIIcdLXvZOe9HXP4XgnadoUSqbpUhYpOyqcT5uxr7AZgef9h/pD+XA1oR4oIDAPjCOvPHCBQnqQClXtoW6w0R50i63xUBE0Cg3DJROk4gyoEFKZ1Fk3ujVqPf82Pte7mxyG6XTuerqEiaPN7Gx2oj0ftnb2C7aPfefeHDLHUDgxcx+L3lj0hqI3zM7qBZs/Q4wlKitUAAAA5HpUWHRNT0wxNCByZGtpdCAyMDIwLjA5LjEAAHicrVIxDsMgDNx5hT8AsoFAmZtOVTt06B+69//qgRI3Q7skRpa488HJGDvq6zFfX2/SFWfnSDJJIeKf0VqjZ2Rm18+nUCdGmjyHVCX2HAeoTGf6Z7GNxWXcgIuEUnmni4TJwIVNXqS14G6LtR3syyEXfRHFEGPZ5+JtXEz64k364k1m15vMrjeZXW8xu4IPHtEJtjRSnQBlVYAmVYCKKiBVFaCTKkBNlUbCqwIksipAohUAiVYAJN8KykgvSt5Yd5IXciO63Gf3Ael7wXEteB69AAAAfnpUWHRTTUlMRVMxNCByZGtpdCAyMDIwLjA5LjEAAHicXY5LDgAhCEOvMktNgAiMv3gcruHhR92MuiF9aRtqbGZiztgPMWFcebpTyhEwkGbm+EJTQKaUA1doTHGjcCWnG6hKLmX1fr2SQiKpCjS88OyhHnD9wH0OnnvwGuT7B4TlL9v5KdzfAAABQnpUWHRyZGtpdFBLTDE1IHJka2l0IDIwMjAuMDkuMQAAeJx7v2/tPQYg4AFiRgYI4AdiASBuYORg0ADSzExsDmCaBUYz6IBoBkYJJh2QJi0McUaYeAZInJmRHaKAGUYjDIQqIIbBzcCowcTIlMDEzMDEksHEwsrAwpbAxp7BxM6RwMGpwMmVwcTFncDNk8HEw5vAy5fBxMGYwMeZIMLExsjBzsbCxMbFzcPLxykO8ycD/2eXRfsOXuA7AOIoKRbsj9wZux/ELp8qfYBnmQWY/Scp+IBZ3w8wWynU+cCCxyr2ILZG4cQDb795gtlz27kPMKx9B2bveKm/P0r5DphdE9G+l7dqqx2IfW3JNvsXy8LAbFEhZoef7yeDzXSZn+SwoHIZmJ1i1uvw59nivSC2/Noih/S2JWBzPsgLO/AKbAezxQDZOk44jMAT+gAAAUB6VFh0TU9MMTUgcmRraXQgMjAyMC4wOS4xAAB4nJ2US2oDMQyG9z6FLlCjlx9ad7IqTSGL3qH73p9qxo7j0BYSG8P8QtKHpNFMgP1ctrevbxiHtxCAElAGwD+vmcEnI2Lw8BeMQtl2xZHFE/1gdC/CB/yHmG+nmFRsquZME+X1cQpHFeqUwiRrFImCfHRE0ZTqRLn8ptCk+Y6CiXsfWXWxI41JU88ttdrjtdDdXKgi77nk09WyVov3UUunFLJ5LudnKDjeryTVJYpXoJz7NBgtL3UEHFHb1lGklOoaRfwdUacI4mItGpXa1nluRVytpVjbWIpck67Oha12ikrbnGcpdHwO3Ax/SFvJcCidjTTCXOXhcaMMw1UdYa5seAwIr4YromuYK+LhYdh/B90jQKMCV3SrwA7VPemGfgc4nbfwAz2bzSkJPIv5AAABFXpUWHRTTUlMRVMxNSByZGtpdCAyMDIwLjA5LjEAAHicZY+5bgMxDER/JaVtyAQvUeQuUqTP1QcpfHVe2zDiIoA/PtQiTRA1FJ/ImdHb4452i4/VwJ/LuVLW04nWifuhh/tijSBkIVzWDCykUcZkIY5oJW9uRtghgwp10pjYNYmAIEduEoTSL8LK2AqCqWJ4IoWqNbVy0T1YZylypFoo1dXq7GjeyJI0CpeZoBm6dS2pqtrKSKBsQUkYw5jKyIDa0iYjUK3Nyihph9E6EcSayRU0A0ePjvkrQe1TLcg5/dir1C7E4TNQQfKyLJuv8/R+PV8Ggf1tmr6fN9vDEVbZTufjUz6+bC6vt2l7uAIP9e9M/T9D9x/3YV4xe7TiXgAAANd6VFh0cmRraXRQS0wxNiByZGtpdCAyMDIwLjA5LjEAAHice79v7T0GIOABYkYGCOCC4gZGNoYMIM3MDGRogBgsDDogmoFRgkUHpFqLHSLODBdnhoijqWeSYISpBxvIhG4gkwQbRAE3A6MGEyMTAyOzAlCSmVWBlU2DiZVdgZ1DgYNTg4mDQUEEqJeDnZWZURzmXgautLQ2+4duy/aDOGlpz/bB2LNmztz/0E3tAIzNwOCgBmKfPaMDElNHUm+PpN4BIg420x6qBkgnLAOxjY2DgfINYLYYABVFLzhaiGPvAAAA3HpUWHRNT0wxNiByZGtpdCAyMDIwLjA5LjEAAHicrZM9DoMwDIX3nMIXwHLMr+fSqSoDQ+/QvfdXnVBMqoDIQJThvRh/cl6Eg7Dm8fH+gC0enQNPYcP+FhF4MRG58L1HakmPofLIIkEBoVYJbnCESHegVITNBRTtpYXC2MrgE8qcU3yimz3K2rupqXwWxv6IcjJLvZcLXJILlOTC+Vz/L53PUp6L9g6/N8pyKb8R1FgfUU5u1BnFxwIvhmPu3kxjRlWbms6Mqt4AagarqBIzEv8lXk04jpUnwH0a3RdATYqyUZZDyQAAANZ6VFh0U01JTEVTMTYgcmRraXQgMjAyMC4wOS4xAAB4nGWNvQ6DMAyEX6UjoMTCJDg/FUPF2qLuFQOobEQgVIZKPHwDbZEiJt+dP59LLMrokVhZx9U6RR2XUeEF+qDEVVF9WiKENGccITMmlezMU5CB94rxDHKjU/2zBEQoZMeRfJKBYghKkSCf5P+KsGEv2N7tOwTNuPLHmNO3ToBgXIPWqPSWxKx5De4+DaPN4Dk79742bddD4q0b+otf3pqxml3bTSCtDBl5ZISlkKEjg9aEjDkytHwAYLZfa5LvUk8AAAFEelRYdHJka2l0UEtMMTcgcmRraXQgMjAyMC4wOS4xAAB4nHu/b+09BiDgAWJGBgjgB2IBIG5gZGfIANLMjExsDhogBguMZtAB0QyMEkw6IF1aGOLMOMRZUMXZHMAWMDMiMdB0sMJ0oCngZmBUYGTKYGJiZmBiSWBhZWBhy2BiY2dg40jg4Mxg4uRK4OLOYOLmYeDmTeDly2DiY0zg40gQYWJj5ONgY2Fi4+Ti5uXjEId5moF/v9zdfV3b7R1AnLrzavu9Gw/ag9h7fzIdWHW6FMyeou93QNPiE5h9lFPwwEt+tv0g9mL/rANZizvB7HvLu/d7Ru0Cs98eX76fU8XmAIhd0T/bblltOli8y3+vvbrga4jet+YOm0qXgNmr/7o6HDjgbQdiH1nY7yCt7gC2a2X/V/ucUxPA7NvsbPZvY0XAbDEAYJxO9oN1VcAAAAFJelRYdE1PTDE3IHJka2l0IDIwMjAuMDkuMQAAeJydVLtuBCEM7Pcr/AOH/MCA61yqKFdckX9In/9XDOwSTkmkPRDFjIxnx8bLBnXdr2+fXzAWX7cNSIESAP65zQw+GBE3P37BEIWsZQYzTRVh8CjCDf6TmPeukpRaLgUlKpPKy3kVDhiT9lxLSGsqEpgpdy/FWCaV+28VmjA/eOGUuNemLHnVS0pU67hQwKxPeJFJxXPtUImqtuaFghCWXpuJ5fNe4o+KZwhK6X0xjLLkxW8mZsPuqshx58+qcChZrKtwiWtTB+LdlX3uyZqrBZUYYil7HVkxne+uPvTFqMSOSIjXvLSJ3R1otrSiQu13oE64zSN3Im0gaBAdxFGaSR45jsqIOLFBDAiPY47qmPeIo1r/HmEgGREnw4Ejf+2OYzq5ruT4zjvA6+26fQOOC81gTPr22QAAASN6VFh0U01JTEVTMTcgcmRraXQgMjAyMC4wOS4xAAB4nGWQu24CQQxFfyUliQbL77GXKn2C0kcpYEPHCoRCEYmPj3eVBlHZc+bMtTXbkcbV58vAX89Llf+qVXkcl96qH4mfbqs1ggpFt8aQaRZtU8iNHLURGFH3QgyortkQ0pEkCwkwk1tJkSy6SOzO3CrAWDwWyZ2I2poAu0mRapLQZ6I1LhYkhD1bBaRkbxsEQQnOOSlR2altSu+ROb8Lca2lGKJLBRTh0KgNpLKlZATKyF5JChphBbqhzdEESaHz1iREMc+qXc16OdaXD3huu5/T9HE5nQeB7+s0/b7t9ocj1FfCdDq+1uX77ry9TvvDBXiwe8ceHRn6vdMfHR2I76U6P1h2+wP84niao/XkkQAAAZR6VFh0cmRraXRQS0wxOCByZGtpdCAyMDIwLjA5LjEAAHice79v7T0GIOABYkYGCBAFYjEgbmBkc8gA0szMQIYGiMGCTjPogGgGRglGHZBuLQxxJhzizDjEWXCIs+IQ58AhzolDnAuHOBtEnJuBMYOJkUmBiTmDCSjLzJrAysbAyp7BxM7BwM6ZwMnFwMmdwcTNw8DIm8DLx8DLn8HEL8DAL5ggKMQgKJzBJCzCIMyQwM2UIMLExiAsyM/LyMbMys7JzSQOC1wG0TtzD9p7O+zcD+LUzvawvzaHex+IfTrcfP/E80/3gthMf3bs354/0x7E/r5EZ/9jIXkHEPtylc2ByLY5YPFHStkHWjWVwOLTz+ceiPrdBNZr8mzZgTolZTC7Q93+QNqf9WC7fGoKDlhFah0AsXNC9+3nEN4MFp/P5bT/Tr4uWNzt+zH7EqfJYPMD/YLtJ7XLgs0vrXB2uKQ9ASxucbbE4WOBFFi8WaHI4SObLdj9q/lWOvzfHQpmHzjt4PA2bA/Y/HPF+Q7nBY3A5osBAH22azqKeNuTAAABlnpUWHRNT0wxOCByZGtpdCAyMDIwLjA5LjEAAHicnZQxcoQwDEV7TuELxCPJkiXXIVUmKbbIHdLn/hPZBi/MJjOsGQp9QJ9n8c0S6nFb379/wjhoXZZAGIhCgD/PUkr4IgBY6vMYBSl78YKRWVO9BtHvQngN/1kcz6V3qKXmAhFTnnPxXsVMmwvWFcy41HUI9bVhMZ1lycrcJhrZRA4ut0cXfKybC8XCAp2FiGyOJVUW6CySkl1noZOLolhfG+Tu9zyLRELZvjSk8gRLOs/FsvWvlZTL7FxUNHe/nFGvs/A5L6awsZQyORfPrhh0FkWA6yxyd6m7UXrqPLsCMsXiHUayZzcjX2exg0uKILzvI5rc0+7iLFt2GRNeZyknF/+/7H8G4zzH4tm1BN2FwK7nBc8rAiTekqOn6T41F+XWW/NilK7PJQ8XbBucuqC2w7CL1CK+3eGWMRwiD+GVHoWNHmvzx10g7MIrxKMYBF7hncAFj8c4oBxFHj054CCowobwl5aDINh7vKJBQB0Md4Ixg48Q3j7X5RdlqRqk8o8uXwAAAXp6VFh0U01JTEVTMTggcmRraXQgMjAyMC4wOS4xAAB4nG2SP2/CMBDFv0pHqILl+2ffmal7W3WvGICyEYFQGSrx4Xt2spBkencvP1+eLznCcbU54nH1/Vpgt26Ko9Ko7IquMvY6qo0K0QvwIu1eHisIApio20Bgzpi7bQxZKaFbMQAl4m7rVYYEOniACOhmPSFgHQQwzdiwlJkMOwysIuQWBmNxBBFVvKeK+HMhytr6DKJ1bkxkktySgOBFtcgUG+ZjNGWrMSmz2nBSsriFISXIQyBNpg0yi0MihyxLpXI08wh+Y2HGGlsitwsrCkNNxcllSyEK+wKcwHZ/qkSkCgANhO9oXJGymE/x1EqRq4XR1yFtDiBz220ej/l7uIVR9Mnrbv976b9ul2vh8HPv+7/3/eF0Dv4VQ385v/nDj/31894fTrcAJT0zac5g0WdG5wz5L/AMeT+juABOKJxTUmASHBaSa4FJdFjIbgUm4WEhPcSCk/i4ED89/gFUzdYbSf4EyQAAAZV6VFh0cmRraXRQS0wxOSByZGtpdCAyMDIwLjA5LjEAAHicjZC/S8NAFMffXe6ankmaS6z9IQhFRKp0EUehyeKiq4u4dHT1DxBdHK10KCh2EEQ3F0XcTMFJBxdHoYqDixShi6PeXRNzHQQP3r3v+9zL917u8/aiB2LZIhAMV1nElIhd5EFF8oxKBGpVeYxKpCZb5zOVGZFwymnMQdZGytEfHOs+KOXGqH9y/ltDQ2Y8yhHJqjFx0oZgaPffbIkHQBiQIUYk2KBAM0DNLDazYDJgYwQzCywbbAecHORccDlwDzwfGALXAp+DbYDvgDVOsJ0n2JkgyC0QxIsE+yWC89TwuE+RQU0mdttiNOfYlit2n7vF5PmhvFBf77bbLJBFZ+k74otsVuqYR1I/NJrR49qx0ofX91Hrq9qV+mB7WuRQ8efLze7+4C3SeKDxQPMJNJ9Q6rn3k3rSH2vl8/LUC5J7y4ON4PWof6Zx1T95sxIm3y73m+HeR+tc41HqybW7uJrf2zkNkn+Jtepx77bC1StT16qn8ANoV22ok6zQ+QAAAYt6VFh0TU9MMTkgcmRraXQgMjAyMC4wOS4xAAB4nK1Wy2oDMQy871foB2IsyY/VuSkUSlvIof9Q6LH/T+V1IztkC4mdZQ8zeDUeWZbYBcpzOr5+/YA9dFwWoAgkAH73FRH4JO/9op8f2LGEUCK9i5z/kK56ePr+T6J/NxV0IsF3sZ0K3KxiXg5XXk7XKtjhcOEFkbgiQo5jXtDFHKUgcil4vN1L7FRI81ilZpTj+Lkkrl7Q5cz+di+45wUe4gXu9UJ7NYKH1AjurRE3FY1gDDx5Lk1lptLlTINfJ++uRqxRqHoZ7sbmZaZGwA7TuZPHzyW4kFLczehjwMtUjdp9IUeXKi8j92VGRSujTbg7pUZUrvvoDhXWeZBjVUHyPKlyGFfBbdhgJbR1eyPBSNhmtZJUSbIVRbkna51ey4bEVlZAfyaKEM9qhZgDRWgOFKE5UITRSAQ0B4rQHCjC1YhuKt2mLdMS04j0AtylXVZC5428uWaglkIAqmq4SRMbyUDBPpPys1HJG8Dz+3H5BcF/U20sPFhWAAABXnpUWHRTTUlMRVMxOSByZGtpdCAyMDIwLjA5LjEAAHicZZDLbsIwEEV/pcsE2SPPw3YcVIkqGxZ97VEWoLIjAqGyqMTHd5JACMnKd+aeuSPPZl1vqtWqRqq6N9us6zzrJHf6q7e51b3MbrKmbGiqXJS+zqv2xTp/7QT1HLaa1aSsOuSqpX65ZggUI5OxBEEcolkiRHGFGKsWsmezdFD4hIGNFUDCSHuL4QEO3D1riGLAEIWNg+jbKsaoHGmIU2wpICFIaDc5JN/G+seQfZ6yw5gDRiEfbrFDSUDj0k5s2/u6DJGYR/9T5aMv/OgIqjwXCe8pliGwT9RO6R8dj5AJ8QDum+aLHnsQUlJpBGJwLvSX1SxOIqTRnvUWadSyQy83299j830+nkr08HNpmr/37W5/gEVbN8fDm9of29Pnpdntz+BLjBMqziksMU2oNKeoJHymtJ5RXJJMKJlTcv0HaVu7MNAOivwAAAF/elRYdHJka2l0UEtMMjAgcmRraXQgMjAyMC4wOS4xAAB4nHu/b+09BiDgAWJGBggQBmJRIG5gFGRQAImzMWgAKWYWGA0TVlABUkzoXDYFExCXES7OkAHSxYzEgKsE0UgKQRSMB6UYwXYiaG6gMxmZOJiYmIEGMLCwsjACncXGzsDOwcLIzsnAycXBxMXNwM3DwMPLwsjDx8DHz8AnwMDDyMDHwsANVCnIwsgtxMIowszKyMTCx8PKzsbNxcnKx8PNxiIOCwAG4U3H3R0stsy0B3HSXz+3t835YAdiT+TcZb9zZc1+EPt5t6CDRiv/ARC7q1Fv7xK/HrD4/ugOW8VnqmDx0Gkr9p+ynrMPxP7JaXLgtdIVMPvSyrQDLNo3wOonukYcYOioBNuV8prvwOoFP8Dsfxml+38mTAazbSZx2h+MWghmX+BYZd9qp+QAYvs69Nqf6WcGm/nvvqrDnokFtiB2T76og4wmxJ06Fdf236t7CGYHLJXcX/RCDaxXDADclFYwZcRKHAAAAXB6VFh0TU9MMjAgcmRraXQgMjAyMC4wOS4xAAB4nJ1VS07FMAzc9xS5AJH/idc8JCQECxbcAYkl9xdO8xr6JFg8R1E108STcWOrWxnj/fLy+V3WoMu2FfRCWAr8Od29fBAAbGM/VyS0gbDGyz4Q1FiF8vj1n8R5bjO2Yxy6x4q5nVXKHSqiLAEeoHpvkFOhSk1oqAQS0lRG4QCERh4PWMENc14iwsymF3WinBesjA2nKwbNeQkHHfmqIiicU+FqIDhdWe+5egkVbn79Gt6kZTMi3jOKynEhyamEA+3XDkDFnEpEKDe59pFBugOYuswqVr696Xu6EYF15obUNedlVCwe96u95bxQZRY/eloh2wHWVCdqhmcvz/fctAHQdGXQPKMSV0L7c5BAvD8HCSRrJYguEsh2gpO0tRKoLxLI17ZeEI6VQIjHOYFwOQiEvAgXlENgkOUgENqZtFPMbz4jQE/SN0axL2kaP5hJXkt5ertsP7/tBBs5/s3mAAABEHpUWHRTTUlMRVMyMCByZGtpdCAyMDIwLjA5LjEAAHicNZBLigMxDESvMssOOEb/D01goDd9h9A3yBFy+JGcycYUz1VS2c/zeh6/F9LxOEqc13a8biUKbc/z+sqCRx+P4//+osU+N6+f93aHaQDoOaiEp4y9UGoY88CJik1oEjNSgRRqwJM9SUdZXZxxeQIZcVRcUJhy2QykcnecFhFtw8nokG1jqOmymLmqtM0NLVYHEKpwM0hDHXshM40eR1OT0MeOU5SFelqGpZWturqgtYmkKpYnEMOrq1iq29h5IqFaPYcAuhVMZeeIJgYaHWIK7l9RVlibEFjXJqQQj96kiVFzK+FM3oRZkj91FIDG7f0HO2VZfmZGBNIAAAEuelRYdHJka2l0UEtMMjEgcmRraXQgMjAyMC4wOS4xAAB4nHu/b+09BiDgAWJGBgjgBWI+IG5gZGNIAIkzszEoAGkWBh0NkDyjBJMOSK0WBwOIz8zE5gCmWRB0BohmZsTGgCiBG8UCMQpiEzMjXJwZIs4NdBUjE9AJQD0KLKwZTKxsCWzsGUzsHAkcnBlMnFwMrNwM3DwM3IwMnCwJIkysjMwsrNxsbOwcnCys4jAfMfA2WPsd4FyXtxfEMZ38dn9GN789iD01WP2AQM5bMPv4V9X9Bot2gtlFmkL2DVOEwewDf9/Z1iZH7QexW0Mn2Gdx/QazWT+oOog9vwhmlzA5O2yPCN8HYs/3Z3LQuJ0K1suXJO9w6ouIA4gtzb9x/6+HMRBzigUO+DqwHgCxxQDYjj1gN15k5QAAASR6VFh0TU9MMjEgcmRraXQgMjAyMC4wOS4xAAB4nJ2SS05FIQyG56yiG5D0RaFjzx0ZNbkD9+Dc/UceHi6JmpzQMPia0j9/oQFa3I+Xzy+YwUcIQAKkAPjncXf4YEQM9fqTRGbWRhgxFWkalWrAM/wnsZ6uQrEko9GbDPOeCkcjTa236akvKvffKrQwLyoYTcccFDXhOtH7ZS9tjox+kqWtiWoHkeXhqiShPRWKxNnGO7vR3h8Bx+RchoqJ+56KRNSMYyIm3PaCcu5L8bzphaM66iDhrNf3RZd9oShF5OePrL/Q1u5yyjQIC+N1LzJVqC8yjYR75ZHoTCqlmVSysf+hU56VSmVWKvmsOBCelUpEs2JA00ElkiV5eGvdp4NXgNvbEb4BrcCze9JGTEoAAAEOelRYdFNNSUxFUzIxIHJka2l0IDIwMjAuMDkuMQAAeJxtkLtuwzAMRX+lo13IBHlFSZQ7FV772osMeW0xHATNUCAfX8qd+tBC8pC8vNA0Sfd+P2LTv+6x7/b+4LVu+kk8xs3drRsiAYgWBiZOhiIaHgYhS5lTYPJQII5AWVQttJZadsKU1WBwpInhyMcLV133ijdTQyI519T0LUU21xISlCzBz9QsbRGUKkppJMfa1COxFkZbgzBqbUMcs4iLWy1WUiNanQVQRNFv49E8X69l9xZX53Bb7sAFDJ70YfuxzG+X5TyCDtd5/nza7o4n8q+ieTk9evN5e365zrvjhTAK/xzy+s+UjvJLSv7RircvlQxjhORSepAAAAFEelRYdHJka2l0UEtMMjIgcmRraXQgMjAyMC4wOS4xAAB4nHu/b+09BiDgAWJGBgjgB2IBIG5g5GDIANLMjIxsDhogBguMZtAB0QyMEkw6IF1aGOLMOMRZUMXZHMAWMDMiMdB0sMJ0oCngZmBUYGTKYGJiZmBiSWBhZWBhy2BiY2dg40jg4Mxg4uRK4OLOYOLmYeDmTeDly2DiY0zg40gQYWJj5ONgY2Fi4+Ti5uXjEId5moF/v9zdfV3b7R1AnLrzavu9Gw/ag9h7fzIdWHW6FMyeou93QNPiE5h9lFPwwEt+tv0g9mL/rANZizvB7HvLu/d7Ru0Cs98eX76fU8XmAIhd0T/bblltOli8y3+vvbrga4jet+YOm0qXgNmr/7o6HDjgbQdiH1nY7yCt7gC2a2X/V/ucUxPA7NvsbPZvY0XAbDEAYKRO9hzKV7kAAAFJelRYdE1PTDIyIHJka2l0IDIwMjAuMDkuMQAAeJydVLtuBCEM7Pcr/AOH/MCA62yqKIl0Rf4hff5fMbBLOCWR7kAUMzKeHRsvG9R13V8+v2As3rcNSIESAP65zQw+GBE3P37BEIWsZQYzTRVh8CjCO/wnMe9DJSm1XApKVCaVp/tVOGBM2nMtIa2pSGCm3L0UY5lUrr9VaMJ844VT4l6bsuRVLylRreNCAbM+4EUmFc+1UyWq2poXCkJYem0mlu/3En9UPENQSu+LYZQlL34zMRt2V0XOO39UhUPJYl2FS1ybOhDvrhxzT9ZcLajEEEs56siK6f7u6k1fjErsiIR4zUub2MOBZksrKtR+B+qE2zxyJ9IGggbRQRylmeSR46iMiBMbxIDwPOaojnmPOKr1HxEGkhFxMhw48tfuPKaT60rO77wCPL/t2zeS3c1hW11uQQAAASN6VFh0U01JTEVTMjIgcmRraXQgMjAyMC4wOS4xAAB4nGWQu24CQQxFfyUliQbL77GXKn1efZQCNnSsQCgUkfj4eFdpEJU9Z85cW/M+0rj6fBr463Gp8l+1Ko/j0lv1I/HDdbVGUKHo1hgyzaJtCrmRozYCI+peiAHVNRtCOpJkIQFmcispkkUXid2ZWwUYi8ciuRNRWxNgNylSTRL6TLTGxYKEsGergJTsbYMgKME5JyUqO7VN6T0y53chrrUUQ3SpgCIcGrWBVLaUjEAZ2StJQSOsQDe0OZogKXTemoQo5lm1q1kvx/ryAY9t+3OcPs7H0yDwfZmm35ftbn+A+kqYjofnunzdnt4u025/Bh7s1rF7R4Z+6/R7RwfiW6nOd5Zd/wD+wHibj/8IVAAAAZh6VFh0cmRraXRQS0wyMyByZGtpdCAyMDIwLjA5LjEAAHice79v7T0GIOABYkYGCBAFYnEgbmBkc8gA0szMQIYGiMHCoAOiGRglOHVAqrUwxDlQxTEMwNDAgsMgZhziTDjEGUm1mB2igZuBMYOJkYmBkTkBKMnMmsHEypbAxp7BxM6RwMHJwMGVwcTFzcDFk8DDy8DDl8HEx8/AJ5AgIJjBJCiUICScwSQswiDMkCDEmiDAniDCzMYgLMQKtJWNXUBQiJWNg4uHT4BdHBayDKK1j5oO7FzQbg/irFvReqDC5ME+EHtr94YDd9z79oPYG8StD5g3LASzhZc4HGCfbnAAxHZefGr/ly4xsPi6/Sv2Td6xBcw2jGm0X/3bBsw+8lzA4f2H42B2UjS3Q/J5Z7Dey2vTHJJzksHi+4tnO3hr3gazTa1yHLzfS4Pdo8Sw0CHZaAmY3cUg47A/azuY/aZH0cG7x9oBxP5e0mPPnOAEFuf8LL9PevEpMLuua//+XRcywOzX//QPlN2+C2Z3FGkdWH7ED6xXDABF9G615PFy0AAAAZx6VFh0TU9MMjMgcmRraXQgMjAyMC4wOS4xAAB4nJ1VS25kIQzcv1NwgSCXjQGv07OKMoss5g7Z5/6K+TSiNTNShycWZRlKZZfhXaF9H7e3z6+wPr5dV2AElhDon8vMwh8mosu3v6RIRmgnEUktN0TRsxRew/8o9jVZUHJtiGKSYmcsGhWlaXlBBLBr+fibBRu2jYWjschg4YxypkUiwU90vqLGz2upGwuiVsHoi9aKMy0UhbszzpeAs+42f0F58BWpfMbC3tM0PdLMdMqCUnT2WU2f727aWCRmSmlUVGtNZ1pSrNon1isq4B84LY9aDNPfTHbmdNBIAhluceX6vBZ+6K7fQR0syUfn1CPlxAOZUHpeCx6nzoqMs36P5PQGwIdt8M0bdcDi3bA86zCys3lp70EyG1qKyGFFHHMWHpPDQj9wuiwWdNt5BNznEStIK3Cke5DXmdzZZqb0p2tman9TZ8YCaA9w3+YIfM+0YClwhLQHus5oaC87VrAUOMJS4AhLgSOme8aR/99mhoewtS3kja1V0YP3EH79vl3fgcYcaQVeoT0AAAFuelRYdFNNSUxFUzIzIHJka2l0IDIwMjAuMDkuMQAAeJxlkb1OQzEMhV+FEVBq+TeOLxM7IHbEUC5svWpV0QGJh8cJXdo7WT757JyTzDTfvt1P8X43asvK8yyj0bMo58rnSlllTo6y8feb39uNAgaSFQK0sCgPqZDXpmWDoOJdMTBytLIhICLzlBiCRbhLXMklJQEkRCp55hbMKRFYE6K+ylpDa6khCIc176NK1FKjPlmlYy6N080D51qlcaVVRu0KuWsqMpzmjQIVVWofa61JN6HQzHNljjl1C8kEpXVMNigFAxRiz7zcuKfjnjLXpZturAvGylIyoqDqsBeeWXHkch8hqNui8p9wRNVIxwkFhoqNN3KN6Js9n6oOpVZhLQIs6FbuyvZ7v7we94eJ4fO0LD9P24+vHeSPwbLfPebh8/bwclo+vo4Qk14yumbaFJdMrBmdiC6h7FeUTCRXlKwpnsiuKFtTNDFexcM15b9/ZEC5yk3+XvQAAAEkelRYdHJka2l0UEtMMjQgcmRraXQgMjAyMC4wOS4xAAB4nHu/b+09BiDgAWJGBgjgheIGRk4GBZA4G5higfLgghpAipmFjSEDRDMzskMYTHAZBh0QzcAkwagDMlqLHSLODBVnlGCGiGOoZ4GIcwNdxMjEwMjMwMjCwMKqwcTKpsDGrsDOocHEzqnAycXAya3AzaPBxM2iIMLIxsLNyc7GKg7zBAOv1frcA//Or94D4vj68h1YXiJhB2Jb6EgeyL900R7ETs5jPuC2dfV+ELt+rfX+5YaLwWpOunPty35wA6zmUdVae4d578Ds6hv8DuoTNMDsDKt8h4vcIWC2vvJl+zvTzcHmLFqj5CCt9A3MXvJLya5NOBnMPrHzwb5H5wQOgNhiAEXDPT3EN8RpAAABKHpUWHRNT0wyNCByZGtpdCAyMDIwLjA5LjEAAHicnVQ5bkQhDO05BRcI8gbYdSZpokwxRe6QPvdXDPxhiLJoguXiPRm/74VPiM0up5f3jziNTiFE5OYRfnQzi28EAMGPP3CqyNoQJCCEpuHILT7H3yRW7yqUiDiPXBS1ReXxPypszC0XU2Gj3Vogc2kIEwvmPRVIlRlHLqPWvY58Gpzh6MiEt1Q8l3M+5qJF1rmc71fxHUktI7dklb1a/L5wPfarpGtHl+8qtGD80lERy8ecsW53lAV1bNqI7O9acMG8qPiOcr9rXotqha25eK5wv/ftXyhU7p+LTBXsARyEepU3IivJkzgqQy90VGfEkU7iyOYxjQjXiCPElcwKHLVHhK5kfvQ1xqfzKXwClkqxwWeilQYAAAEVelRYdFNNSUxFUzI0IHJka2l0IDIwMjAuMDkuMQAAeJxlj09rwzAMxb/KjmlxjPXHtpTSwwj0tIXdRw4t662hpayHQT/8XtLTVhBYevo9Sd71zW6F6GnbD32z/Vx3NK6GBq+Ms4pEx5d700qsJMahTTFx8iocNi1HZtEaUiQ1s4ckLmhSLOIsi5KyFAotRVECvcGMKkJUYBSyKrZoJDmJz05XyWEDPmchCFYUs7FNa3aYSjYFgJukukIwNkmLpahnmq+sVAlb4cpK1ef1zuyYg03ZETNlVhM9TlIxRzp/qnCRsAr77/P0cT1fOotft2n6edsfjqe4RjmdT69ovu8vw206HK+ROkp/IdRPlHTE/yh+pvT+C7K/Y6GrCjquAAABOXpUWHRyZGtpdFBLTDI1IHJka2l0IDIwMjAuMDkuMQAAeJx7v2/tPQYg4AFiRgYI4ANifiBuYORg0ADSzExsDmCaBZ1mh9DMbA4ZYJoRJsCgA6IZGCWYdUCmamGIM0LEYQbBLMJQxwRRx83AqMHEyJTAxJzBxMySwMKawcTKlsDGzsDMkcDBycDBlcDFrcHExZPAw8vAw5jAxpQgwsTGyMPFwczEygJ0P6s4zHMMfG9ese5X2mLsAOL8+PHBuvnCZnsQ+7t74X6eHZV2ILbRt0v7DiRM2Q9iL+g/tf+OJdsBEFu83fTABsHFYPF6M5kDn0UMwep/20cc2PhlNdic63s77BniN4PVuC05Y59QbQzWm6bJ6PBOu2wfiC3xPN7hK993MDs2Z5+9y6UpYL0rGWQcHm8VAbtNDADdrUkHKrxh1wAAATt6VFh0TU9MMjUgcmRraXQgMjAyMC4wOS4xAAB4nJ1Uu27DMAzc9RX8gQh8ySLnulPRFMjQf+je/0dpK1YEtAUcCRruQPF8PspOsK3b+vb1DX3xmhKQAhUA/HO7O3wyIqY4fsFcWGjvzIbVN4Q5qggf8J/EuNPRUXVDlBXVBpWX0yrhxZVL62UVn1VRYtwQZSrOcyqUi+1eLpzRqwwq1/Mqkaks2vS4Ks554ayK90ypss95kSzVvM1IVHRQuf1WoQHLMGnKuJi0N1KkuVxCpbjZPSHUJ7zQoBKTIeM2c45rN5UuSFa3cr85dty/Z7+ASMNqbYgWrnO5xKSlloaEZTmfC3cV2gk3wvv0qJFA2iuBSq8EWnolUO2VQNZJ9HgnDoQjoYMEou5gI91BoPg1Pch25vGc49g7wOt1TT/zWsBul3oZ+gAAARp6VFh0U01JTEVTMjUgcmRraXQgMjAyMC4wOS4xAAB4nGWPu27CQBBFfyWljdajee3urC2K9ElIH7kAhw7bCIUiEh+f2VAhKmvunvvwbjvRxFOzTAt/bXoZ26XxL43t1Gx37UJ+8PhyazqEyEKUAoNhthwGBMQsUUogUFQLg0NFOaIGBFaxlP41JUaJoSOgWNglgmgcKXQMWDJXp6dKklwhzlLukiqaeBZlLkIuCUi22icqGgYCTMalmhTprsRiOdVkQ1X3eAUZeXudhIreL6DFfG+d5jskVZ+6zTyZEqdcbSo5qv+tsANt2P+s8+dlPfcZvq/z/Pu2PxxPsPFzXk+v/vi+P39c58PxAtKXR6Y8M9STPEJ+P1F8+wP9D2bynRNtpwAAARJ6VFh0cmRraXRQS0wyNiByZGtpdCAyMDIwLjA5LjEAAHice79v7T0GIOABYkYGCACx+YC4gZHNIQNIMzMjMzRADBZMGlMlhhaYAEMCkMHEBKO5GRgzmBiZEpiYM5iYWRJYWDOYWNkS2NgzmNg5Ejg4M5hYuBi4uBk4GRK4mRg4mBNEmNkYODmYmRjZWNnYOZhZWLlYmJm4xWHOZ+C59kPrwOTY3v0gjp6i2oGc/pN2IDZz1ML9uQ877UFsjufNuxzL2sDi8cdX2buYFYHFTRnUHHR2sYHFezUUHTRfrQObcyBjuv1dY+YDIPYUlrl77ynPA4sv2rFlf8HCr2D23vJCe14bSQcQW56bb/9WYTkwWwwAPAo7AfQkaEgAAAELelRYdE1PTDI2IHJka2l0IDIwMjAuMDkuMQAAeJydUzluxDAM7PUKfmAFHhJl1vFWQbZIkT+kz/8R6rCQBbINBQEeesTBaGgn6OvzfP/+gb34TAmIgQoA/rvNDL4YEZMfv3HW1s/CjTJh6QgwO4vwBq8k/u6lUq0es1dMWkyFMitq76WMrXFMpXcQzl5W1pCKOxApy4EVpZhKz0VWLiQU9OIqdNBMSFRr9EZ0sM1pYUUMp9u0rWlJjXnx3kJkExlZzMuYjPBMSBxGb1RrsalSWiwXHw6P3Qt/yHjVC0dlM47qZhzpZhy1zTg6NuPINmPQv2y+BIguxpH/9FeBA63iyY4NE6P4ALg/zvQLnPaoHnHat2kAAAC2elRYdFNNSUxFUzI2IHJka2l0IDIwMjAuMDkuMQAAeJwljssNxEAIQ1vZYyJNEOY7aI9byrSR4pdJ4IKejO2FtWTpOlaPLpy/n3zu4xKKhNm4QGAzGd9GXp5zMGlp5EYgCfYYIM5ENWFiBnOotU6iF+MLUrU+mMoC1mR7aUYTKDwfgIkdpxEu+wdT5hwdy85sr3dGxKwtE3XLp4IBWwcqzOoOO0VFdUgXNXlbudvsHCGLKh/n/QfYdzNhi71bWQAAARZ6VFh0cmRraXRQS0wyNyByZGtpdCAyMDIwLjA5LjEAAHice79v7T0GIOABYkYGCACxeYG4gZGDQQNIMzOxOYBpFgSdAaKZGfEyIGrZITQzOs2gA6IZGCUYdUAWa3EzMGowMTIlMDFnMDGzJLCwZjCxsiWwsWcwsXMkcHBmMHFyJXBxM3AxJrAzJYgwsTFycXKwM7Exs7CysTOJw9zOwCOYcnj/92rhAyCO/s0J+wWro/eD2Nrhi+3eTfu8D8R+8nO9fajHUbB4/hUTh46CKWD2A0k/hwr1yXYg9uV2LodynbX2IHbkQTP70JUVYPYm3vX7FkUzOYDY6UVP95ffvwwWX/KP+0C7RihYr+K1jAP3phqC7RIDAKtHP78sxexDAAABFHpUWHRNT0wyNyByZGtpdCAyMDIwLjA5LjEAAHicnZNLasQwDIb3PoUuUKOHZVvrpqvSGZhF79B970/l2PEEpoWOjSC/+OMPPZIA7dy2969vmIe3EIAYSADw1zAz+GREDP76C0XlrE1xFEypMTC6i3CFvxDnGBQSyk1hrFrkRHn9N8VvCBl2Sipa1ygUpaQ6eku0WAvHyqqjN7WyRpHIjKMCriWt1kI56ehN2VanW0jGXSuLc/HNSCLrVaHk844uz3wvxTj3jrKqrFF8LnOmjJXXKBKzVOq9UZHzjm6PFHrUYVe8R0v8Id0Mu0rTcaXTcZWn46pMx1WdjiubjgHh4bgiOhxX/tPfE5hJAxzlfAC8XbbwA+PzpqamD9HjAAAA73pUWHRTTUlMRVMyNyByZGtpdCAyMDIwLjA5LjEAAHicJY67bsMwDEV/pWMb0IT4kqgYHbr3tRcdHCdbbAdBMxTIx5dENUlXl4fn43mmmec8vK4rfe329P1wfxwIjat2GBilqCqMEZFQFRgKujXRBmNBoV5YM9NmrRKMhNLUCRKhxNFidLaoJ8F6dRgFmYsIFGRvSlmhqqYQw8adk9xI3D0qPW6SArFNYx0BY5Hq/06ts1nMVQvhSIIUSE50caqBGgSreGkpSeHNAk8w/WzL53W77InweFuW39fpcDrjLt/Ldn6J77fp8n5bDqcr0v0PYppEDF+0zgsAAAFKelRYdHJka2l0UEtMMjggcmRraXQgMjAyMC4wOS4xAAB4nFWQv0rEQBDGZzaT5JLb3CYaTitJIXIcV4jYCHKbhxDt5F5CsLQSmysEr7Kxus7KVkyws/ERRCy0FGxsnU1u79aF4ffN8M0f9vvp/g34SQ6E9uUcfY4LzKAw9QAGDI8sbbnYZoj/KVJQ7Bvisg4TYxNtXSDByIwB3MSRWTlcNbZc9hnYbAGEtteyy1ej6Ajh8SFAPvEYCEIIO4RhBFEMcZdELEEmkPQIEwUqBZVBgqAIJJvXSMh1ErnnoyCV+GEg48hXiQxow34J5I9XB+X050Ob5H3+ySxdXRm98DR6d68evx7dNvrh+Vhf/w5qo1+ml5X19262aus5Gx/W1nOOJ8xy7ni04ymdOdrZpZ1djef0645rkx2js3xYzmaRdnS1mpM6t6VNb/8PQ49RpBi8lt0AAAFIelRYdE1PTDI4IHJka2l0IDIwMjAuMDkuMQAAeJytVctqxDAMvPsr9AMxkm35cW4WCqU97KH/UOix/09le1dxSAvduMaEGWQNM34QA3Vc15ePL9DhVmPAITgHgD/OUgq8O0Q0bb3NpWBFZFPyDaGVKsLT528S4zS9N6O79SbGnQo8rrLMqGiiZSYRWp84dBVHns95QZt9LlXF2RiQTnkRB8jkJvdFHISU8mQiUSmB/Gwibz0X7ola71wi+JdEcEh0ParQEe/PaOoF6K2bSbTdumOih9604zT2nnyNkdMtB/sUznlRlWVCZXhHTrLtEj3//b5sJ31eRbbV9ftjGvLtW4mgoBUhrERQbIQ6SVoRlJUIKrosA+G9IohICQF1B7ETrxUPpA4EEd/VKolaiUBpJHno2cLVBh6kd66pqANf/1mdvAJc3lbzDZoDEJ9PK0sdAAABEnpUWHRTTUlMRVMyOCByZGtpdCAyMDIwLjA5LjEAAHicZU/LbsMgEPyVHu0IViwLLKSqlMoXH9qqd4uDo/hmy1HUHCrl4wtx/KhyAWZmZ2Zp6thUh0NEna86Fs1uj7GsEkqg6sv0iFg0dSynucxV+XirZlnfuUnpX26FRFAWtdCgrXidkQLOSINh5iAQNJKlOxMMEqZxZxRiYgjIBuOEdOAckukkuo1VPnvlan7UyUffDKdlFBBb8rTJUODJB+U3GQheYeA5Q4MPPkMEZlK0DvzXF3lpeS7ZdGjLyglOJrRu+mJSLZuca4kNhZWRC1WK9mccvi/jeU9wug7D70d77HrYJTiM/XsSP9vz13U4dhfA2x8OAGwmgZmAawAAARt6VFh0cmRraXRQS0wyOSByZGtpdCAyMDIwLjA5LjEAAHice79v7T0GIOABYkYGCOADYn4gbmDkYNAA0sxMbBCahR1CMzPogGgGRglGHZAmLZg8XL2CCpBmYoGrY4apSwCJA83LAJvDiMSAyoB1MiJ0MkF0cjMwajAxMikwMQONVWBh1WBiYWNgY2dhYuNg4OBk4OTiYOLiZuDmYeDhZWHkYWTgYWMQYWJlBKriYePg5OLmYROHeY+Bj+3Qwf0Tv2s4gDiWuW37z4XMtgexuT2+739eOH0piM26JPrAhYdTliGp2Q9iQ/UeALHzthywY2BwAIvPvJVgF1/JDxb/W3wNaN4BsHh+rJ0DTA2UbY+kxh7JHHskc8BuEwMA32M+lQscp1AAAAEQelRYdE1PTDI5IHJka2l0IDIwMjAuMDkuMQAAeJylkUtuwzAMRPc6BS9ggUP9onXdVdEUyKJ36L73R2nJYRWkAZxK0GIGNJ85oqPtXNa3r2+yI6tzhEhIRPznrbXSpzCz088X+IRQWqfPkbEp9lpl+qBHiPHuFE4sWy+8IKSB8vIMpQbUTe29v5TzcUrw8RHlck/Bvb5NtMwl2l93mXhd7Qglx56opJtEx2fRDkHNfRaJOB1/lzBQ4HNJcc/G/51FfC0xzSYyCs1QLBHNJLIdTc1iO6JndyRGQTPSjbTtoRtVcTTJjKpsPWqKVVSdmsndVKtUAl+NKuAKUAWxihBsAlXoE6AZkqFiP30nej2v7gfrBsAG0gVyNwAAAPl6VFh0U01JTEVTMjkgcmRraXQgMjAyMC4wOS4xAAB4nG2QwWrDMAxAf2XHZNjCkuwoSQl0+Lq1u5ccUppbTUpZD4V+fJWElK7bUc9PT+BtE3GT7d5rbPOYNdt8F9ctjYAVxCZGBesWSQm1b7fMIgRkYUNQeIdoVkpccIQGgZADT6RiLCtjVSLB0FssFDP4GesGsysVh6d9+xyYj9jHFQcsBYsY60DCOBNWofSjQh6lNCuEQoKfOioQVOJ9WPxlnKdFncxH+qW8hHPT/Qzp+zycaobDJaXrZ7fvj6B/BGk4fujjV3faXNK+PwPW8tuRvw7X+BLCf0p0uwMVmmXXLHGqdwAAAPx6VFh0cmRraXRQS0wzMCByZGtpdCAyMDIwLjA5LjEAAHice79v7T0GIOABYkYGCOAFYn4gbmBkc8gA0szMWBkaIAYLgoZJMCQAGUxMmBKEDeNmYMxgYmRKYGLOYGJmSWBhzWBiYWNgY2dg58hg4uBM4OTKYOLiTuDmyWBiZUjgYU/gYVYQYWZjYGVhZmJkZWNh5mFn4+Dk4uZhF4d5hoH35dnsA7ySSvtAnCZlvwPG4YvsQWyZygf7K51O2UOUOexf5/vODsQ6FzJ7/yXesv0gdvRE7QMGnxbth6hZsHT3w9f7oWrskdQ4wNQA7XJAsssByS57JLvsYXaJAQDt4kHW4R7/FQAAAPZ6VFh0TU9MMzAgcmRraXQgMjAyMC4wOS4xAAB4nK2UO47DMAxEe51iLhCBpP51nCrYLbbIHdLn/ggl20SKTSNIIOChRnwQbcIOff1t9+cLtmRzDhzACaB/o7WGhxCR0+OX4HONsSvynGroDPLqEq74hviMgyJCrdeylzxLYV9SKDslVZI5CilFK0ZtzCnO3kU4pJ3XMvMcRfTtUjl4JdBsR6NiUGqUOkXBko6wpCMsmTosmTosmTqsmDr9JDKiJ/oIY6snqqI5qpI5qrI5mhRzVFVLVDU71sB0OqqYT0cViznS/yNnTR7bhxM+0LzfbSQ/wO13c29aFrUI7SDArgAAAJt6VFh0U01JTEVTMzAgcmRraXQgMjAyMC4wOS4xAAB4nG3OSw7DMAgE0Kt0mUg24m+sLHsU1Fvk8MXZtckOPQY0SZnJuSXt71xzJnV+nVsX8FDV1hHIgudsRxkzTm4E7EUlBMNkFFggWQEWNAR1U/ErwCQm6810Io0yrtdYsbUcgnzRGGb+6eSLQzmkHQ/H99uHoree/zV/W+7nFx2NNpb4+vQSAAABRHpUWHRyZGtpdFBLTDMxIHJka2l0IDIwMjAuMDkuMQAAeJx7v2/tPQYg4AFiRgYI4AdiASBuYGRjSACJM7M5aABpZhY2hwwQzcyIEIDQDDogmoFRgkUHZIoWhjgzDnEmmDiGwWgCcB2sEB3cQOcyMmUwMTEnMLNkMLGwMrCwJbCxM7BxZDBxcDJwcCVwcWcwcfMk8PBmMPHyMfAyJnAzJ4gwsTHy8nAzM7GxsHFwcTOLw/zMwN/BUeXgwHBoP4jzc7Gmg42CEZgdx7TCfv619WC2hEa67a8zvGB2CM+S/f/WzAWzT3es239xo94BEHtnltKBgs+X9oHYzbwVB2Kbu8FqanVkDxz/2WUPDu2JuQdevfwMZqcJ9+/3PPAQzN67ltfOd0EhmO3ze4/9imnnwWzTR3oO3L6+YHaDU5MDe+5eMFsMAJ74TZi7GuYnAAABQ3pUWHRNT0wzMSByZGtpdCAyMDIwLjA5LjEAAHicnVQ7bkQhDOzfKXyBRf5gwHU2VZQUW+QO6XN/xXweYrWJlAVRjN/g0WCbd0Bdt+vb1zfMxdfjAFKgBIC/bjODT0bEo56XYJiKgwsFJU31GwZnEV7gL4l1NxUOSbHmXjAk07KnQkGIU/ciWfKeimdoit2LqtiWijvgQsMLC+3dqN4jiVbEIbOt1b09qtCC46LCQSWXfqNIjHteJJSso9NYzP7vRe68RO39rSq61yP3ksc9yOdPn6gL31WXKA8vOe3PC0mJHVncnBd3EHObOkeJN+fF31EWHtUouNlpiAFze43N1TOd1qlCrdTUA24zwD2QNpyDiS1nMI7SZDzIM3BU1sBmjgHhZDygM3BEfB5zRDIZAYqTifVvdzJxcV2zT6PvAK8f1+MHiTPNW8woEnMAAAEhelRYdFNNSUxFUzMxIHJka2l0IDIwMjAuMDkuMQAAeJxlkL1uAkEMhF8lJUSLtf5f31VR2iRKH1HAhY4TCIUiEg8f7ykNovLszGfL3tcJp4mm1dfzINv1Uvm/UtaMJkyt26fbiiGquZcNgqIal5HAtKbYVLDQVqWMCIykC8TOnk6FqiYs3jFVjhplzJga9tYUjE0Wi41Vy4bAKSwdAmV3642CVLFTDM21UW+sLUIXTFRzRHfUdYEcMVo6UTNahiO6d8eNrfW1kBtnViGEm1LfXdyEkjFCj36fM6kl0ipGXjUKVK/WEfFoUdZl93OaPy+n86DwfZ3n37fd/nCE/DKYT8eXDN9354/rvD9cQAa/Z/yR4SHumXhkaEC5h/L9QOntD7mUeH4t2v62AAAAwnpUWHRyZGtpdFBLTDMyIHJka2l0IDIwMjAuMDkuMQAAeJxljj0OwjAMhf2TuuFnRNAxY4S6Midi4SocAzbOwd4rkK5cgpmNK5AoDY2EJeu9fHmy/XkML4i1jo2Qazn1BQ9gEhewUVgJnJMyttnQ7wf6pEAd9mnKvs2cJ44dZ/6XV5mv4nIkS8SGlVGNJSVGWhBt9MKSRrNBQS2KaVfOTDd6f22akJ+jm7139+e7+DD7MVSZsfLheDu5KuPLnIq7wrdfF1kjKyISjP0AAADYelRYdE1PTDMyIHJka2l0IDIwMjAuMDkuMQAAeJytkzsOwjAMhvecwhdoZLtNE8+UASE6dOAO7Nxf5AFWpD7UApGH/7etr3aiGkhnGq6PJ+jhwRggTAHLISJwZ0Q0qb+1UcU0NGgdSZ9yWHIXWEPUkSlk3RrltJ+C1rtCIRso+K8ozQZl3E/5y0bNxu1OcwpXmpY2SjfkpftxI2CLPrjtWajS7dIbzWc58NLHZqnvpVMK5QIVw3lKLqbNbe9KVE5NVL22ReO1ElWojaiR/C/xx+hHbwDncTAvzX+LFWTT0JQAAAC4elRYdFNNSUxFUzMyIHJka2l0IDIwMjAuMDkuMQAAeJxljLsKg0AQRX8lpSs6OO7L3WARrAKJpA9brMTORZFYBPz4DESIj2ZmztzDvVZYVnUVlc/YomN1RJs7Rl86hDvNEU/SDCQahTI5I8g1ZqCJEQosFGG6552ebrp+Ng1tRM4XO4dMF0It1f9wnbHEv/vwGPvBSnhNIXxuvmk7iAlD310ovPuhnkLTjoBWbx19dLg1W8ccHTF/AVeATkAtHfeVAAABQHpUWHRyZGtpdFBLTDMzIHJka2l0IDIwMjAuMDkuMQAAeJx7v2/tPQYg4AFiRgYI4AdiASBuYGRzyADSzMxYGRogBgsbA5SG8QlrYdAB0QyMEow6ICu14AqgEkwSTGgScAY3A2MGEyNTAhNzBhMziwILqwIrWwYTG3sCO0cGEwdnAicXAyd3BhMLjwYTM28CL18GEx9DAjdrgggTGwMfLzMTIxsbOwcnN6s4zK8M/FcDahyaDVfuB3HC5VQcfjJ/BbNtE9bZy2g1gdnHXhyxv8PxyQ7EDjJcYycSs94exF4e0brfStgdzO6ZxX+gKvkamM11Nv0A88QuMNtfsPiAfe6FfSC2mLLaAd2vy8BmRqcbHjhwwegAiG3wcPb+HFczsPgeTWZ7YxZzBxD74TIrh7UhHWBzek81O7Rd0wa7QQwA5vhJyHqn2a0AAAE9elRYdE1PTDMzIHJka2l0IDIwMjAuMDkuMQAAeJydVEtuxSAM3OcUvsBD+ION101XVbt4i96h+95fNYHHi5S2CxBShgyeDHbMBm3c97evb5iD9m0DLIAKkH+d7g6flHPe2n5OLiQBbpiYsLZ3OQWb4QX+kjjPQ4VSURkqjkJrKuFAibtKRvVVlcJVeqwY2ZpKTszKw5VF7IpKO4c8cmpmuqZCiYRr96Ima3m5cVJC7yq50qIXTubWVG6R3Wy4eqLixcdfl0VWVcyOEzXkcq7R/aqCVzxqRGi1n8iQ1iodEQWPPopeqMX1fy90xaOPHFVGjbSsZRckIdbSY1FtqafxMEZ9EQ/uSdsOJJMJVCYTSOcikM1tgepkAvlkHDA/mECI58XTQVxofPooytwm7bYbDHYvg6GnnXeA1499+wEi0sz6aGr2nQAAARR6VFh0U01JTEVTMzMgcmRraXQgMjAyMC4wOS4xAAB4nGWPMU9DMQyE/wpjW6VRznbsuE8MiBUQO2JoQ7c+tarogNQfj/NG2M7nz/a5o/e+el516kN8bHb4XHdaP4aiUB0P9xVnFxJOW2QmNEoT5arCdTgOQUtTtJQYwylQx3Aqt2BKFiMjSVPJzMqQFLClaaACsyDMTBeLchxqCEJNxpYtZ42bHk5pVOvimJtHnNhcrDRe5qrXoEbEIqyLZYaBhXB2Xw4SLLbHpAHuNjJVEFdNlFt15fGbQ2WELFpLvCYZaNIiJjSmkdZp/32e36/nyw4lf93m+edlfzie8mbU8/n0FO3X/eXtNh+O14wd6A9F/ym6/wL58F15x1YujwAAATF6VFh0cmRraXRQS0wzNCByZGtpdCAyMDIwLjA5LjEAAHice79v7T0GIOABYkYGCOCD4gZGNoYEkDgzO4MCkGaGcSE0ExOM5gBLM7EpbABLM7I5aICUs7A5ZIBokAAeBkQtgw6IZmCUYNQBuUOLG+gcRiagaSA5FlYGVjYGNnYGdo4MJg7OBE6uDCYu7gRungwmHl4GHvYEEUY2dh5uLk4OcZgnGPhyzfcd2GXfYg/izJww48C8GZdsQezFrfMP5J3fsB/ELn8ZfmCxhAZYzYXZPAcq1Hv2gdiBE3z3L0/utwOxp/nst+upUgOrn+P0yP7p8qN7QeyjYv/sK+dtBOuVc45wcGz8AWYnn5joIJjdCGZHXO5ymGn+CWzm1zwbh3UFzWBzHrboOVzeonAAxBYDABFRSFMUR1egAAABOHpUWHRNT0wzNCByZGtpdCAyMDIwLjA5LjEAAHicnZRNbsQgDIX3nIILDPIPYLysmq6qTqVZ9A7d9/6qDQmKNB21JWLxXhx/AWwI0Z/b9vr5FedDWwgRs48IPw5VjR8EAME+v5SkObNnYgJmcgXJohCf4yPEeXRKTlJFRi4CtRPl+h+KtoKuMHFDXpsLJxauI7eWWtcolFAFXEEiYVmjQGrAMHKpAZ4o73+mWMa+oouvCH7ZF35AwaNGRgGtZWlFRtlrFL1Gsra70WukO0Wz9+oKJadSiuy9i4irFObGY1+y8CKFjnNkvQtUdZUiI9f6ryDCiXK7p+C9Dl3R8KErPps8jakyjak6jSmZxlSbxpR2jBuNCEfEFOIRMYVzBqb8IO8R7vcSHmai3+wee9q68T9G8jcv1y18A1acwWk0NvACAAABA3pUWHRTTUlMRVMzNCByZGtpdCAyMDIwLjA5LjEAAHicLY89bwMhDIb/Ssck4iwbg4G7qWLp0KTdo1S6fGxBF0XNUCk/vjYKE35s3sfU3aqua/3a14/DiU52aL8Z6fD2XA0RSgg+OQJkpuKmIUCSJNEhEPrMnZQcUdxAwJnYEAMnFq9DEkW8KPJAJSG5AcEn7VoWQkZfNERZRozJTdhfcp+TiIzKqCvFEBaJRGLMpLYYZ1tnMmUhrUugENwUIMaYDCARZgPMqtKQkNiAf/1N90YvuZOkFxV5UAsGt3Y/NIqbf5f2fV9uIzGcH639fc7HyxU2Vrfl+q7t7XzbPdrxcgd6/gP8TUyD4PLCeAAAAT96VFh0cmRraXRQS0wzNSByZGtpdCAyMDIwLjA5LjEAAHice79v7T0GIOABYkYGCOAHYgEgbmBkY0gAiTOzOWgAaWYWNocMEM3MCBNg0AHRDIwSrDog3VoYCnDrYILpQBNnxiHOgirODXQuI1MGExNzAlARM2sGEytbAht7BhM7RwIHJwMHVwYTFzcDF08CDy8DD18GEx9jAh9bgggTGyMfGyszExs7BxcPH5s4zM8M/Mw3F++3/2B6AMR5J7N9f++NNftB7Epx4wMN7DZgdt5OmwOJHE72ILaFYteBfzfWg9lHltzdH2J+Ecxm8d61ry2pCcxeVxZlv3LqJzB7rRqfw50Di8HsnbwxDv9/cjqA2ItPiDsss4jaB2IvDcx3OGIeArZL7/Bke4uqbjBbkn25vYij6gGomfuAZoLViwEAnQNM8wcFy6cAAAFBelRYdE1PTDM1IHJka2l0IDIwMjAuMDkuMQAAeJydk0tOxTAMReddhTdA5F8+HlNGCAZvwB6Ys3/hJH15qQBRJcrAV05Orx13g7pu++vnF4zF+7YBRaAEgL9uM4MPRsTNjz9R4IJUIw5FuVQGBs8iPMNfiHkfFBVMPZKIukbhkK1IjTBk0bhKMS1HHTlmW6NoEEq1IvCKssx9uf2k0BTHU1+y0EFJyxVhkCTWKUglLVH8RominVIs8hqFAzNRp3A2WaOIz0t7aedRjPMb/dNdPnmR3LrrHWISWvWSxbRTChte9yIThbwO1f7mWCyvefFZQ7U+xbEwXfeip3nxbhz/kZ5n97IXaq2mLrjVyl1I+9iR0TbvD5HGMY/yyHhURsYjGxkDwlnQ/ZhHNBxUIUMIkM4ijjtxcl3F3cEbwMv7vn0DRhDNUzmzzV8AAAElelRYdFNNSUxFUzM1IHJka2l0IDIwMjAuMDkuMQAAeJxlkLFOQzEMRX+FsUVpFPvaif06IVZA7IihfXTr06uqdkDqx+MUltLJzslxdJ3nkcZx8fE46OdyHPm35+h7xV+VqMQPl8WKMlspNa04m3DztA4kKNpSNNCCGohzcwOlVckNIuZX5mLGKZA2lyCSQVVrirkGo+tTDaQepEI0QMmoMJUghawirUs2RfhBzJXiZc7MRBQg8oDTGhHIIIkzqZp1A40q9zhMoNK60+CRNZCxeYvUFLrA+x7FvLZOUMT7rmpM1zgxD0Efk4ZYMi3T5jRP78f5MEj+Ok/T98tmu9vn+LQ8zfunuHzdHN7O03Z3zDr4reP3Dg9Et1Kc7ywMhH8W7i25/ABc4niX1sGhkwAAAZ56VFh0cmRraXRQS0wzNiByZGtpdCAyMDIwLjA5LjEAAHice79v7T0GIOABYkYGCBAFYnEgbmBkc8gA0szMQIYGiMGCTjPogGgGRgl2HZBuLQxxLhzinBBxDgawOBNB81lwmMOMQ5wJhzgjqr3cDIwZTIxMCUzMGUxAVcysCaxsDKzsGUzsHAxMnAqcXApc3BlM3DwJPLwMPHwZTHz8DHwCCQKCDAJCGUxCwgzcIgrsDAlCXAkijAoizGwM7KzMTIxsnFzcIoxMbDx8AkJc3OKwYGUQZXqrcSD1yiN7EOdOyob9pbdLweyMpTv3R2zi3A9i9+kaHUjfOQfMvnzW7ICag94BEFuwpPHAO6/L+0DsOsU1B75v6gGrueNed0D7SxfYHIvG5Qeu3foEZv9udNx3ff8asJqS1kn2Fvs1wWyGY7Ptp5wLBau5Zavs0Hr8EJgtf0/dwczd0QHEzvtR7LD5oClYfMnKJQ6iBzeB2RoR+Q6SZzzB5sRxLnDYceYMmO33V9LB/dgeMJszVtRhupsd2M1ST033fj17CaxXDABPnG2TP2XOSwAAAaB6VFh0TU9MMzYgcmRraXQgMjAyMC4wOS4xAAB4nJ2VS27EIAyG9zkFFyjyk8e601XVVppF79B97686QAhRW4kBZWEL/Om3jcnm9nW/vX59u77otm2O0BE7B39+OWf3SQCw2fEn8oGz7pHoY7RAW+BtF9yz+w8xfoWCnmNqsTmArFJES+wTeOWY1yjkYxKoPCJZ1EI+aeDGI4oD5f6bgoMdB4p4EG0ZCV4oD2hRq27GmpG1j6a14EhhnyNj7TRYcqtaiKXdl2TA+brkgQIeU249Ysmjlo9pLaYARWLlhcBrd9cohBFqbGKlNQp5VYiVpwZcpQSicu/Zg83ofHVloLBPSC024oXygBa16mrLiHPQeS180RI5p9qjmBKvagHANgEBHqkLXaorEBpFoqTVHjFzqi/DMVFzWvCkWDVA6ttkGUkY37rpCcCSHlWHSt2xOlwuRNsxS/uOOaE7obxWp5N6TCrz2mkIh2PW3ojmoMOuwCzsCsxCGR3tx9RhV7A7XYFZmEYn95js6FRA+//t0FYD8Dh2aqNaneK8Offyftt+AOyFHHlECrU6AAABdXpUWHRTTUlMRVMzNiByZGtpdCAyMDIwLjA5LjEAAHicbZE7TxxBEIT/isMDLaN+TT/WkXMbnFsEx5qM1SEEgSV+vGsWHHjvop6p/rq6ZnfhRe4WXQ53iyyHX9dz3F9tlQkHxqH+CZ9VPquiKqrdf3k/3EhzLYuJW4QwT19vuGkk5UStnDShUCMzoyhQbl6guAl7DSi1c+pQ2OAD2F0pbJvjzAqZhqeVb+bWSX1gHWti2EuLNNJBiZjxJmV35Wn0RDwgGUJ0kzFpLJ4D64haVGOSqmRI2iqUDUkpe88NErUuUJAFQaUZ+ceQheUQVPFO7EKfkFJbaMUWMjIl4NsbEcMWQxuQLGR4fWBvxuizdYB4aDmWwbR38g6hCw8HfGiR0kkbcVVMV9Px9bT+fDk9z9l+v63rn+/Hh8endo3renr6huaP4/Pt2/rw+NICv/V/CPczimlm2WFyjtXMtqPsgtnMvqP8nJKZd+n5QnydZRdfLsS397/Fe7o0Niwi+wAAARZ6VFh0cmRraXRQS0wzNyByZGtpdCAyMDIwLjA5LjEAAHice79v7T0GIOABYkYGCACxeYG4gZHNIQNIMzMjMzRADBZ0mkEHRDMwSrDogIzRQmhgSAAymJggNDMjXCETVCG6ODNEnJuBMYOJkSmBiTmDiZklgYWVgYUNyGJnYOdg4OBk4OBi4OJmAGrmYmIQARrOxsLMxMjKzszExSEO8wcDj1m9kMMd3Vv7QZzfL/3sj1/9AWYff8i37wmnB5j9N/+k3dzncvYgdt7T9/bF5jFg9mU+KQfbUwIOIHbQbBOHgiuz9oHYi+vc9v88dRys5sU3xgPGKbZgtsfm6ANBrxaB2SwLL+5PMTIFmy/xR+VAk8kHMFsMAKGVP9dBKjjUAAABEXpUWHRNT0wzNyByZGtpdCAyMDIwLjA5LjEAAHicnZM9bgQhDIV7TuELLPIPYLvOpIqSYovcIX3urzDAIFZJpBWI4j0efLI9mgDnuh9vX98wFx8hADGQAOCf293hkxExtPuRzesx3CgqpnyeYawpwgv8h1h36C8MTTrFU8Ityg0jieeu1Cjt1iIu421h1D0KRdPEo7eCvEfhmFB5zLmwLJT7bwotOj1QjBn7XAR5by7nTDWn3lsu6HsUjijXTFXc9igSk7D1WljZnp8LLxSKRVRHb6ibtXDMqt55pqU8X4tMCrXCuBtuCXUj7VOOpKo8k6rKanS9ZjOpyqdxIFwNXaaq+tNfiTY1k1nOO8DrxxF+AJrxpqQJQc++AAAA/3pUWHRTTUlMRVMzNyByZGtpdCAyMDIwLjA5LjEAAHicZY69bsMwDIRfpaMdyAR/JJFypsJrW3QvMjhutggOgmQokIcvBU9BphNP3x1uoWXhpZum7mc38qGf2FUO/dLu6Epvj46BrSCFgUAxJg57BEPjZM0qMaJbAwJJSSLBX2oUPeCcFInuIWRG5Y1TTaKBIGU0c4cBBdUZlWKeGgSisLEjrKwNIciiunWjbqGkaqUtMM057JtGKl5jGZm9hyGiUgq+P7PEZhhTsdYiyG12H+bbWr+v62XM8Huv9e9jPp7OsPOzrud3//ycL1/3ejxdgUd7ZuyVkZHwGfL7hYqPfyOBXjUUhG0TAAAA3npUWHRyZGtpdFBLTDM4IHJka2l0IDIwMjAuMDkuMQAAeJx7v2/tPQYg4AFiRgYI4IbiBkZBBgWQOBuDBpBiZoHRDDogmoFRgk0HpEeLnSEDJM7EiKaASYIRpgAszgzXyAwRx1DPAhHnBrqFkUmDiYmZgYlFgYVVgZVNg4mVXYGdg4GdU4GTS4OJk1FBhJGNkZOdlYVJHOZwBu6HbmoH3MTn7gdxHrot2y8e+2UfjL3rRZM9hK12YNeLR/ZQPUthbKAaeyQ1DsjiCHPUHGDmAwNIFcF2UFvcrXcAxBIDAHyDNWcvGZ7wAAAA63pUWHRNT0wzOCByZGtpdCAyMDIwLjA5LjEAAHicrZLBDoIwDIbve4q+gEtbwNGzeDJy4OA7mHj0/WM3oYyAAsalh/7p+uVvNwfxdM3l/gQ73DgHRDEAF0NE4MaI6PT6gX0lNcWMPHMoIgO9VhFOj0+IPFzfK4IxQ1+GKQX2U4A8qpnfKDaRUkKYULo5JV/TMaP0HUuUdrMX+MtEsG8ink839TJ/ox0TffkvK9stMoptd+HXbfcyUtiHVS/5XkqjUCrQW3ByyW9RpGtkojJRpa/SC82C9aioraKZZIJwEJoRDT2Eo4MrwLlt3AsRq5hdAZgm6gAAANZ6VFh0U01JTEVTMzggcmRraXQgMjAyMC4wOS4xAAB4nG2PsQ6CMBCGX8URDL302tKWGgbDqsTdMGBko4EQGUx4eA+IQVK3v9//Xa8t2gLzIrofna7isohySkhpJrKKqaSgqsMUMQFpZrlNGIIQRpjkNKcs4yphHJQhJH8YAhcr+Q4ibA4itQ1DvdFgMJj7sy58lKDAVbrcvVENWqNUKxVgFhon9avzt6HrnYTn6P37Uj+aFujr4Lv2TOW17svRP5oBtNN7R4cOOrt3bOhIh3wv0Tmw1PQBqUJqgJ4a0TQAAAEWelRYdHJka2l0UEtMMzkgcmRraXQgMjAyMC4wOS4xAAB4nHXQUU7DMAwAUMfNsiwrXSlh45MfBkK7Q/O1c+Qznz0ClwH2wxVIToLYCbgCiUKoBYol1+6Ta6n+en/7gBhtTAY5tjF3MZ+YMA+xNvxvhUOqwG6Wh/TR4z8XFVcVX1VcVpxVHCveVJxXfJF9DeyWoUNsALnlC+DCoViCkFauQCqHah1v57C9gLaz3Qa63mF/Cf1ghysYtEN9DRqsYlajQC6kYqLt+kHDrpw73dt4gM/n/GrGmHe5D+P5+OKLn4/78NOb2Scz+2TiY5/7U+rvyfxI5g3ZP5L9xT1xP7sJZE+YfQrwG6fUv5J5T+YD2V/+yxfffgPZ2lDcIz8rXAAAATF6VFh0TU9MMzkgcmRraXQgMjAyMC4wOS4xAAB4nK2UTW4DIQyF95zCFwDZJmTidaerKl1k0Tt03/urBjIeqpBWhUGzeG+Enz7Mj4M8buvb5xfY4NU5YAaOANj9RAQ+GBGdTvcYloRYlf7LCu7qBZ5FtJ+rFfcUmEmhkGqtp8AisyyeQ5ILNSm3xxRq9NKkxG0dUyxxmOXcS5nqbgr8LOV3Fvq5oq32kL7Af/sivfMyxYLDLJc9xR/C4sdZqEk5ZI/8+B5xN2Xi7Ho7u4+v1B8ssdeXmTvtx+/0qXdepljG37pkKVQ2jKrh0jGuJhZkMpPMpPJA7WaxmqVcCzIjZqQ8JLuhrUYV7QRqjEAVGUE2RqCKzq0xAlVkBNkYgSrG1hiBKjaC/JtsWtOdK8Dr++q+ASydJnVGW0JWAAABJ3pUWHRTTUlMRVMzOSByZGtpdCAyMDIwLjA5LjEAAHicbZC9bsMwDIRfpaNTmISofzFT97boXmRwXG8RHATJUCAPX9lRA0v2dBLvSH1iTw30sm++X9kddrParCTyISSVSf2/kVVmVVl1UkpqDi/3BgQ604JC743zA5Bt93OJ0Fqj1KNCmDKEMgShsw8STfDCt3u1sFRlTVeNWgsfHpMMymm2c1bZVDFTe9n9bJ4eLd98WlB4UJmLiaAqa7qKdJg5oACB5Ueg+gmUK4ByB7u2u47x6zKeWeHPLcbf9+44nDAtHeN4ekvmR3f+vMXjcEHHpsyYdcayKzNunSHBoQyFdSgwURlK91XKM1XctAFOTBU5baBLpoqdNuAVUwVPG/SaZUUvN+jN/Q98PtoVbNpAfgAAAMN6VFh0cmRraXRQS0w0MCByZGtpdCAyMDIwLjA5LjEAAHicZY49DsIwDIX9k7rhZ0FC0DFjhHqHRCxchWPAxjnYewXSlUsws3EFEqWhkbBkvZcvT7Y/j+EFsdaxEXItp77gBkziAjYKK4FzUsY2G/r9QJ8UqMM+TTm0mfPEsePM//Iq81VcjmSJ2LAyqrGkxEgLoo1eWNJotiioRTHty5npRu+vTRPyc3Sz9+7+fBcfZj+GKjNWPhxvJ1dlfJlTcVf47gvv6iMHs3LU+wAAANl6VFh0TU9MNDAgcmRraXQgMjAyMC4wOS4xAAB4nK2TOw7CMAyG95zCF2hku00Tz5QJ0aEDd0Bi5P4iD7Ai9aEWiDz8v219tRPVQDrTcLk/QQ8PxgBhClgOEYEbI6JJ/a2NKqahQetI+pTDkjs91hB1ZApZt0qB3RS03hUK2UDBf0VpNijjfspfNmo2bneaU7jStLRRuiEv3Y8bAVv0wW3PQpVul95oPsuBlz42S30vnVIoF6gYzlNyMW1ue1eicmqi6rUtGq+VqEJtRI3kf4k/Rj96BTiPg3kBqaaLW2SKgv8AAAC4elRYdFNNSUxFUzQwIHJka2l0IDIwMjAuMDkuMQAAeJxljD0LgzAQhv9Kxyjx8ExiTIpDydpK9+Kg1C1BER0K/vgeVKgfy9099z68zjssXeVY+Yot1lHFaIs6oi8dsr4sTPAkBYUmR8WvCGqLKWhihAKLnDA58kFPdl0/m4Y2MhOrnUGqC5mv1f9wm0W8mfrwHPvBKnjPIXzuTdt5iAlD728UPpqhmkPbjYBW7x19doQ1e8ecHbl8Ac7mTqbUIy9eAAABUHpUWHRyZGtpdFBLTDQxIHJka2l0IDIwMjAuMDkuMQAAeJx7v2/tPQYg4AFiRgYIEABiQSBuYGRjSACJM7M5aABpZhYYzaADohkYJVh1QJq02BwyQOLMjHCFhDSywDSiiTPjEGfCYRE30NWMTBlMTMwMTCwJLKwZTKxsDKzsCewcGUwcnAwcXAlc3AxcPBlMPLwMPHwJfPwZTPyMCfzsCSJMbIz87KwsTGwcXDx8/OziMN8zCPR1cxzYZKh9AMQx/fxk/7v22ftB7H+RFgemM9rvA7G11LoO3FGXA4tzeygdcP69yB7Ezlzds/+/3i0wm43fcb9Vq5cDiN296NXudj5dsDjXr232/3f1gtkfZB/bW3urgNVkxCo6OJgstAWx9/vXOvzvZQKrKfXmdjA6sQFs103tQIfv5lJgt9Wk2Nq//f0cLK5ktWVf6goPMFsMADaeT2SER4pMAAABS3pUWHRNT0w0MSByZGtpdCAyMDIwLjA5LjEAAHicnVMxbgQhEOv3FXwgaDwDzFBnU0VJcUX+kD7/V2ZhF3FKopxAFLYAyxizhWPc9tfPrzAG79sWUAI0BPp11lrDBxPR5tufOEIkd1Q01UODoq9SeA5/ScyzqSCqmXbEyFhT4WhGzQtFWMlrKikKlLtKgdqkcvupggnnOy9ZcjnO+o1UZDUXj5u6ilIqayoUNaeWqUSUlFZViFoufrZooSUVv0fimjsC6VouRxra7+E5F5378s8bpUnFzzIuB9r6t+BFYs12ppFB9LgXufMCbY31NxfD2g9wL1xMev8SVX3cC08q3hep1r0YeLkvkpnO/pnYigqaMXTCLTHuRNpTYpA8iKMytjnSseLEBnFUx7YaQGPFCS7iCDyT4cARhoODDAeOMBycCINcDt5CeHnft292OdoZBM/2vwAAATN6VFh0U01JTEVTNDEgcmRraXQgMjAyMC4wOS4xAAB4nG1Qu24CQQz8lZQQLZYf67V9VBFtEqWPUpALHScQCkUkPj7eEw0h1drjGc94NyONi/fHQT+W47jYLEee25ptf+X6ch8TP1wWKwYSUS5ZNKtBZb0iMPdmJQsmxUiIwR1rlBUCeVPuWAUho9axRmbRZp6KNi2pNJlZBBgenohh1c5BMK0kWgSoVfEZQsxV3SGXWUOUsiaoHEqpJDS2DpjV1KVJsx4rC870qUFzo+4nEGrR12TwsJlE1qr3a8SJqHO4ufSLK0beuc5EEk7aOU58jZS/gjifZ57KKMuy/T5Mb6fDcRD4Ok/Tz/P2c7eH/FeYDvunHL5sj6/n6XN3Ah3ilhP3nDoQ3ZKyv2PJQH/s6B8/vvwCzLR+aJdkXxYAAAFHelRYdHJka2l0UEtMNDIgcmRraXQgMjAyMC4wOS4xAAB4nHu/b+09BiDgAWJGBgjgh+IGRjaGBJA4M4RmYmJTMAHRjCwwCXYGDSDNDFQAplkYdEA0A5MEiw7INC2YPFScUYIZIo6mnlGCEVVcmUEBJIxhLBtEGTfQrYxMDEzMLExMLAwsrAqsbBpMrOwK7BwM7JwKnFwMnNwaTNw8DNy8Crx8Gky8LAoijGwsvNyc7KziMF8y8J+8PMFBY5eYPYgTM83JYZaCD5jNUsfuUP33yD4Q+1GZtoOz0+P9ILb1ZkV7g0QTsLh7zNR9l26vBYsvz+qyi36vfwDErql8vH+mxmSwuNhVnQNKNqJg8TlbRQ/EXSqzA7EvHis5IGT2DMzutNq6/7vmNrC9Ljp/9+eJmTiA2Ip1fbb7ymaDxZUN6+xNkyXB4mIAAiZDiMpI2NkAAAFIelRYdE1PTDQyIHJka2l0IDIwMjAuMDkuMQAAeJydlD1uBCEMhfs5BRdY5D8wbpNNFWWLLXKHtFHurwCeQaySrDYgivfk8aeHQbOFtq7n14+vMBadty1gajvAr9vMwjsBwNa+l5goWVMQUynFVa1CeA5/IebdKRxBQLxXTWWNQhFRsYoTRC6iq5Ss1ikYVVNao0DMjNmzoGaaKJeHKbWXDNWzcCq8moUUWu+JoorBRLn+pNCkZcrSpqH7XFBM105UEwAkV8xY7mfBSfMNhdn2mZIgLM3lxLGQZO8Vsfx4FryZi2Dq56iKmNayYDRV8/dXkG2iPH3+46Yh+zkwEkpeylI7zGjPwpb1/lzm95IHBXsB3VC/vd1wf1bVZDdpVFIH7KYqdbibMipV2WQQDlMV4mzoAFSFI0EzMoz0Xx0dZsR5C+Hlct6+AUVEy9/SuIPIAAABNnpUWHRTTUlMRVM0MiByZGtpdCAyMDIwLjA5LjEAAHicZZA/TwMxDMW/CuMdylnxn9jJVZWALAxQsaMbWtGtp1YVHZD64XGOqXRy8vzz80tq/axPr1NX+w3Wbv35OMrUbzqvPPW1VZz6de1ezn3F1tbp4doJJEo5hQgpZ7McVgxRorArVoyNwooA0aKFIQJnMdEmqRXkMCCYpaZEUEYVbBSaYvJBP1JBt3CMUyZrGFlkSWEgMMnFFy4ehtIolKJN8maMCRvFjH8KcyH0WCQY2a0GhkyiLan4GONiJdie45U8zqIU86iBICPnJUHUkkwbRChpyV4KFff0JUU9QB+238f543w8jQpfl3n+edvu9gfwT4P5eHj25vv2tLnMu/0ZZMy3TL5neMR4C/n9jsIR5R8l95RefwFPSXqs9Ifl4wAAASd6VFh0cmRraXRQS0w0MyByZGtpdCAyMDIwLjA5LjEAAHicjZAxTsQwEEXH442dbJwQgglbpooQ2jvYFRUN26/k0uIGdByFKh1XIDkJEidA4gTYZHczYhUJS6P/8zzzR/HX+9sHhKNCMZhOE2oT6oWlcBuUo7C/uvqrsI0KbLPaxuG7M84XOC5wNvF/75ULOesFni3wdOI5sJahR+QuXPLEYyIgkU6mIDOP2Rowb3PVqsJjUbryAsrKY3UJVe3qK6i1R30NBbQZc1o5zQUUKkcmeCIzhqKsaq2Km+NDQ/P9sO8AxiF+fN73QS31ZvLdGHqItzb63dPzOPf0p54DHwgfSA7xdoz+8dX0c35vSKaZczpLZu1xNuyyZNep58AN4YbkED/9S/MD8UlZAWf8flkAAAFDelRYdE1PTDQzIHJka2l0IDIwMjAuMDkuMQAAeJytVMtuxCAMvPMV8wOLsMkDzs2eqrbSHvoPvff/VQPFSxWqbUmiHGYAj8Y2xiB9t+354xP68WYMmMET4Lp/jBHv7Jwz6byzgmQZF7JzQXXtDb9JtL8psRxjVnF2nX+oPA2o4IgK2zkGSrG7jIZUfK1QVbntVajBU6PibYgrHc1othQX7mb0wIvveTnUI/Wyvy8PvHCvunuVkR5d/tsjalR0AvbV/fsE4JS7i1PmCKdUF+PVXRuVU24dhm8duZ6XQz0an8bYq+6RVwrjr1RQFcqjSYVwfjO4EJ8fs+8dQXNLFj225I6TkqAk5JyVpGaUGEFEuuNB6kAQeSWyM2nMBFIHgmhpyarHVpA6SEQdCGLXEqoxgvjuoORM1ei9OnJMvb0A19fNfAHDbiix48CDNQAAARB6VFh0U01JTEVTNDMgcmRraXQgMjAyMC4wOS4xAAB4nG2RzW7DIBCEX6VHp4IVC+Znyan3tum9ysGhvgU5ippDpTx8qUUqL/aFYTTfrgZxwKRTd0gmYeo+n6M/7mZFVS9U1BQN1fdVTVVdVP8NHJ/unQfnPeEo0QmJYMW+nJpI9UIq8EtfrQPXB+PniXmA5820BktBhbr73xmxNxDIIz5AC0gObQUfIc/YDmbKPskCuUz4NsnWyaaH5EUkf4BcvGAnhu8pf1ynS/Twdcv553U4jWcoXwJ5Or+U8G24vN/yabyCj8QZWjOoIiKnil9hFNE0lFlTIaJtKLum+ohNd9wobyI27XGjvo66aa832uP9F9b5vB/AXunAAAABkHpUWHRyZGtpdFBLTDQ0IHJka2l0IDIwMjAuMDkuMQAAeJx7v2/tPQYg4AFiRgYIEAZiESBuYGRjSACJM7MpmABpJkYWNgYNIIOZhQNCM3EwZIBoRkY2B4gEmwNYgBlJAEIz6IBoBkYJFh2QPVoY4sw4xJlg4hgGownAdbBCdHAzMrAwMjIxMDFrMDGxKDCyMrCyZTCxsSewc2QwcXAycHAlcHEzcPFkMPHwMvDwJfDxZzDxCyQICGYwCQoxCLIm8LMniDCxsQoK8LOzsXFw8fDxs4vDwohB2CvTyaHBVOoAiPOAz8ahYW3mfhDb6nWrQ/fOBXtBbNZ9TQ7LO7bag9gWfzY4JK20Bas5dvO0/Xyzhn0gdo3WRrvzN7rA4ts+1e+/ZXcDLO54hP/AvVsLwOLXMzkP9JsbgO2ye5Z6oFtZAiz+8tqsA2/vbAOzVzjkHPjCkg62K8hw4YFJ3ifAbJFKmQMLfe+B2XrrevZ3tnSC2UfZxe02+H8Bsz+r7Lef0TYfzOaN0ne4r8HqAGKLAQBWnmH58xpDawAAAXd6VFh0TU9MNDQgcmRraXQgMjAyMC4wOS4xAAB4nJ1VS07FMAzc9xS+AJF/Sew1jxUCJBbcgT33F079GvUJkB6pshhn0tH4k3aD8bxfnj+/YD582TYgB0aA35e7wwcj4jbOS0HpPcADF0WvYw9LsAiP8JfEee0qXFxMhwoWJ7Y1FS1kFVMFuy160YJWR0ZARcn4pPJ2v0otVdDSS4/CrKlQqc4tVbiirmWERbTtdaHITfuSyuiM+zUjZV7zElPC2jS9cG2LXriQdkrUq5y9vP9UoRPWk4qM6nJmVF1lzYsWE+uZkbK1+73IjZfmdM3DcbW6ddxGuU5OU7vfC99UV1U9Vbqs9ihmzZ1SBbsuz250uqaKo615GTfZ5VqXmL+1TsdXqot7IjT8R6frVOGdGCsDyYPbjjT7kEGdTOy1GQTq81ggm0wgn4wD4WQioCMIRHwO5Hgn0JhAmsF0EIjaPNaA+mQ6kE3Gxp/jYOzkerx9GH0BeHq9bN/e0gD+rwQ69gAAAVx6VFh0U01JTEVTNDQgcmRraXQgMjAyMC4wOS4xAAB4nGWQO0/EQAyE/wrlHdqs1q+1nQgJdA0Frx5dcQQ6okMICiR+PHZEA1c5O/t5Zya7x93l9X6z21zcb++3M8wzzpvH85H323XS78SYcTVDfMv+7HtDtZF2KwNWbi5YJqxOxlCGVh3QGpeJKxh7T6mpsZqn1ky0QGUwpDJJFWrqySg3671MUMVRLCWUxqBlapW4N9QyQOyzxuPp466uyTEiiYSIFbmTJIfSI2BKwNo8o6oQRYaB0gFWB3FGj/gDVyNTyk1G67hi3YGlhFNDhtyU7I2ZXzobrM8zczSDqtTXYBHRvSXTlKln+kjADqF4VLSsyE6EIURc9Px7Sm5aMOq1MN+Ww8dxeXg/vo1enz+X5evm8PTyWs/juBxfr+Ly9vB297k8vbxXHgH+QnE+oWgE+kfRKYUj2D/KTin5/gEu6ouSQkVzmQAAAaF6VFh0cmRraXRQS0w0NSByZGtpdCAyMDIwLjA5LjEAAHice79v7T0GIOABYkYGCBAFYnEgbmBkc8gA0szMQIYGiMHCoAOiGRgl2HVAqrVg4ug0XB0Hqjq4OCdEnIMBLM5E0BxGHOYw4RBnxiHOgmovNwNjBhMjEwMjcwIzSwYTC2sCKxsDK3sGEzsHAwunAieXAhd3BhM3TwIPLwMPXwYTHz8Dn0CCgCCDgFAGk5AwA7eIAjtDghBXggizgggzGwM7KwswsDi5uEWAfuDhExDi4haHBSuDaO2jpgM7F7Tbgziv/+kfKLt9F8zuKNI6sPyInwOIXde1f/+uCxlgcefFp/Z/6RLbD2JvELc+YN6wEMwWXuJwgH26wQEQe92K1gMVJg/2gdhbuzccuOPeB1azbv+KfZN3bAGzDWMa7Vf/tgGzv5f02DMnOIHN72KQcdiftR3MftOj6ODdYw12g6lVjoP3e2mwuBLDQodkoyVg9uW1aQ7JOclgc/YXz3bw1rwNZh95LuDw/sNxMDspmtsh+bwz2G2cn+X3SS8+BdYrBgBaLGyBZfMdPAAAAaR6VFh0TU9MNDUgcmRraXQgMjAyMC4wOS4xAAB4nJ1VO47dMAzsfQpdYAUOSX1Y520VJAG2yB3S5/4IJdl6WiQB/GSoGMLkYDgi7SO05+Px9dfvMB9+HEdgBJYQ6J/HzMJPJqLD0980khFaJSIlyw1R9LcUvoT/Uayns3As6rSdpYjILkvOwq1WIgvVheXjbxYsuCwsiGpZR62R6Z4WxFSl+fJGjip2OzLubjgfZ5Q9FokErxg+J+P7vtSFRSNKrqMjlWJ7WlJMKBgdAcj3tdjCQlG4z1q7LWDV8uO2ljaxoDz4ilTe6qixWDkn1t3dm93AzdM0+NSb22VJrDyQCel9d7GwSMyGc2Iz2d7shhRJIKMjrvzCNvJnLaQ67qjWureNQWNNfWJ9Xgr4hQ2QT+5y1nN2U2bavSOUftNtL5Ol+1r0yeJuwCUMd88vzcsbgG41j4B7r5iBzsBRmmmO8nyT+5fzGdSZVvu+4qoBXYGjNlpngICpwBGmAkfQNZgKHGEqaMFU4Ah1DWzWWOCnAm7/t0vbKMCV9tTWci4F30J4//44/gCqhhx+lHjsHQAAAXp6VFh0U01JTEVTNDUgcmRraXQgMjAyMC4wOS4xAAB4nG1Ru27bQBD8lZRScFrs8/aWrNLn1QcqZMadCRlGXATwx2eXchPKDYc3Nzc7N7fQcvj1efLzceHl8GOR/PBx4+h8Q35HSZREfV+PREqM86e3w0kBA8kaAVpYtPnE4BohybgI943pXVibAAu6JUOg0ZUaQmCo3CgbQtROmD8DbSSHIBw2vNUBopFcziHsUjKXwTm5qHDhNHMLdt8OknVGajfXNjOouFUoLcsijJWlMYSgapsFehBaunQM4jYboBB7HuHBHpsCVXqNHmOIS5sVhnnGynxOzGXLeS8rogJoMeSuycjWkGx9BGc1JeJO5VObhJm32stLpFM2S96H1rjMXs0aGDlu5kRk3o7t8ue6/ny5Pk8Mv1/X9e/Xy8PjE+TjwXp9+pKb3y7P31/Xh8cX8Inwf1Gu71Q00c6KPvDiiXSn0nuVTNR3qn6v0onGTjXuVWPiXXr+IH28/QOJbbqzFbn2BAAAAQV6VFh0cmRraXRQS0w0NiByZGtpdCAyMDIwLjA5LjEAAHice79v7T0GIOABYkYGCACxeYG4gZHNIQNIMzNjZWiAGCxsDAlAmomJHcJnxlSJWwbO4GZgzGBiZEpgYs5gYmZhYGFlYGVLYGPPYGLnSODgBIpxJXBxZzBxMyRwsiaIMLExcHMxMzGysrFycrCLw5zNwCPOlnFAfH3AfhBnr2HqARM1fXsQO26twIHzCzeB2elfq/fv65YGs9Ut1thNubICzBbOO2fv8ZEdzDZid3DIvtMDZvsr1Tl0thjuBbHDt3g76L5ZATb/9p079tN+lIHZGYub9/d6poDZwmFiB2qMz4LZYgCQNz1CxBe17wAAAQp6VFh0TU9MNDYgcmRraXQgMjAyMC4wOS4xAAB4nJ2SPW4EIQyFe07hCwT5BwyuM6mibJEid0if+ytmlvVusZEiI6R5HvO+eQMUWOPzeP/+gRh8lALEQAKAT6eZwRcjYvHlL1KVe1sK66TOi4HVuwiv8BficW5Kn7S9OluSwpUVdXmpinXNUbBaJFAcOYo7RLTtLGSYo1BV4nH1dqFHyuX/FFmOnYUQey6LVBs4rzuETSRLocm2KOe+jNwfUR3c+z4tbSOVxRMgz9vdNUpSuEo7z9d5ikIZipv4nKvwh5yvVuGqRcdVj44rjcLViMLVDI8ri47BukNBI7p1XFEkWOrewft3PgDeLkf5BU8rpmb9xdchAAAAtnpUWHRTTUlMRVM0NiByZGtpdCAyMDIwLjA5LjEAAHicJY65rQNRCABb+aEtvUXchzZ0KeSuwMUb/COkYQTT1N2P15u7383Pbvr7PC4BZ5M4F0KSUfC5h1kS+UHwVIoYxMCOlodAysyHIFQqk6yF4UjnRhBxcV+LsurcBE4c65gQsp1bABFVR6GZuaACUzcAVURKllFy5rn2UAXtoWDTWqtcQwbNEnkSfulFof+dstb8RZm25+cLg7wy8KvtYqcAAAFyelRYdHJka2l0UEtMNDcgcmRraXQgMjAyMC4wOS4xAAB4nHWQL0wDMRTGX3vtXe92lG5sHHKCwbIsQfAnKO4sCgMOgcMgCAFHAhKDQCyQLEGhCASFmKEXEhQBiyAhBIOEYDAI2t4famjy8v369b2vTT9uLl9ArVAVgmw1VEWq9hGDtlIHC2gqJcwIdmFNC3ab43qKlJr7RZuxMYGuDgE05nT1DR03C/3z8T8+yv08vvRp0W8eVTWCylOSnSLI9oVWADGMMGBHX0EoUBdcDygD5hPEAggqEIQMh0MQcuDDBHEBogqiBh4CwYCrqRGCaZ3guuMh7BDqejRggodUcEJZVPwfNFYHV0lrsdbSm8HWXskn9yvJxs9FrPlg/TO+u34y/N6bUZrYLK0eac0avj2dl4/LfcNTZ8fy6Ludan7e9tNidvJ8KT38epOWH1t+bOXEVk6iebrzIDfDuQnNu7NJ2uv50mLTHwWvC3yHJxabN4z+AlbzWzfzZUyrAAABanpUWHRNT0w0NyByZGtpdCAyMDIwLjA5LjEAAHicrZRLasMwFEXnXsXbgMX76DuOC4XSFlLoHgoddv9Uv8gKcWhk13hwrxUf7n1yNEG6zsvL1w+0i5dpAiZgAcDNO4QAn4yIU/q9UTqQTgpVfIadeod7iP7OFFHhHuVjhCIabVKkxJDZl4WUd56Lst5wRzk9TkHl0NRGzlw12kOZj1Bao/lIozbd+cB0Uw+mmoVJzK4s8V3y2iXFymqkjnK+pVCnpaOwIu38wenOolhrX1I5J/h4Ft7KcuR7WbPAaBba2iP4lz2C0T0yVxSLHDZPhqG5IIqUbkZcf9KcvscpcEP5o5FeKfENLc6WuTCbvtvzyMlQKfN+CuVPkIvh/N+gYiRHrkbnHakmKttMVK6ZqHy/EpoJQGUeVAxdVqJK39lq5BInKmoJkmkJoiJ7oSXTEkRFvjdrAp+5bWXNlrh9OS5Bba7AVM0rwNPbMv0C32McDrTQ/VsAAAFNelRYdFNNSUxFUzQ3IHJka2l0IDIwMjAuMDkuMQAAeJxlj0FvwjAMhf/Kji1KotiO47QVElMvHLYxaccqBxDcqEBoHCbx45fAKKw9xX7+/J7TLWPXLhYRsF19FfNVuWpTH6HolrG8TYpuVnMs2/xCLOfXAq/TiLmmNISi3ZepdvHlUljjSFhEaTSIHCrVWCOWHaDSqWLVgAkSwCsNxgdGUg0ZcpY5K8TgOCtVBRQUGPQh0E4nvmHjpuJ9d1i92w/uQ/4tfrhwODDfhQCC2RmI0066BILLjHcWIAlowIn4PxNNBp2TnCJClp4APSL0Axly9CRIP5LS4RYDK28IvA23byY7awmv9kziqHrSBqlU6+9D/3k6HGsAsz33/c/berPbm1nu+8P+NY3f18ePc7/ZnQzXQCOKphTUwCOKpxTWICNKphTVaP9TqZ9Q7vILTXilR+LOK6IAAACnelRYdHJka2l0UEtMNDggcmRraXQgMjAyMC4wOS4xAAB4nHu/b+09BiDgAWJGBgjggOIGRg4GDSDNzMQGoVnYGDJANDMjEgMig1CJroKbgVGDiZFJgYlZg4mZRYGFVYOJhU2BjV2DiZ1RQYSRjZGdjYWZSRxmOchmBwcGhgeqEO4Be6BDlkDYDvYP3Zbth7L3I9gH9p86KQxV73AAiFWR1Ngj6QWzxQA0IBzVpOSOegAAALF6VFh0TU9MNDggcmRraXQgMjAyMC4wOS4xAAB4nK2SQQoCMQxF9zlFLjAl7Vhr1tbVoIIL7+De+2OSShRHYdSGQH/62wcJAdQ41elyRY9UARA3lvQ2mRnPiYhA349BFKmiV3XET4jnNEoMuf0dZpTtcgqFkhslhsT8G2XoQ/GO5nP5gtJluo+O8I+OsAclyoJZaiHHaFdaiFq5Iyq7I2rtjhTFnWJbeneaMmePuDtUuAHB9XDjmLfYewAAAHt6VFh0U01JTEVTNDggcmRraXQgMjAyMC4wOS4xAAB4nF3MzQnAIAyG4VV6VIihMY1GxJMDOEW3cPimPxTaU3g+eDNap956d214u7RMx8CoKln3QAkqoUAQFCGKtmSoK2abCGMp6wY1/H0WJiVN94vA5pSE+fFVvMFHfh4KEx608UNv1AAAAYV6VFh0cmRraXRQS0w0OSByZGtpdCAyMDIwLjA5LjEAAHice79v7T0GIOABYkYGCBAGYhEgbmBkY0gAiTOzM2QAaWYmoIAGiMHCAaGZYDSbA0SczQGskJkRJsCgA6IZGCVYdUDGa2EowK2DCaYDTZwZhzgLqjg30D+MTApMzBpMTCwKLKwKrGwZTGzsCewcDOycGUycXAlc3BlM3DwJPLwMPHwZTHz8DHwCCQKCDAJCGUxCrAlCXAkiTGysQlyc7Gxs3Dx8AkJc4rAQYhAOP7j5QFXky/0gDptIy4EnU/+D2Tdu+h3wFvcGs12flRxgLxO1B7Fz/t3brzChHCzOeSRif5lgkR2Ifb9qx/4bHefAat7t4d7f7WniAGIHfZ+4v8CsDcyesbbQPkxbE8xe/v6IPbPAIrD60kuODjMvd4PZj+6lOziembgPxD40Z7HDq72vwOx1E/Qc/Gv3ge2Vnhvi8CfR/ACILZkzz/6Jwwqw+BG79XaTVBXB4rXLVOw9YhpsQWwxAH3xYcl39ouVAAABfnpUWHRNT0w0OSByZGtpdCAyMDIwLjA5LjEAAHicnZRLTsNADIb3OYUvUMuveXhNWCGK1AV3YM/9hZNphqkAKUyUhX95/PW3PekC23NbXz4+oT+yLguwgxDA76+7w7sQ0RLHLwkzF9sixipKG4MwsgRP8BdifHeKIctBcc91oFzPUxRFN99wISyuOudFo4+cW22qOnp5O09hLCbcvHguNEchrJZLqxXNNtcRoxnLVsuxLc1zlJiGJd/vCFZ+6OhfXtho7yN27sYD5faTwkOcvilR4dZqBbNNbjqmkbTmFsXKeY6iSFJTo5DL3I6CktvXE3OWanWOkpBZvFEsNVfnpisDRbBIyfebU6vMdqTSvoC4LznZeS/6sCPRUpoXZUtzXgjVRJuXxMXPe7EHSty1YxpZyowX3kfNTcje613o/mPSReqZiHIXEZV+LKLaMxH5IJiOYxExH5mItv+Ce0aAu4OI2EaR+rEE3B1sonRRgOsovNf44DpEd/AK8Hxdly+d/QDVeHlhnwAAAVJ6VFh0U01JTEVTNDkgcmRraXQgMjAyMC4wOS4xAAB4nGWQvU5DMQyFX4WxrVIrPv5Jcq8YUFdo2RFDuXTrVRGCAYmHxykstJOd48/2iTfbzeJ2t9xNPE2Lp9Vgz8tpwm+OyHuUv6gRGTffi7WRcxFLa6YKyZrGtRKjqHapNS8ekhAEOahMpYlAz1qFm6VMVqUKQmIqCtaORWNWCS1TVS/KwUFcmp05VYYljuVidqZMrRoSqLK3doZYs3gKO005oBiqmgMHuQpKGplMqnnMgTtzGoUyqpYQcoO0LjjQanfUCx6METNq61Ks1JpGUEGx2j+staJ3CZADCTdu4SE2QYqXjghrb8okCsR1AjIuDV0KX+z9JNnRL7dM+4/T/Ph+ehsqvX7O89f9/uVwpFU859PxLooP+7ft5/xyeCcbWP5D8b6iMLBdUHZNycDlgirXlH7/AL8DifM2pZIIAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 9 - } - ], - "source": [ - "sfs = [dm.to_mol(s) for s in list(scaffolds)]\n", - "dm.viz.to_image(sfs, n_cols=6)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Assembling\n", - "\n", - "Assemble fragments to create new molecules." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 10 - } - ], - "source": [ - "# Get the fragment set of a molecule\n", - "smiles = \"CCCOCc1cc(c2ncccc2)ccc1\"\n", - "mol = dm.to_mol(smiles)\n", - "frags = dm.fragment.brics(mol)\n", - "\n", - "# Limit the number of fragments to work with because\n", - "# assembling is computationally intensive.\n", - "frags = frags[:3]\n", - "\n", - "# Assemble 8 molecules from the list of fragments\n", - "mols = list(dm.fragment.assemble_fragment_order(frags, max_n_mols=8))\n", - "\n", - "dm.viz.to_image(mols)" - ] - }, - { - "source": [ - "## Decomposition \n", - "\n", - "It's also possible to break a molecule based on a set of chemical transformation and gets the non-overlapping fragments and how they are linked" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(['CCC', 'O', 'C', 'c1ccncc1', 'c1ccccc1'],\n", - " {'C',\n", - " 'CCC',\n", - " 'CCCOCc1cccc(-c2ccccn2)c1',\n", - " 'Cc1cccc(-c2ccccn2)c1',\n", - " 'O',\n", - " 'OCc1cccc(-c2ccccn2)c1',\n", - " 'c1ccc(-c2ccccn2)cc1',\n", - " 'c1ccccc1',\n", - " 'c1ccncc1'},\n", - " )" - ] - }, - "metadata": {}, - "execution_count": 11 - } - ], - "source": [ - "dm.fragment.break_mol(mol, randomize=False, mode=\"brics\", returnTree=True) \n", - "# returns fragments, fragments + intermediate decomposition, decomposition tree" - ] - } - ], - "metadata": { - "kernelspec": { - "name": "python3", - "display_name": "Python 3.7.8 64-bit ('izanagi': conda)", - "metadata": { - "interpreter": { - "hash": "5b8cad2b912e2fd2443f770479ff1d2934a5ec71f5d020b6909cc4121edfd2eb" - } - } - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.8-final" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/docs/tutorials/Preprocessing_Molecules.ipynb b/docs/tutorials/Preprocessing_Molecules.ipynb deleted file mode 100644 index 99360396..00000000 --- a/docs/tutorials/Preprocessing_Molecules.ipynb +++ /dev/null @@ -1,559 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Preprocessing Molecules\n", - "\n", - "You have a dataset of molecules (SMILES) and you want to preprocess it in order to clean and standardize them. You can use `datamol` to easily design your own preprocessing pipeline and execute in parallel." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import datamol as dm\n", - "\n", - "dm.disable_rdkit_log()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The pipeline is applied on every molecules of the dataset and consist on the following steps:\n", - "\n", - "- Convert to a mol.\n", - "- Fix common errors in the mol.\n", - "- Sanitize the mol.\n", - "- Standardize the mol.\n", - "- Generate a standardized SMILES.\n", - "- Generate SELFIES.\n", - "- Generate InChi and InChi key.\n", - "\n", - "The resulting dataset can then saved as a CSV file or an SDF file (both files will have the exact same information stored)." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(642, 4)\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
iupacsmilesexptcalc
04-methoxy-N,N-dimethyl-benzamideCN(C)C(=O)c1ccc(cc1)OC-11.01-9.625
1methanesulfonyl chlorideCS(=O)(=O)Cl-4.87-6.219
23-methylbut-1-eneCC(C)C=C1.832.452
32-ethylpyrazineCCc1cnccn1-5.45-5.809
4heptan-1-olCCCCCCCO-4.21-2.917
\n", - "
" - ], - "text/plain": [ - " iupac smiles expt calc\n", - "0 4-methoxy-N,N-dimethyl-benzamide CN(C)C(=O)c1ccc(cc1)OC -11.01 -9.625\n", - "1 methanesulfonyl chloride CS(=O)(=O)Cl -4.87 -6.219\n", - "2 3-methylbut-1-ene CC(C)C=C 1.83 2.452\n", - "3 2-ethylpyrazine CCc1cnccn1 -5.45 -5.809\n", - "4 heptan-1-ol CCCCCCCO -4.21 -2.917" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Load a dataset\n", - "data = dm.data.freesolv()\n", - "print(data.shape)\n", - "data.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Preprocess using `pd.DataFrame.apply`\n", - "\n", - "Easy and classic way to perform row-wise operations on a dataframe." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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iupacsmilesexptcalcstandard_smilesselfiesinchiinchikey
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32-ethylpyrazineCCc1cnccn1-5.45-5.809CCc1cnccn1[C][C][C][=C][N][=C][C][=N][Ring1][=Branch1]InChI=1S/C6H8N2/c1-2-6-5-7-3-4-8-6/h3-5H,2H2,1H3KVFIJIWMDBAGDP-UHFFFAOYSA-N
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" - ], - "text/plain": [ - " iupac smiles expt calc \\\n", - "0 4-methoxy-N,N-dimethyl-benzamide CN(C)C(=O)c1ccc(cc1)OC -11.01 -9.625 \n", - "1 methanesulfonyl chloride CS(=O)(=O)Cl -4.87 -6.219 \n", - "2 3-methylbut-1-ene CC(C)C=C 1.83 2.452 \n", - "3 2-ethylpyrazine CCc1cnccn1 -5.45 -5.809 \n", - "4 heptan-1-ol CCCCCCCO -4.21 -2.917 \n", - "\n", - " standard_smiles selfies \\\n", - "0 COc1ccc(C(=O)N(C)C)cc1 [C][O][C][=C][C][=C][Branch1][#Branch2][C][=Br... \n", - "1 CS(=O)(=O)Cl [C][S][=Branch1][C][=O][=Branch1][C][=O][Cl] \n", - "2 C=CC(C)C [C][=C][C][Branch1][C][C][C] \n", - "3 CCc1cnccn1 [C][C][C][=C][N][=C][C][=N][Ring1][=Branch1] \n", - "4 CCCCCCCO [C][C][C][C][C][C][C][O] \n", - "\n", - " inchi \\\n", - "0 InChI=1S/C10H13NO2/c1-11(2)10(12)8-4-6-9(13-3)... \n", - "1 InChI=1S/CH3ClO2S/c1-5(2,3)4/h1H3 \n", - "2 InChI=1S/C5H10/c1-4-5(2)3/h4-5H,1H2,2-3H3 \n", - "3 InChI=1S/C6H8N2/c1-2-6-5-7-3-4-8-6/h3-5H,2H2,1H3 \n", - "4 InChI=1S/C7H16O/c1-2-3-4-5-6-7-8/h8H,2-7H2,1H3 \n", - "\n", - " inchikey \n", - "0 OCGXPFSUJVHRHA-UHFFFAOYSA-N \n", - "1 QARBMVPHQWIHKH-UHFFFAOYSA-N \n", - "2 YHQXBTXEYZIYOV-UHFFFAOYSA-N \n", - "3 KVFIJIWMDBAGDP-UHFFFAOYSA-N \n", - "4 BBMCTIGTTCKYKF-UHFFFAOYSA-N " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "smiles_column = \"smiles\"\n", - "\n", - "def _preprocess(row):\n", - " mol = dm.to_mol(row[smiles_column], ordered=True)\n", - " mol = dm.fix_mol(mol)\n", - " mol = dm.sanitize_mol(mol, sanifix=True, charge_neutral=False)\n", - " mol = dm.standardize_mol(mol, disconnect_metals=False, normalize=True, reionize=True, uncharge=False, stereo=True)\n", - "\n", - " row[\"standard_smiles\"] = dm.standardize_smiles(dm.to_smiles(mol))\n", - " row[\"selfies\"] = dm.to_selfies(mol)\n", - " row[\"inchi\"] = dm.to_inchi(mol)\n", - " row[\"inchikey\"] = dm.to_inchikey(mol)\n", - " return row\n", - "\n", - "data_clean = data.apply(_preprocess, axis=1) \n", - "data_clean.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Preprocess using `dm.parallelized`\n", - "\n", - "Parallelize the preprocessing. This approach will only be faster if your dataset is very large." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 642/642 [00:00<00:00, 1317.53it/s]\n" - ] - }, - { - "data": { - "text/html": [ - "
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iupacsmilesexptcalcstandard_smilesselfiesinchiinchikey
04-methoxy-N,N-dimethyl-benzamideCN(C)C(=O)c1ccc(cc1)OC-11.01-9.625COc1ccc(C(=O)N(C)C)cc1[C][O][C][=C][C][=C][Branch1][#Branch2][C][=Br...InChI=1S/C10H13NO2/c1-11(2)10(12)8-4-6-9(13-3)...OCGXPFSUJVHRHA-UHFFFAOYSA-N
1methanesulfonyl chlorideCS(=O)(=O)Cl-4.87-6.219CS(=O)(=O)Cl[C][S][=Branch1][C][=O][=Branch1][C][=O][Cl]InChI=1S/CH3ClO2S/c1-5(2,3)4/h1H3QARBMVPHQWIHKH-UHFFFAOYSA-N
23-methylbut-1-eneCC(C)C=C1.832.452C=CC(C)C[C][=C][C][Branch1][C][C][C]InChI=1S/C5H10/c1-4-5(2)3/h4-5H,1H2,2-3H3YHQXBTXEYZIYOV-UHFFFAOYSA-N
32-ethylpyrazineCCc1cnccn1-5.45-5.809CCc1cnccn1[C][C][C][=C][N][=C][C][=N][Ring1][=Branch1]InChI=1S/C6H8N2/c1-2-6-5-7-3-4-8-6/h3-5H,2H2,1H3KVFIJIWMDBAGDP-UHFFFAOYSA-N
4heptan-1-olCCCCCCCO-4.21-2.917CCCCCCCO[C][C][C][C][C][C][C][O]InChI=1S/C7H16O/c1-2-3-4-5-6-7-8/h8H,2-7H2,1H3BBMCTIGTTCKYKF-UHFFFAOYSA-N
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" - ], - "text/plain": [ - " iupac smiles expt calc \\\n", - "0 4-methoxy-N,N-dimethyl-benzamide CN(C)C(=O)c1ccc(cc1)OC -11.01 -9.625 \n", - "1 methanesulfonyl chloride CS(=O)(=O)Cl -4.87 -6.219 \n", - "2 3-methylbut-1-ene CC(C)C=C 1.83 2.452 \n", - "3 2-ethylpyrazine CCc1cnccn1 -5.45 -5.809 \n", - "4 heptan-1-ol CCCCCCCO -4.21 -2.917 \n", - "\n", - " standard_smiles selfies \\\n", - "0 COc1ccc(C(=O)N(C)C)cc1 [C][O][C][=C][C][=C][Branch1][#Branch2][C][=Br... \n", - "1 CS(=O)(=O)Cl [C][S][=Branch1][C][=O][=Branch1][C][=O][Cl] \n", - "2 C=CC(C)C [C][=C][C][Branch1][C][C][C] \n", - "3 CCc1cnccn1 [C][C][C][=C][N][=C][C][=N][Ring1][=Branch1] \n", - "4 CCCCCCCO [C][C][C][C][C][C][C][O] \n", - "\n", - " inchi \\\n", - "0 InChI=1S/C10H13NO2/c1-11(2)10(12)8-4-6-9(13-3)... \n", - "1 InChI=1S/CH3ClO2S/c1-5(2,3)4/h1H3 \n", - "2 InChI=1S/C5H10/c1-4-5(2)3/h4-5H,1H2,2-3H3 \n", - "3 InChI=1S/C6H8N2/c1-2-6-5-7-3-4-8-6/h3-5H,2H2,1H3 \n", - "4 InChI=1S/C7H16O/c1-2-3-4-5-6-7-8/h8H,2-7H2,1H3 \n", - "\n", - " inchikey \n", - "0 OCGXPFSUJVHRHA-UHFFFAOYSA-N \n", - "1 QARBMVPHQWIHKH-UHFFFAOYSA-N \n", - "2 YHQXBTXEYZIYOV-UHFFFAOYSA-N \n", - "3 KVFIJIWMDBAGDP-UHFFFAOYSA-N \n", - "4 BBMCTIGTTCKYKF-UHFFFAOYSA-N " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "smiles_column = \"smiles\"\n", - "\n", - "def _preprocess(i, row):\n", - "\n", - " dm.disable_rdkit_log()\n", - "\n", - " mol = dm.to_mol(row[smiles_column], ordered=True)\n", - " mol = dm.fix_mol(mol)\n", - " mol = dm.sanitize_mol(mol, sanifix=True, charge_neutral=False)\n", - " mol = dm.standardize_mol(mol, disconnect_metals=False, normalize=True, reionize=True, uncharge=False, stereo=True)\n", - "\n", - " row[\"standard_smiles\"] = dm.standardize_smiles(dm.to_smiles(mol))\n", - " row[\"selfies\"] = dm.to_selfies(mol)\n", - " row[\"inchi\"] = dm.to_inchi(mol)\n", - " row[\"inchikey\"] = dm.to_inchikey(mol)\n", - " return row\n", - "\n", - "data_clean = dm.parallelized(_preprocess, data.iterrows(), arg_type='args', progress=True, total=len(data))\n", - "data_clean = pd.DataFrame(data_clean)\n", - "data_clean.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualize" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": "\n\n \n \n \n \n \n \n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "smiles = data_clean.sample(n=8, random_state=19)[\"standard_smiles\"].tolist()\n", - "mols = [dm.to_mol(s) for s in smiles]\n", - "dm.viz.to_image(mols, legends=smiles, mol_size=(200, 200))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Serialize the cleaned dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Save as CSV\n", - "# data_clean.to_csv(\"/my/path\")\n", - "\n", - "# Or save as an SDF\n", - "# dm.to_sdf(data_clean, \"/my/path\", smiles_column=\"standard_smiles\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "interpreter": { - "hash": "144f8f14f0771df5238b4f97759b46840a71db5ab43af4759f06ce6783a7b2e5" - }, - "kernelspec": { - "display_name": "Python [conda env:datamol]", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/tutorials/The_Basics.ipynb b/docs/tutorials/The_Basics.ipynb index 4b2cfe85..a0219322 100644 --- a/docs/tutorials/The_Basics.ipynb +++ b/docs/tutorials/The_Basics.ipynb @@ -604,7 +604,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.10.5" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/docs/tutorials/Visualization.ipynb b/docs/tutorials/Visualization.ipynb index e0a694dd..633bd3d1 100644 --- a/docs/tutorials/Visualization.ipynb +++ b/docs/tutorials/Visualization.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "1309aaae-9cdf-4482-996c-e252fb6081e4", "metadata": {}, "outputs": [], @@ -28,18 +28,18 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "id": "74fe9eb0-430c-4d4c-ad9f-7e4e7a7734d6", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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[Clustering](https://en.wikipedia.org/wiki/Cluster_analysis) - the act of *grouping a set of objects in such a way that objects in the same group (called a **cluster**) are more similar (in some sense) to each other than to those in other groups (clusters).*\n", - "\n", - "One of the largest challenges in early-stage drug discovery is narrowing down the massive chemical space of approximately **10 to the power of 60 molecules (10^60)** to a list of molecules that have the desired properties for a specific target of interest. This is where computational approaches comes in, taking a large library of small molecules and reduce its size by filtering in/out molecules based on similarity, patterns, predicted physicochemical properties, specific rules, etc. This selection process allows scientists to focus on compounds with the highest chance of success before experimental testing in a lab, saving time and money.\n", - "\n", - "Clustering molecules is an extremely useful process where you can easily manipulate and subdivide large datasets to group compounds into smaller clusters with similar properties. Comparing molecules and their similarities can then be used to discover new molecules with optimal properties and desired biological activity. \n", - "\n", - "### How are compounds clustered?\n", - "\n", - "Compounds can be clustered via multiple clustering algorithms. There are also multiple ways to measure similarity between compounds, and theoretically, any [molecular descriptor](https://pubs.acs.org/doi/abs/10.1021/jm401411z) can be used. ***The current common approach for structural clustering is the [Butina](https://pubs.acs.org/doi/abs/10.1021/ci9803381) algorithm which can use multiple similarity measures. In Datamol, the measure set as the default is the [Tanimoto similarity](http://www.biotech.fyicenter.com/1000134_What_Is_Tanimoto_coefficient.html#:~:text=Tanimoto%20coefficient%20is%20a%20metric,union%20of%20the%20two%20sets.) index, measured on a scale between 0 (not similar) to 1 (most similar)***. After clustering molecules, you can also identify **centroids**. These are essentially the molecules in the middle of the cluster and are frequently used to **represent** **the cluster as a whole**. \n", - "\n", - "For a more detailed breakdown of clustering methods and their uses in computational chemistry, read [here](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.630&rep=rep1&type=pdf).\n", - "\n", - "**Note:** centroids are highlighted here only as an example. Centroid identification is not linked to clustering itself, and there are algorithms commonly utilized that have nothing to do with centroids (i.e. [hierarchical clustering](https://chemaxon.com/presentation/hierarchical-clustering-of-chemical-structures-by-maximum-common-substructures)).\n", - "\n", - "## Molecular Fingerprints\n", - "\n", - "In order for us to perform machine learning techniques or statistical analyses on molecules, we must represent molecules as mathematical objects (i.e vectors). Molecular fingerprints essentially encode the structural characteristics of molecules in the form of vectors enabling us to subsequently leverage statistical techniques to uncover new insights. \n", - "\n", - "The most common fingerprint used today is ECFP4 (extended connectivity fingerprints), also known as the Morgan fingerprint. [Here](https://towardsdatascience.com/a-practical-introduction-to-the-use-of-molecular-fingerprints-in-drug-discovery-7f15021be2b1) is a practical blog that explains what and how to use ECFP4. \n", - "\n", - "## Tutorial\n", - "\n", - "This tutorial will walk you through the following:\n", - "\n", - "1. Loading an example dataset\n", - "2. Calculate fingerprints\n", - "3. Then generate distance matrix\n", - "4. Cluster with the Butina algorithm \n", - "5. Pick diverse molecules from a list\n", - " 1. Why is this useful? \n", - " 1. Resource limitations generally prevent you from experimentally testing as many compounds as you want/are available. Therefore, you want to be able to collect as much information as possible through diversity. By selecting diverse molecules (i.e. one representative example from each chemical series in a list), you can quickly gain information around the effect of structural changes on in vitro activity while exploring a larger chemical space in fewer “shots”.\n", - "6. Pick centroids from a set of molecules\n", - "\n", - "First let’s see what this process would look like on RDKit: \n", - "\n", - "## RDKit Example" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "e69926c7", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'dm' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mrdkit\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mSimDivFilters\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrdSimDivPickers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MaxMinPicker\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m### Clustering compounds\u001b[39;00m\n\u001b[1;32m 9\u001b[0m \n\u001b[1;32m 10\u001b[0m \u001b[38;5;66;03m# Get some mols\u001b[39;00m\n\u001b[0;32m---> 11\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mdm\u001b[49m\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mfreesolv()\n\u001b[1;32m 12\u001b[0m smiles \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msmiles\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39miloc[:]\u001b[38;5;241m.\u001b[39mtolist()\n\u001b[1;32m 13\u001b[0m mols \u001b[38;5;241m=\u001b[39m [Chem\u001b[38;5;241m.\u001b[39mMolFromSmiles(s) \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m smiles]\n", - "\u001b[0;31mNameError\u001b[0m: name 'dm' is not defined" - ] - } - ], - "source": [ - "import numpy as np\n", - "\n", - "from rdkit import Chem\n", - "from rdkit.Chem import DataStructs\n", - "from rdkit.ML.Cluster import Butina\n", - "from rdkit.SimDivFilters.rdSimDivPickers import MaxMinPicker\n", - "\n", - "### Clustering compounds\n", - "\n", - "# Get some mols\n", - "data = dm.data.freesolv()\n", - "smiles = data[\"smiles\"].iloc[:].tolist()\n", - "mols = [Chem.MolFromSmiles(s) for s in smiles]\n", - "\n", - "# Create fingerprints\n", - "fps = [Chem.RDKFingerprint(x) for x in mols]\n", - "\n", - "# Calculate distance matrix\n", - "dists = []\n", - "n_mols = len(mols)\n", - "\n", - "for i in range(1, n_mols):\n", - " dist = DataStructs.cDataStructs.BulkTanimotoSimilarity(\n", - " fps[i], fps[:i], returnDistance=True\n", - " )\n", - " dists.extend([x for x in dist])\n", - "\n", - "cutoff = 0.2\n", - "# now cluster the data\n", - "cluster_indices = Butina.ClusterData(dists, n_mols, cutoff, isDistData=True)\n", - "cluster_mols = [operator.itemgetter(*cluster)(mols) for cluster in cluster_indices]\n", - "\n", - "# Make single mol cluster a list\n", - "cluster_mols = [[c] if isinstance(c, Chem.rdchem.Mol) else c for c in cluster_mols]\n", - "\n", - "### Pick diverse compounds\n", - "# Get some mols\n", - "data = dm.data.freesolv()\n", - "smiles = data[\"smiles\"].iloc[:].tolist()\n", - "mols = [Chem.MolFromSmiles(s) for s in smiles]\n", - "\n", - "# Calculate fingerprints\n", - "fps = [Chem.RDKFingerprint(x) for x in mols]\n", - "\n", - "def distij(i, j, features=fps):\n", - " return 1.0 - DataStructs.cDataStructs.TanimotoSimilarity(fps[i], fps[j])\n", - "\n", - "npick = 10\n", - "seed = 0\n", - "\n", - "picker = MaxMinPicker()\n", - "initial_picks = []\n", - "picked_inds = picker.LazyPick(distij, len(mols), npick, firstPicks=initial_picks, seed=seed)\n", - "picked_inds = np.array(picked_inds)\n", - "picked_mols = [mols[x] for x in picked_inds]\n", - "\n", - "picked_inds, picked_mols" - ] - }, - { - "cell_type": "markdown", - "id": "fa727157", - "metadata": {}, - "source": [ - "## Datamol Example\n", - "\n", - "**Note:** Datamol abstracts away the explicit steps 2 (calculating fingerprints) and 3 (generating a distance matrix) of the tutorial" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "b87a7afe", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - 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"execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import datamol as dm\n", - "\n", - "# Load example dataset\n", - "data = dm.data.freesolv()\n", - "smiles = data[\"smiles\"].iloc[:].tolist()\n", - "mols = [dm.to_mol(s) for s in smiles]\n", - "\n", - "# Cluster the mols\n", - "clusters, mol_clusters = dm.cluster_mols(mols, cutoff=0.7)\n", - "\n", - "# Cluster #1\n", - "dm.viz.to_image(mol_clusters[0], mol_size=(100, 100), n_cols=6, max_mols=18)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "71b09c2e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - 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"dm.viz.to_image(centroids, mol_size=(100, 100), n_cols=6)" - ] - }, - { - "cell_type": "markdown", - "id": "a233a779-6ce1-4e8c-ade5-86eaa627f039", - "metadata": { - "tags": [] - }, - "source": [ - "**Note**: Datamol provides one method (Butina using Tanimoto/ECFP for distances computations) for clustering molecules. In practice, an infinite number of methods exists and the user should build them as needed. Please feel free to contribute to Datamol if you wish to add any specific methods that are useful! \n", - "\n", - "## Understanding key parameters\n", - "\n", - "- Determining an appropriate threshold for cutoff\n", - " - Butina uses distances (which is 1 - distance) and the cutoff is dependent on the distance metric used. As mentioned earlier, Datamol uses Tanimoto with ECFP fingerprint. Therefore the distance cutoff is 1 - Tanimoto.\n", - " - Generally speaking, if you have a very small distance cutoff, compounds must be extremely similar (i.e. high Tanimoto score) in order to be grouped into one cluster. Therefore, with a small distance cutoff, you’ll get more clusters with fewer compounds per cluster. Vice versa is true.\n", - "\n", - "**Note:** This is an extremely general overview, in reality, the output greatly depends on both the size and diversity of the dataset being used. There is no “default” cutoff that is set in Datamol and instead each user should set cutoffs according to their specific dataset and use case. \n", - "\n", - "You can also see a more detailed definition of the methods, arguments and their returns, [here](https://github.com/datamol-org/datamol/blob/main/datamol/cluster.py#L173). \n", - "\n", - "## References\n", - "\n", - "- Macs in Chemistry - [https://www.macinchem.org/reviews/clustering/clustering.php](https://www.macinchem.org/reviews/clustering/clustering.php)\n", - "- TeachOpenCADD - [https://projects.volkamerlab.org/teachopencadd/talktorials/T005_compound_clustering.html#Picking-diverse-compounds](https://projects.volkamerlab.org/teachopencadd/talktorials/T005_compound_clustering.html#Picking-diverse-compounds)\n", - "- [https://jcheminf.biomedcentral.com/articles/10.1186/s13321-020-00445-4](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-020-00445-4)\n", - "- [https://towardsdatascience.com/a-practical-introduction-to-the-use-of-molecular-fingerprints-in-drug-discovery-7f15021be2b1](https://towardsdatascience.com/a-practical-introduction-to-the-use-of-molecular-fingerprints-in-drug-discovery-7f15021be2b1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6acbd158-cca3-4899-b781-c6493c6754a7", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "main_language": "python", - "notebook_metadata_filter": "-all" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/tutorials/new/Fragment.ipynb b/docs/tutorials/new/Fragment.ipynb deleted file mode 100644 index 5cf3acf8..00000000 --- a/docs/tutorials/new/Fragment.ipynb +++ /dev/null @@ -1,1442 +0,0 @@ -{ - "cells": [ - { - "attachments": { - "053aeef2-86e6-4191-bd4a-f82359a8efc4.png": { - "image/png": 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" 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" 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" - } - }, - "cell_type": "markdown", - "id": "7f18a74d", - "metadata": {}, - "source": [ - "# Fragmenting Compounds\n", - "\n", - "\n", - "## Background\n", - "\n", - "A [fragment](https://www.frontiersin.org/articles/10.3389/fmolb.2020.00180/full#:~:text=Fragment%2Dbased%20drug%20discovery%20(FBDD,in%20target%2Dbased%20drug%20discovery.) is essentially a small molecule that has a low molecular weight and small size, often representing a small part of a bigger, drug-like compound. These small compounds are of great importance in drug discovery, especially in the early stages. Fragments are used to identify small chemical and functional groups that bind, even if weakly, to the target of interest and thus serve as useful starting points in a [medicinal chemistry](https://en.wikipedia.org/wiki/Medicinal_chemistry) campaign. Once identified, fragments act as building blocks and are subsequently chemically modified (addition/removal of specific chemical groups) to improve the overall interaction of the newly generated compounds with the target of interest. \n", - "\n", - "Generally speaking, fragments will follow the [rule of three](https://www.sciencedirect.com/science/article/abs/pii/S1359644603028319?via%3Dihub). However, this is not always the case. Different researchers will have their own definitions of what is deemed a fragment:\n", - "\n", - "1. **Molecular weight less than 300 Da**\n", - "2. **ClogP value less than 3**\n", - " 1. ClogP is a well established measure of a compound’s hydrophilicity which is important for absorption, permeation, and other drug-related physical properties.\n", - "3. **Less than 3 hydrogen donor and acceptor groups.**\n", - "\n", - "Existing molecules can also be split into smaller fragments, a good visual is shown below of the fragmentation process: \n", - "\n", - "![image.png](attachment:f4220217-9021-45e0-8c56-a33cc7f65ce3.png) \n", - "\n", - "[***Source***](https://www.frontiersin.org/articles/10.3389/fchem.2018.00229/full)\n", - "\n", - "## Fragment-Based Approaches for Compound Optimization\n", - "\n", - "Standard fragment-based approaches for compound optimization are: \n", - "\n", - "1. **Fragment growing** - adding chemical groups to the fragment to improve properties.\n", - "2. **Fragment merging/scaffold hopping** - combining fragments that have an overlapped binding site.\n", - "3. **Fragment linking** - linking two or more fragments together to drastically improve [binding affinities](https://www.malvernpanalytical.com/en/products/measurement-type/binding-affinity#:~:text=What%20is%20Binding%20Affinity%3F,(e.g.%20drug%20or%20inhibitor).).\n", - "\n", - "You can read more about these methods in detail [here](https://www.frontiersin.org/articles/10.3389/fmolb.2020.00180/full). \n", - "\n", - "## Fragment Generation\n", - "\n", - "![image.png](attachment:ddd64e95-9256-46e4-9c25-2560deadf01c.png)\n", - "\n", - "***[Source](https://www.researchgate.net/figure/A-schematic-overview-of-a-molecular-fragmentation-process-For-a-single-step_fig2_353714355)***\n", - "\n", - "Fragments are essentially generated by breaking specified bonds in a larger molecule. There are multiple ways approaches that one can take to fragment a molecule. The methods covered below include; RECAP, BRICS, FraggleSim and AnyBreak. \n", - "\n", - "1. **RECAP - R**etrosynthetic **C**ombinatorial **A**nalysis **P**rocedure \n", - " 1. Alkyl groups smaller than five carbons and cyclic bonds are left intact while compounds are dissected based on 11 pre-specified bond types\n", - "2. **BRICS -** **B**reaking **R**etrosynthetically **I**nteresting **C**hemical **S**ubstructures\n", - " 1. In BRICS, compounds are dissected based on 16 bond types while considering the chemical environment and surrounding substructures. \n", - " 2. Both RECAP and BRICS are examples of systematic fragmentation\n", - "3. **FraggleSim**\n", - " 1. RDKit uses the Fraggle similarity algorithm developed by Jameed Hussain and Gavin Harper of GSK. Read more about the details of the algorithm [here](https://raw.github.com/rdkit/UGM_2013/master/Presentations/Hussain.Fraggle.pdf) and [here](https://www.rdkit.org/docs/source/rdkit.Chem.Fraggle.FraggleSim.html).\n", - "4. **AnyBreak**\n", - " 1. This method uses BRICS first and fallback to generating all possible fragmentation if it doesn't work. \n", - "\n", - "**Note:** It’s challenging to point to one method and refer to it as the status quo. The method you should use depends on what exactly you are trying to do with the fragments, the types of molecules you’re working with etc. Generally speaking, it is ideal to fragment a molecule in a way that is synthesizable in the lab, and each of the methods listed above have slight variations in their approach. There’s no point fragmenting a molecule at certain bonds if these bonds have never been broken before in a lab setting, it’s not realistic. \n", - "\n", - "Once molecules are fragmented, the next step is typically a matched molecular pair analysis (MMPA). This analysis compares the chemical structure of two molecules that only differ by a **single chemical transformation** (i.e. changing one functional group). MMP’s are useful to analyze a large collection of compounds because the minimal structural differences make it much easier to interpret any observable changes in physical or biological properties. We will not cover MMPA’s in this tutorial. See below for a visualization of a matched molecular pair: \n", - "\n", - "![image.png](attachment:053aeef2-86e6-4191-bd4a-f82359a8efc4.png)\n", - "\n", - "[Source](https://en.wikipedia.org/wiki/Matched_molecular_pair_analysis#:~:text=Matched%20molecular%20pair%20analysis%20(MMPA,matched%20molecular%20pairs%20(MMP).)\n", - "\n", - "**Note:** Sometimes the term fragment is used synonymously with scaffolds. However, scaffolds are better defined as key core structures of a compound, often critical and essential for binding, whereas a fragment may only partially match with a “core structure”. \n", - "\n", - "## Tutorial\n", - "\n", - "Now let’s walkthrough how you could do this in RDKit and then compare it with Datamol. Starting from a cluster of molecules, this tutorial will cover the following:\n", - "\n", - "1. Generate list of all fragments in different ways as described above\n", - " 1. RECAP, BRICS, FraggleSim\n", - "2. Show how to return the results as a hierarchy of nodes instead of just a visualized set of fragments allowing for more flexibility in the manipulation of the results\n", - "3. Fragment molecules on specific bonds suitable for an MMP analysis\n", - "4. Briefly exploring other manipulations\n", - " 1. Assembling - assemble fragments to create new molecules. Limit the number of fragments you’d work with because it’s computationally intensive.\n", - " 2. Decomposition - break a molecule down to get non-overlapping fragments and how they are linked.\n", - "\n", - "## RDKit Example" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a5e049ff", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'mol1_f' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m smiles \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCCCOCc1cc(c2ncccc2)ccc1\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 10\u001b[0m mol \u001b[38;5;241m=\u001b[39m Chem\u001b[38;5;241m.\u001b[39mMolFromSmiles(smiles)\n\u001b[0;32m---> 12\u001b[0m mol1_f \u001b[38;5;241m=\u001b[39m Chem\u001b[38;5;241m.\u001b[39mGetMolFrags(\u001b[43mmol1_f\u001b[49m, asMols\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 13\u001b[0m MolsToGridImage(mol1_f)\n", - "\u001b[0;31mNameError\u001b[0m: name 'mol1_f' is not defined" - ] - } - ], - "source": [ - "from rdkit import Chem\n", - "from rdkit.Chem.Draw import IPythonConsole, MolsToGridImage\n", - "from rdkit.Chem import BRICS\n", - "from rdkit.Chem import Recap\n", - "from rdkit.Chem import rdMMPA\n", - "\n", - "from rdkit.Chem.Fraggle import FraggleSim\n", - "\n", - "smiles = \"CCCOCc1cc(c2ncccc2)ccc1\"\n", - "mol = Chem.MolFromSmiles(smiles)\n", - "\n", - "mol1_f = Chem.GetMolFrags(mol1_f, asMols=True)\n", - "MolsToGridImage(mol1_f)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a393217f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Decompose compounds based on BRICS algorithm. \n", - "# Calling dm.fragment.brics runs this algorithm, as well as fixes/sanitizes fragments in one line of code.\n", - "\n", - "brics_frags = BRICS.BRICSDecompose(mol, returnMols=True, singlePass=True)\n", - "brics_frags = list(brics_frags)\n", - "MolsToGridImage(brics_frags)\n", - "\n", - "# Recap, FraggleSim and rdMMPA can be run in a similar manner as the BRICS algorithm above." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "fe058c37", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import datamol as dm\n", - "\n", - "smiles = \"CCCOCc1cc(c2ncccc2)ccc1\"\n", - "mol = dm.to_mol(smiles)\n", - "mol" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "7c2059f8", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "RDKit WARNING: [10:26:25] WARNING: not removing hydrogen atom without neighbors\n", - "[10:26:25] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:26:25] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:26:25] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:26:25] WARNING: not removing hydrogen atom without neighbors\n", - "[10:26:25] WARNING: not removing hydrogen atom without neighbors\n", - "[10:26:25] WARNING: not removing hydrogen atom without neighbors\n", - "[10:26:25] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:26:25] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:26:25] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:26:25] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:26:25] WARNING: not removing hydrogen atom without neighbors\n", - "[10:26:25] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:26:25] WARNING: not removing hydrogen atom without neighbors\n", - "[10:26:25] WARNING: not removing hydrogen atom without neighbors\n", - "[10:26:25] WARNING: not removing hydrogen atom without neighbors\n", - "[10:26:25] WARNING: not removing hydrogen atom without neighbors\n", - "[10:26:25] WARNING: not removing hydrogen atom without neighbors\n" - ] - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# BRICS\n", - "frags = dm.fragment.brics(mol)\n", - "dm.to_image(frags, n_cols=3)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "99027b1a", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "RDKit WARNING: [10:27:02] WARNING: not removing hydrogen atom without neighbors\n", - "[10:27:02] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:27:02] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:27:02] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:27:02] WARNING: not removing hydrogen atom without neighbors\n", - "[10:27:02] WARNING: not removing hydrogen atom without neighbors\n", - "[10:27:02] WARNING: not removing hydrogen atom without neighbors\n", - "[10:27:02] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:27:02] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:27:02] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:27:02] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:27:02] WARNING: not removing hydrogen atom without neighbors\n", - "[10:27:02] WARNING: not removing hydrogen atom without neighbors\n", - "[10:27:02] WARNING: not removing hydrogen atom without neighbors\n", - "[10:27:02] WARNING: not removing hydrogen atom without neighbors\n", - "[10:27:02] WARNING: not removing hydrogen atom without neighbors\n" - ] - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - 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without neighbors\n", - "[10:27:33] WARNING: not removing hydrogen atom without neighbors\n" - ] - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Recap\n", - "frags = dm.fragment.recap(mol)\n", - "dm.viz.to_image(frags, n_cols=3)" - ] - }, - { - "cell_type": "markdown", - "id": "54bceb67", - "metadata": {}, - "source": [ - "What you can also do is assemble some new molecules based off a list of fragments. This is how fragments are used as building blocks for larger, more optimized molecules. By having an understanding of the properties of the underlying fragments, you can essentially run a “mix and match” process to generate optimal molecules. \n", - "\n", - "Assembling molecules from fragments is computationally expensive. Make sure you use the parameters: \n", - "\n", - "- ***frags***\n", - "- **max_n_mols**\n", - "\n", - "To limit the number of fragments to work with and the number of molecules to be assembled. " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "e9551e69", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "RDKit WARNING: [10:27:40] WARNING: not removing hydrogen atom without neighbors\n", - "[10:27:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:27:40] WARNING: not removing hydrogen atom without neighbors\n", - "[10:27:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:27:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:27:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:27:40] WARNING: not removing hydrogen atom without neighbors\n", - "[10:27:40] WARNING: not removing hydrogen atom without neighbors\n", - "[10:27:40] WARNING: not removing hydrogen atom without neighbors\n", - "[10:27:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:27:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:27:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:27:40] WARNING: not removing hydrogen atom without neighbors\n", - "[10:27:40] WARNING: not removing hydrogen atom without neighbors\n", - "[10:27:40] WARNING: not removing hydrogen atom without neighbors\n", - "[10:27:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:27:40] WARNING: not removing hydrogen atom without neighbors\n", - "[10:27:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit ERROR: [10:27:40] Explicit valence for atom # 2 C, 5, is greater than permitted\n", - "[10:27:40] Explicit valence for atom # 2 C, 5, is greater than permitted\n", - "RDKit ERROR: [10:27:40] Explicit valence for atom # 3 C, 5, is greater than permitted\n", - "RDKit ERROR: [10:27:40] Explicit valence for atom # 2 C, 5, is greater than permitted\n", - "RDKit ERROR: [10:27:40] Explicit valence for atom # 3 C, 5, is greater than permitted\n", - "[10:27:40] Explicit valence for atom # 3 C, 5, is greater than permitted\n", - "[10:27:40] Explicit valence for atom # 2 C, 5, is greater than permitted\n", - "[10:27:40] Explicit valence for atom # 3 C, 5, is greater than permitted\n", - "RDKit ERROR: [10:27:40] Explicit valence for atom # 5 C, 5, is greater than permitted\n", - "[10:27:40] Explicit valence for atom # 5 C, 5, is greater than permitted\n", - "RDKit ERROR: [10:27:40] Explicit valence for atom # 5 C, 5, is greater than permitted\n", - "[10:27:40] Explicit valence for atom # 5 C, 5, is greater than permitted\n", - "RDKit ERROR: [10:27:41] Explicit valence for atom # 2 C, 5, is greater than permitted\n", - "[10:27:41] Explicit valence for atom # 2 C, 5, is greater than permitted\n", - "RDKit ERROR: [10:27:41] Explicit valence for atom # 2 C, 5, is greater than permitted\n", - "[10:27:41] Explicit valence for atom # 2 C, 5, is greater than permitted\n", - "RDKit ERROR: [10:27:41] Explicit valence for atom # 17 C, 5, is greater than permitted\n", - "[10:27:41] Explicit valence for atom # 17 C, 5, is greater than permitted\n", - "RDKit ERROR: [10:27:41] Explicit valence for atom # 17 C, 5, is greater than permitted\n", - "[10:27:41] Explicit valence for atom # 17 C, 5, is greater than permitted\n", - "RDKit ERROR: [10:27:41] Explicit valence for atom # 3 C, 5, is greater than permitted\n", - "[10:27:41] Explicit valence for atom # 3 C, 5, is greater than permitted\n", - "RDKit ERROR: [10:27:41] Explicit valence for atom # 3 C, 5, is greater than permitted\n", - "[10:27:41] Explicit valence for atom # 3 C, 5, is greater than permitted\n" - ] - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - 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"# Get the fragment set of a molecule\n", - "smiles = \"CCCOCc1cc(c2ncccc2)ccc1\"\n", - "mol = dm.to_mol(smiles)\n", - "frags = dm.fragment.brics(mol)\n", - "\n", - "# Limit the number of fragments to work with because assembling is computationally intensive.\n", - "frags = frags[:3]\n", - "\n", - "# Assemble 8 molecules from the list of fragments\n", - "mols = list(dm.fragment.assemble_fragment_order(frags, max_n_mols=8))\n", - "\n", - "dm.viz.to_image(mols, n_cols=3)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "93f77ef0", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "RDKit WARNING: [10:28:03] WARNING: not removing hydrogen atom without neighbors\n", - "[10:28:03] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:28:03] WARNING: not removing hydrogen atom without neighbors\n", - "[10:28:03] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:28:03] WARNING: not removing hydrogen atom without neighbors\n", - "[10:28:03] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:28:03] WARNING: not removing hydrogen atom without neighbors\n", - "[10:28:03] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:28:03] WARNING: not removing hydrogen atom without neighbors\n", - "[10:28:03] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:28:03] WARNING: not removing hydrogen atom without neighbors\n", - "[10:28:03] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:28:03] WARNING: not removing hydrogen atom without neighbors\n", - "[10:28:03] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [10:28:03] WARNING: not removing hydrogen atom without neighbors\n", - "[10:28:03] WARNING: not removing hydrogen atom without neighbors\n" - ] - }, - { - "data": { - "text/plain": [ - "(['CCC', 'O', 'C', 'c1ccncc1', 'c1ccccc1'],\n", - " {'C',\n", - " 'CCC',\n", - " 'CCCOCc1cccc(-c2ccccn2)c1',\n", - " 'Cc1cccc(-c2ccccn2)c1',\n", - " 'O',\n", - " 'OCc1cccc(-c2ccccn2)c1',\n", - " 'c1ccc(-c2ccccn2)cc1',\n", - " 'c1ccccc1',\n", - " 'c1ccncc1'},\n", - " )" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Decomposition\n", - "# It's also possible to break a molecule based on a set of chemical transformation and gets the non-overlapping fragments and how they are linked\n", - "\n", - "dm.fragment.break_mol(mol, randomize=False, mode=\"brics\", returnTree=True) \n", - "# returns fragments, fragments + intermediate decomposition, decomposition tree" - ] - }, - { - "cell_type": "markdown", - "id": "4a2dae4e", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "- [https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.6b00596](https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.6b00596)\n", - "- RDKit Cook Book - Creating fragments - [https://www.rdkit.org/docs/Cookbook.html#create-fragments](https://www.rdkit.org/docs/Cookbook.html#create-fragments)" - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "main_language": "python", - "notebook_metadata_filter": "-all" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/tutorials/new/Fuzzyscaffolds.ipynb b/docs/tutorials/new/Fuzzy_Scaffolds.ipynb similarity index 100% rename from docs/tutorials/new/Fuzzyscaffolds.ipynb rename to docs/tutorials/new/Fuzzy_Scaffolds.ipynb diff --git a/docs/tutorials/new/Generatescaffold.ipynb b/docs/tutorials/new/Generate_Scaffold.ipynb similarity index 100% rename from docs/tutorials/new/Generatescaffold.ipynb rename to docs/tutorials/new/Generate_Scaffold.ipynb diff --git a/docs/tutorials/new/Generatingconformers.ipynb b/docs/tutorials/new/Generating_Conformers.ipynb similarity index 100% rename from docs/tutorials/new/Generatingconformers.ipynb rename to docs/tutorials/new/Generating_Conformers.ipynb diff --git a/mkdocs.yml b/mkdocs.yml index d0b6f3ec..75300630 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -14,9 +14,8 @@ nav: - Usage: usage.md - Tutorials: - The Basics: tutorials/The_Basics.ipynb - - Preprocessing Molecules: tutorials/Preprocessing_Molecules.ipynb - - Cluster Molecules: tutorials/Cluster_Molecules.ipynb - - Fragment and Scaffold: tutorials/Fragment_and_Scaffold.ipynb + - Clustering: tutorials/Clustering.ipynb + - Fragment: tutorials/Fragment.ipynb - Visualization: tutorials/Visualization.ipynb - Working with local and remote data: tutorials/Filesystem.ipynb - API: diff --git a/news/tutos.rst b/news/tutos.rst index 7c344d3d..ea7c1854 100644 --- a/news/tutos.rst +++ b/news/tutos.rst @@ -6,6 +6,7 @@ * Revamped all the datamol tutorials and add new tutorials. * Improve documentation for `dm.standardize_mol()` +* Multiple various docstring improvments. **Deprecated:** From 1954adf2edb2e6b9d5d5f52563e0946f1d74bac9 Mon Sep 17 00:00:00 2001 From: Hadrien Mary Date: Sun, 4 Sep 2022 13:49:31 -0400 Subject: [PATCH 03/15] warp up tutos --- datamol/viz/_viz.py | 2 +- docs/index.md | 6 +- docs/tutorials/Conformers.ipynb | 600 + docs/tutorials/Descriptors.ipynb | 1418 + docs/tutorials/Fragment.ipynb | 1094 +- docs/tutorials/Fuzzy_Scaffolds.ipynb | 1567 + docs/tutorials/Preprocessing.ipynb | 1013 + docs/tutorials/Scaffolds.ipynb | 638 + ...ne_DNA_Libary_5530cmpds_20200831_SMALL.sdf | 77062 ++++++++++++++++ docs/tutorials/images/Conformers_1.png | Bin 0 -> 310889 bytes docs/tutorials/images/Descriptors_1.png | Bin 0 -> 40925 bytes docs/tutorials/images/Fragment_1.png | Bin 0 -> 422734 bytes docs/tutorials/images/Fragment_2.png | Bin 0 -> 67840 bytes docs/tutorials/images/Fragment_3.png | Bin 0 -> 34879 bytes docs/tutorials/images/Preprocess_1.png | Bin 0 -> 333241 bytes docs/tutorials/images/Scaffolds_1.png | Bin 0 -> 102167 bytes docs/tutorials/new/Descriptors.ipynb | 215 - docs/tutorials/new/Fuzzy_Scaffolds.ipynb | 3451 - docs/tutorials/new/Generate_Scaffold.ipynb | 598 - .../tutorials/new/Generating_Conformers.ipynb | 612 - docs/tutorials/new/Preprocessing.ipynb | 555 - env.yml | 1 + mkdocs.yml | 8 +- news/tutos.rst | 2 +- setup.py | 1 + 25 files changed, 82845 insertions(+), 5998 deletions(-) create mode 100644 docs/tutorials/Conformers.ipynb create mode 100644 docs/tutorials/Descriptors.ipynb create mode 100644 docs/tutorials/Fuzzy_Scaffolds.ipynb create mode 100644 docs/tutorials/Preprocessing.ipynb create mode 100644 docs/tutorials/Scaffolds.ipynb create mode 100644 docs/tutorials/data/Enamine_DNA_Libary_5530cmpds_20200831_SMALL.sdf create mode 100644 docs/tutorials/images/Conformers_1.png create mode 100644 docs/tutorials/images/Descriptors_1.png create mode 100644 docs/tutorials/images/Fragment_1.png create mode 100644 docs/tutorials/images/Fragment_2.png create mode 100644 docs/tutorials/images/Fragment_3.png create mode 100644 docs/tutorials/images/Preprocess_1.png create mode 100644 docs/tutorials/images/Scaffolds_1.png delete mode 100644 docs/tutorials/new/Descriptors.ipynb delete mode 100644 docs/tutorials/new/Fuzzy_Scaffolds.ipynb delete mode 100644 docs/tutorials/new/Generate_Scaffold.ipynb delete mode 100644 docs/tutorials/new/Generating_Conformers.ipynb delete mode 100644 docs/tutorials/new/Preprocessing.ipynb diff --git a/datamol/viz/_viz.py b/datamol/viz/_viz.py index 46b1795c..87fa2492 100644 --- a/datamol/viz/_viz.py +++ b/datamol/viz/_viz.py @@ -58,7 +58,7 @@ def to_image( - If set to a molecule, it is used as a template for alignment with `dm.align.template_align()`. - If set to False, no alignment is performed. For a more custom alignment, we suggest using directly the module `dm.align` instead. - kwargs: Additional arguments to pass to the drawing function. See RDKit + **kwargs: Additional arguments to pass to the drawing function. See RDKit documentation related to `MolDrawOptions` for more details at https://www.rdkit.org/docs/source/rdkit.Chem.Draw.rdMolDraw2D.html. """ diff --git a/docs/index.md b/docs/index.md index f7b9a9cb..8ae16a26 100644 --- a/docs/index.md +++ b/docs/index.md @@ -19,10 +19,12 @@ mamba install -c conda-forge datamol ``` !!! tips -You can replace `mamba` by `conda`. + + You can replace `mamba` by `conda`. !!! note -We highly recommend using a Conda Python distribution to install Datamol. The package is also pip installable if you need it: `pip install datamol`. + + We highly recommend using a [Conda Python distribution](https://github.com/conda-forge/miniforge) to install Datamol. The package is also pip installable if you need it: `pip install datamol`. ## Quick API Tour diff --git a/docs/tutorials/Conformers.ipynb b/docs/tutorials/Conformers.ipynb new file mode 100644 index 00000000..adb196e0 --- /dev/null +++ b/docs/tutorials/Conformers.ipynb @@ -0,0 +1,600 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ad8338f0", + "metadata": {}, + "source": [ + "# Generating Conformers\n", + "\n", + "A drug-like molecule can exist in a variety of diverse 3D shapes depending on the number of rotatable bonds, bond order, torsion, and in general, its degree of freedom. Each individual 3D spatial arrangement of a molecule is defined as ***conformer*** and each conformer may have different properties (e.g. relative energy). This is why the [sampling of the conformational space](https://pubs.acs.org/doi/full/10.1021/acs.jcim.7b00221), often referred to as conformational search, is a key step to understand the 3D properties of a given compound. You must factor in all the possible conformers and their respective properties in order to achieve the best representation of a molecule. It is a necessary step in any [virtual screening](https://en.wikipedia.org/wiki/Virtual_screening#:~:text=Virtual%20screening%20(VS)%20is%20a,a%20protein%20receptor%20or%20enzyme.) campaign.\n", + "\n", + "**Note:** You can see a good visualization on how relative energy of conformers changes based on manual manipulation of bond angles [here](https://www.sas.upenn.edu/~kimg/mcephome/chem502/ethbutconform/ethbutmm2.html#:~:text=The%20highest%20energy%20conformer%2C%20the,energy%20of%203.5803%20kcal%2F%20mol.). \n", + "\n", + "A common term that you will see throughout this example is ***RMSD*** which stands for root-mean-square deviation. RMSD is widely used as a similarity measure when analyzing conformations: the smaller the RMSD between two conformers, the more similar in 3D spatial arrangement they are. Once conformers are generated, they are usually pruned on RMSD, meaning, structures that are redundant and essentially correspond to the same conformation are removed from the list. \n", + "\n", + "**Note:** RMSD is not the only measure of conformer similarity, and it does have its limitations. If you’re interested in learning more about all the various ways in which chemical structural similarity can be measured, read more [here.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6068280/) \n", + "\n", + "### How are conformers generated?\n", + "\n", + "The current default RDKit method used to generate conformers leverages various versions of experimental-torsion distance geometry with additional basic knowledge ([ETKDG](https://pubs.acs.org/doi/full/10.1021/acs.jcim.5b00654)) created by Riniker and Landrum. From the RDKit book the default algorithm followed is:\n", + "\n", + "1. The molecule’s distance bounds matrix is calculated based on the connection table and a set of rules.\n", + "2. The bounds matrix is smoothed using a triangle-bounds smoothing algorithm.\n", + "3. A random distance matrix that satisfies the bound's matrix is generated.\n", + "4. This distance matrix is embedded in 3D dimensions (producing coordinates for each atom).\n", + "5. The resulting coordinates are cleaned up somewhat using the “distance geometry force field”, based on distance constraints from the bounds matrix. \n", + "\n", + "The first 5 steps describe the “ETDG” approach. The additional “K” in ETKDG just defines further constraints from chemical knowledge such as “aromatic rings are to be flat or bonds connected to triple bonds are to be collinear”. These additional constraints introduce a certain level of “chemical awareness” that helps generate correct conformers which are chemically and physically valid. Read more [here](https://www.blopig.com/blog/2016/06/advances-in-conformer-generation-etkdg-and-etdg/), [here](https://greglandrum.github.io/rdkit-blog/conformers/exploration/2021/01/31/looking-at-random-coordinate-embedding.html) and [here](https://greglandrum.github.io/rdkit-blog/conformers/exploration/2021/02/22/etkdg-and-distance-constraints.html). \n", + "\n", + "![Conformers_1.png](./images/Conformers_1.png)\n", + "\n", + "***[Source](https://pubs.acs.org/doi/10.1021/acs.jcim.5b00654)***\n", + "\n", + "## Tutorial\n", + "\n", + "Now let’s start with a tutorial on how you would go about generating conformers via RDKit.\n", + "\n", + "## RDKit Example\n", + "\n", + "Below is an example of how you would go about generating conformers in RDKit." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4e45a284", + "metadata": {}, + "outputs": [], + "source": [ + "from rdkit import Chem\n", + "from rdkit.Chem import AllChem\n", + "from rdkit.Chem import rdDistGeom\n", + "from rdkit.Chem import rdMolAlign\n", + "from rdkit.Chem import rdMolDescriptors\n", + "from rdkit.Chem import rdMolTransforms\n", + "from rdkit.Chem import rdForceFieldHelpers\n", + "\n", + "from rdkit.Chem import PyMol\n", + "import copy\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4b9cefae", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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bN29256Lp6eltLrBXLCYvL687lyACQzEqcOxSoRMnTig27tu3D8DcuXO5qqojLly4wC4wcnZ2Li4uftph1dXVsbGxS5cuZafyFfM0LS2tp4pJSUlhi3FxcSkpKemp0xJhoBgVuICAAAD//ve/FRvv3r0LwNTUlKuq2hUXF6erqwvA29u7urq6I1+pra1l81RxAbytrW1AQEBiYqJcLu9yMbGxsWwxPj4+NTU1XT4PESqKUYH78ccfAcybN69Fu6mpKYDcFk/cq4bmBZvskqPOfl0qlSYmJgYEBAwaNKg5T62trdk8lclknTrbnj172NVOXSuG9AUUowLHbhpvbm7eot3T0xPAzz//zElVzxAaGioSiQAEBgZ2pwvJKOSphYVFc56amJgsXbo0Nja24cmnEtoUEhLSXEx3KiHCRjEqcHK5nB3UazErEhwcDGDdunVcFdZauws2u0wmk6WlpQUFBQ0fPrw5Tw0NDdk8ra+vb/0VqVT67rvvssVs27atB4shwkMxKnzs5sHR0dGKjfHx8QDc3Ny4qqqlurpvVq8GoK2tfeDAgd67TmZmZlBQ0MiRI5vzdODAgRKJJDIysqqq6nEtdYsWLWJXj6pgh52oGopR4QsKCgLw4YcfKjaWlpaKRCIdHZ2O3Nv2uooKZtq0ek1NyeTJp0+fVs41r169GhQUNHr06OY87dev3yuvvBIZGTl9+nQ2Xs+cOaOcYgivUYwK39GjRwFMnjy5RTt7h5uens5JVX8pKmJefJEBGDMzhotisrOzQ0NDXV1d2WFQ9kaeFtiTjqOtSYTP2dlZJBKlpaVJpVK2hX3nO7uk9NKlS1wWl52NSZOQno5hw5CYiDFjlF+CjY3N6tWrk5KScnJyvvrqKyMjI5lM9sUXXyh2VAl5BiXtH044ZGhoOGzYsDt37ly7dk0qlf70008HDx4cNWrU5MmTMzIyHBwcOKssLQ2ennj4EE5O+PVXmJhwVgkAwNraes2aNffu3QsLC8vNzeW2GMIj6rTHV19QVFT03HPPaWhoLFmy5PTp05WVlbdu3UpISNi1a1dKSkp9fb2VlRW7wlx5TpyApyfKyjBzJo4ehYGBUq/+dGVlZQcPHuzfvz+7Rwkh7eN6VIEoSfPe7K+//npOTk5ERISXl1fzNprq6uqurq6hoaEFBQXKqObHHxmxmAGY119nVGGOS8GdO3cAmJmZcV0I4Q2K0T4hLCyM3WIuICBA8TGekpKSp22YdPv27d6qJjSUUVNjACYggOnkM0VK0LzStsVmKIQ8DcWowMnlcnbBk0gkCgkJedphpaWle/bs8fb2bn6bkEgkcnJyOrt1K9ODeSqXM4GBDMCIRMzmzT122p42Z84cAFFRUVwXQviBYlTIGhsb33zzTQAaGho7d+7syFdabJj0wMmJARh7eyYwkOnmLsWNjYy/PwMwmprM3r3dOlUvYycMPvjgA64LIfxAMSpYf/75J9ur0tPTO3r0aGe/XlNTc/DgQekbbzADBzJA05/nn2f+8Q+mIxvQVVczOTnM4+eCGIZhGhoYDw+mXz/m2LHOFqNkx44dAzBp0iSuCyH8IGIYRnnzWURZysrKvL29k5OTjYyM4uLi2O3uu0gmw/nziI5GVBTu329qtLLCvHnw9sbUqXjydW+4fBnr1iExETIZRCI4O2PzZkyaBADV1cjOhsqvxywrKzM2NtbR0amsrGzxMjtC2sB1jpOel5OT8/zzzwMYOnRoVlZWj51XKmUSE5mAAGbw4L/6p8bGzNKlTGxs04R7ZibTrx/j6ckkJzMPHzKpqcyiRYymZncHBJROVR7xInxAMSo0GRkZlpaWABwcHPLz83vlGjIZc+4cs3YtM3ToX3nK7nLi6ck4ODyxhkkmY1xdGSenXqmk17z22msAIiIiuC6E8AA9DCoop0+fdnNzy8/PnzZtWlJS0uDBg3vlMmpqmDgRX36JnBxkZiIoCHZ2mD4ddXVISMCrr0IsfuLgZctw8SKKinqlmN7BPinLvhOQkGejcR/hOHz48JIlS+rq6ubPn79v377mpUu9a9QojBqFDRvQ2IjcXDQ0wMam5THDhgFAdjbMzZVRUk+YMGECKEZJx1BvVCC2bt26cOHCurq6VatWNT+wpFRiMeRyAHi8kv8vWloA8HhjFF4YM2aMlpbWjRs3Hj16xHUtRNVRjArBpk2bVq1axTBMUFDQN998wz6wxAEzM4hE+OOPlu337gGApaXyK+oyLS0tR0dHuVzO8Q5YhA8oRvlNJpOtWLFi/fr16urqERERHG80o6+PcePw228t2+PjMXQobG25qKnraHiUdBDFKI/V19cvXrx4+/bturq6hw8ffuutt7iuCPj4Yxw7hrAwNK9H/v577N2LTz7B402R+YIdHk1NTeW6EKLqaPk9X5WXl8+dOzcpKcnAwCAuLs7V1ZXrih7bsgX/+Ad0dfH887h7F2Vl+Ne/8M9/8i5Gb9++PWLECHNz88LCQq5rISqNYpSXCgsLPTw8rl69amFhER8fr3L7tJeU4PRpFBfDwABTp8LMjOuCuoJhGBMTk9LS0j/++MOSVwO7RMlowRP/XL9+3cPDIy8vz97ePj4+fsiQIVxX1IqxMXx9uS6iu9g9ruLj4y9cuEAxSp6BxkZ55sKFC1OmTMnLy3NxcTl79qwqZqiA0PAo6QiKUT6Ji4ubPn16SUmJj4/PyZMnjYyMuK5I4GiynnQEjY3yxp49e9566y2pVLp8+fLt27fTzkNKUFpaamJioqurW1FRQf/BydNQb5QfNm3a5O/vL5VKAwMDd+3aRf+klcPIyMjW1ra6uvr69etc10JUF8WoqpPJZH//+9/Xr1+vpqa2bdu2kJAQrivqW2h4lLSLYlTVbdmy5b///a+2tnZ0dPTKlSu5LqfPoeFR0i6KUVX3zjvv+Pj4HD9+fP78+VzX0hdRjJJ20RQTIc9SX1+vr6/f2NhYUVHBvuaPkBaoN0rIs2hpaY0ePVoul1++fJnrWoiKohglpB10X0+ejWKUkHbQZD15NopRQtrR3ButqakZP378//3f//3+++9cF0VUCE0xEdIOhmGMjIzKy8u/++675k1dhw0btnDhwgULFkyYMEHEtz0ASc+i3igh7WC3egJgYGCQmJgYEBBgbm5+9+7dzZs3u7i4WFlZrVixIi4uTsqrl02RHkQxSkj7xo0bByAtLc3NzS0sLCw/Pz8xMTEwMPC5557Lz8/fvn373LlzzczM/Pz84uLiGhoauK6XKBXd1BPSjoqKiunTp1dUVERGRk6aNEnxI4ZhLl68GBMTExMTc/fuXbbRwMBg7ty5/1yyZPiUKVD+K1qJ0lGMEvIsBQUFHh4eGRkZlpaWZ8+etbGxedqR165di46OPnLkCPsy0T/t7PTu3cP06ZBIMH8+aOm+cFGMEvJUN2/enD17dl5enp2dXXx8vJWVVUe+lZWVdfyXX96LikLzy5l1dODujgUL4O0Nff1erJhwgWKUkLalpqZ6eXkVFxc7OzuvW7du/fr1Xl5eEonE1dW1o1PzeXk4dAjR0Th/HnI5AKirw8UFEgkWLeLpK6pIaxSjhLQhISFh4cKFVVVV3t7e+/fv//jjj8PCwtiPbG1t2aVOzs7OHc3T4mIcO4boaPz2GxobAYU89fXF4MG99nsQZaAYJaSl77///m9/+1tjY+OyZct27NihoaEhk8nOnz8fHR194MCB5vctm5iYuLu7SyQSd3d3sVjcoVMXF+PwYRw8iJMnwU7oq6lh1So8zmjCRxSjhDwhLCxszZo1DMMEBgZu3LixRX9TLpcnJycfOXIkJibmzp07bKORkdGcOXMkEsns2bM1NTU7dJmKCiQkIC4Ohw5h0ya8+y4YBqdOITkZ1dUYMgReXujYUCzhHkMIYRiGYeRy+QcffABAJBJ9+eWX7R6cmprKLh1t/tdkYGDg5+dX8euvTG1tR69aVcVUVTHl5czUqYyWFjN5MiORMMOHM5qaTFhYd38lohQUo4QwDMPU19cvXrwYgKam5v79+zv13czMzJCQEFdXVwDDDQ0ZDQ1GR4fx8mIiI5nKyg6dYvFixtycycpq+lEuZzZuZEQi5uTJTv4ehAN0U08I8OhRUUDAiJ9+UtfROXz48NSpU7t2mlu3bhUdPz4lMhJpaU1N2tqYPbtpqZOBQdtfy8+HtTXCw/H4gX0AYBiMHYshQxAb27ViiNJQjJI+7/59zJmD9PRsD4+qjRsdHR174JzsUqcjR3D6NNhn7Zun5l95BebmTxx84AAkEmRmYtSoJ9rfew8//YSSkh6oh/Qmeqae9G3Z2Zg8GenpsLW13bKlZzIUgJUVVq9GQgKKihAZCS8vqKnh3Dm8/z4sLeHmhrAw5Oc3HcwGZetlT5aWKCtrWnBKVBjFKOnD0tLw0ku4fRtOTjh/HgqTRT3G2Bh+foiLQ2EhduzAnDnQ0GjKUysr7NoFADo6AFBZ2fK7FRXQ1oYa/SNVdfR/iPRVJ05gxgw8fIiZM3HiBExNe/dyxsZ48038+ivKyxEbi6VLoaeHiRMBwM4OAG7davmVrCzY2/duVaQn0Ngo6ZP27sXy5WhsxOuvY9cudHDxfM+qrW3qh8rleO45ODri4EE0L1O9excODvj0U3z4IQe1kc6gGCV9T1gY1q6FXI6AAHz9tUrcNSckwMsL8+bBzw/m5khPx2efwdgYSUm01Z7qoxglfQnDIDgYwcEQiRASgo8+4rogBSkp+PRTJCXhzz9hbQ2JBJ98ggEDuC6LtI9ilPQZUilWrMCuXdDUxO7dePVVrgsiAkExSvoMHx/ExqJ/f8TE4OWXua6GCIcG1wUQ0hklJbh8GVOmQEvrr8bMTNTWwsmp6cf6epw7hz/+QL9+mDABQ4Y0tfv7IyUFv/wCFxdll00EjXqjhFeOHoWnJ/Lzn1isvmwZ7t5FUhIA/PYb/P1RUoIRI/DgAUpL4e+Pb79tit3qaujpcVM5ES4VmKMkpKfcuIF58+DujvJyZGTg4UMcO4aff8batU0HUIaSXkAxSgTkiy9gZobwcOjqNrXMmoV//QvffYf79zmtjAgZxSgRkFOnMG1ay7X0c+agsbHplp+QXkBTTISHxo9/Ys18RQVefBEAiora2DHe2rrpI0J6B8Uo4aHQUBgZ/fXj5s2oqQEAsRh1dS0PZls6+G4PQjqPYpTwkJvbEzP1P/yAu3cBwNYWOTktD2Y/GjZMWcWRPofGRomAeHsjPh7FxU80fv89jIzg6spRTUT4KEaJgKxdi4ED4emJpCTU1iInB8HBCA9HSEjTXkqE9AK6qScCYmiIs2fx3nuYNq3p1R3W1tizB0uXcl0ZETJ6ionwCsOgsbHlfJFMBoaBhkKfoKoK+fno3x+WlkoukPRBFKOEENItNDZKCCHdQjFKCCHdQjFKCCHdQjFKCCHdQjFKCCHd8v8HOARiqy6dYQAAARZ6VFh0cmRraXRQS0wgcmRraXQgMjAyMi4wMy40AAB4nHu/b+09BiDgZYAARigbhBsYORg0gDQzExuEZmFjSACpYYaLO0DFHTJANDMjXgbMDAgNM4ODAayAkZGbgVGDiZEJaLwCM4sCC2sGEytbAht7BhM7RwIHZwYTJ5cCF7cGExePAidLgggjGwsnBzsbq/gyqKvBgDfhhMOB++um24M47dt1DhzxnbAPxM77U3zgwdYl+0HsCYcX7V+cZgdmi5xT3Fe2p9UOxD6f+3FfruFdsN7NMSr2717qOYDYb3JYHP5FCoLZR2olHCIe+4LV7ClcZC/Z5gs2P3fyKXvh4qtgM8vmOzhUeXIcALEb1D7YHeTSBrPFAELlQyENXB9CAAABdnpUWHRNT0wgcmRraXQgMjAyMi4wMy40AAB4nH2T3U7DMAyF7/sUfoFF/kviXLJ1QgitlWDwDtzz/sLuNNKJiKyukvSrU5/jTRDjbX79+obfwfM0AeA/V2sNPgURpwvEBI7n55cFTten433ntH4s13cgiQvj98g+XdfLfYdghYMkJOZYJbKMrIAJt9FfZTjBgVNpVpHhgImNTGVASpCSzFOak+RksQD+kBqH+/NKuUnkrCo1j07PkRMTZcOoKXEhxtHh5Qaqp1TyeiobVhqA1UFMRT1NA05VBNkGnDnHCUsjNZ/4txKNuLZxYlxq8ceGynlUM7k//l1cGray6YhYsY5I2sjsKd0R16mUmutIHWIX0k3E5mWES8RNLY9IcdLl8dNVNz8rZxnpc17mh0a5tc5xXebeOhzRGyQ2pHdBLLVbTR65++kLKN019ajdG/WwboF6tK60etBeUdputFOO40a8U4i2HdkpofFW3he8Ly/W93+Vz6cfL6yvwg/SSAAAAAC9elRYdFNNSUxFUyByZGtpdCAyMDIyLjAzLjQAAHicJY7LDQJRCEVbcakJQ/jDy8TVFGARtmHx3qfsOBzgXtf9+Xq83vrepf/u9rkfzqNmQ4eyTQ2dh3GtaToEQCccyFkgOSnrpBgI7NZc2+rwzgAT1hxRJ2yWmviPBbxQrLaNNJ3CFZgtMm73LRlLLU0A3FTbwMeQRXgkLOnENxi/SCK9rygnFNuxqzpBkFFWOyG/2orZn6KWRGxUben0+HwBo5c17GA0yJAAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m = Chem.MolFromSmiles(\"O=C(C)Oc1ccccc1C(=O)O\")\n", + "m" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e7b4d603", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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YpydLSmLFxdYUPH369ODBg/k3SkhIuHbtWr036PX6TZs2Wa60ZmZmjho1in+kIcsI9Vj+vJaQkFBQUGBN/8FZIEZdXWVlZdu2bc17MJ999tl9+/bZqpjRyFQq1qYN272bZWezyEj2zTds5042ZgxLSWFZWUwkYmIxk0rZ5cuCFDSZTCqViv/43rZt25SUlEeNK8+ePWvejeDn56dUKmtqappWMTU1lY/xvb29lUrlE0ey4OwQo67LZDKlpaXx5b/WrVt7enpu3LjRHrsg+dLhSy+xnTvv/aWqinXtyvR6tnw5y8kRvODNmzfNtywZMmTI2bNnLc9evXrVvBvB29tbLpdXNH4ZoZ6ioqLExEReMTo6Oisry8oGoTlDjLqonTt3mi86CgkJ4Rt31q9fb78e9OnDLK87GjiQ2Xj3ZVpaWrdu3fjGA7lcXltbW1hYmJSUxBc0PTw8ZDJZUVGRgBW3b9/Ot5e6u7snJSXduHHDck/rkSNHBKwFDoQYdTmZmZnm9buuXbumpKRotdovv/ySiIKCghq13dIqUVHs+vX7L59+mllcLGQjlZWVM2fOFIvFROTv788D1N3d/bXXXntw5VQQd+7ceeutt8RisUgkWr58ueVlYwEBAbaoCPaHGHUhfPmPB2i95T+DwRAeHk5En332mZ168+abbOXKe8d5eSwsjNnrqsqMjIzevXsHBASIRCKpVNrkq54a7siRIwsWLNizZw9itEVCjLqEhiz/8YffBQQENHyHplVu3WJDh7Lp05lczqKi2IED9ij6q61bt/LrOO1ZdM+ePQMHDvz0V507d7ZndbAdd4IWrbCwcP78+evWreMPkX/ttdc++OADf3//B985ceLEQYMGHT169JNPPpHL5TbvWefOdOAA5eSQRkP/939k33t36nQ6IuIXj9qTp6en/YuCreF+oy1WWVlZcnJynz591qxZQ0SJiYm5ubkqleqhGcrxZ94plcqKigp7dNHNjSIiaNAgO2coOe729T169Pjjr2x9t2mwG8So0zt9+rTBYODH169fLykpqa6uXrx4cUhIyOLFi7VarVQqPXfu3Pr164ODgx/f1O9+97tRo0ZVVlYuX77c9h13JNy+HgSEGHV6iYmJ5eXl/Hj16tWpqalBQUHJyclVVVUJCQknTpxQq9V9+vRpYGsffvihSCT6z3/+c+vWLZt12fEccvv6sLCwV1991fzS+sdhQTOBGG1p/Pz8IiMjhwwZsn///u3bt5vv0dlAAwcOHD9+vEaj+fDDD23Uw+aAP1ipdevW9izapUsX81YzIpo6dao9q4Pt4CemlmDDhg38hkwnTpwICQn53//+xy9/bJqFCxd+//33q1atmjVr1hPXAZwUHjQPAsJotCXw9vb28fHx8fHh9zSyJkOJKCIiYsqUKXq9nj/UvkXCE5JBQIjRlmDixIkvv/zyyy+/3K9fP0EaXLBggYeHR2pqak5OjiANNjeIURAQYhQeIjg4+O9//7vRaFQoFI7ui03gl3oQEGLU6c2ZM4cvjBLR+PHjn332WUGa5c+/3LJly7FjxwRpsFnBg+ZBQIhRpzdlyhTzL86DBw/mT7OwXkBAAH8O+9y5cwVpsFnBpB4EhBiFR0pOTm7Tps3du3f52K0lQYyCgLDhCR6pQ4cOhw4dKi4uNq8hbt++/fnnn5dIJI7tmPWwNgoCwmgUHqdVq1b8Qnvu3Xff5RvXnR3WRkFAiFFwRZjUg4AwqYcnuHr1qvm+eaWlpY7tjFAwqQcBIUbhCTp27Dhx4kR+vHHjRsd2RigYjYKAEKPwBL6+voMGDeLHrVq1cmxnBKHT6fhNrN3d8e8fBIC1UXA51dXVhKEoCAf/N4bH6dq169KlS80vv/jiC6deT8zJyVEoFB07diTEKAhHxBhzdB8AbO7q1atz5879+uuvjUZju3btKisre/funZeX5+h+QUuAST20cCUlJcnJyWFhYevXrxeLxTKZbNu2bUFBQaWlpYcPH3Z076AlQIxCi1VeXj537lz+TKq6ujqpVJqTk6NSqeLi4v7yl79UVFQMGzbsH//4R1VVlaN7Cs4NMQotkUZTu2xZaO/e8+bNq66unjhx4pkzZ9RqdUhICBHpdLq2bdsOHTpUIpGsXLmyb9++69evd3SPwZkJ/eB7AIfS6ZhKxQICGNGqoUOHDh36888/m0/W1dWtXbu2e/fuRCQWi3ft2mV+OFJCQkJBQYEDOw7OCzEKLYXBwNatYz16MCJGxAYP1u/fbz5pMpnS0tIiIyN5aIaHh6vVapPJZDKZUlNT/fz8iMjLy0upVBoMBgd+CXBGiFFwfiYTS0tjkZH3AjQ8nKnVzGQyn09PT4+JieEB2qNHD5VKVS8rb9y4MWnSJCLq6OWleeEFduqU3b8DODHEKDi5AwdYTMy9AA0JYV99xYzG+2cPHXoxPp4HaNeuXVUqlV6vf1RL27Zty37xRUbEJBKWnMxqauzRf3B+iFFwct98w4hY585MqWRa7f2/nz3LpFJGtGP48A4dOiiVypqGxKJGw+Ry5ubGiFjPnmzXLtt1HFoMbL8HJ3TqFP3wA5lMNGYMxcTQunX0yiv065NUKC+P3n+f1GpijHx9tcnJ+jffbNxDp0+cIJmMsrKIiKRS+uwz6tRJ+G8BLQU2PIGz+f57mjGD+venZ56ht9+mjRtp2rR7GVpYSDNmUL9+tGkTSSQkk9HFi63ee69xGUpEAwZQZialpJCPD23eTKGhtGYNYcABj4DRKDibmBj6+mvq25eIqLCQRo6kixeprIyWLqUVK6i2ltzd6c9/pnnzKDjY2lqXL9Mbb1B6Onl6Um4uaTSUnExVVSQW0z//SRMmWP9toAVAjIKz6dCBysruvwwMpIsXKTubhg0jkYgmT6YFC6hPHyErbthA5eU0bRoNGECbN1NUFJWWUnw8rVtH0dFCFgLnhBgFZ+PvTzdukPlWoZ07U1ERubnRvHk0YQJFRdmq7o4d9O239NVX915+8QVduEAWt78Cl4W1UXA2zz1H339/7/innygyktzciIgUChtmKBEVF1NAwP2XAQFUXGzDcuA8cL9RcDbLltGf/kQ//EBubnTs2P3hoa0FBtLu3fdfFhRQt252Kg3NGyb14IQMBsrLI6OR+vYlicRORfV6ioqizz+n556jwkIaO5a2bqXQUDtVh2YMMQrQYPn5lJxMRUXk5UVyOY0Y4egOQbOAGAVojKwsunqVoqIE3gwAzgw/MQE0xpdf0h//+JtFUnB5iFGAxqipISLC4/DAAmIUoDEQo/AAxChAYyBG4QGIUYDG4DHq7e3ofkAzghgFaAyNhgijUfgNxChAY2BSDw9AjAI0woSOHZ+NiLiJST1YwDX1AI1wPDf35s2bGI2CJYxGARpBo9EQkRdiFCwgRgEaoaamhhCj8FuIUYCGqqurq6urc3d39/DwcHRfoBlBjAI0FB+KeuP3JfgtxChAQ2FhFB4Kv9QDNJRIJBo1apSPj4+jOwLNC+43CtAgK1eunDlzJj/+8ccfQ0NDg61/gDO0CJjUAzTIggULzMebN2/Ozc11YGegWUGMAgBYBWujAA3CGEtMTOTHmZmZkydPdmx/oPlAjAI0iEgkWrRoET+ePXu2YzsDzQpiFKChuv36YHpsHQVLWBsFALAKNjwBNMj58+fDw8P5cWFhYdu2bX19fR3bJWgmEKMAAFbBpB4AwCqIUQAAqyBGAQCsghgFALAKYhQAwCr/D/NFQe8MoEg4AAABg3pUWHRyZGtpdFBLTCByZGtpdCAyMDIyLjAzLjQAAHice79v7T0GIOBlgABGIBaF4gZGDgUNIM3MxAahWdgUEoA0CzNcPAEqnpABphnxMmBmQGiYGRwKYAVMjIwMGmAXkEtzMzBqMDEyMTAyKzCzKLCwZjCxsiWwsWcwsXMkcHBmMHFyKXBxazBx8ShwsiQw8TIw8TEw8TOwCjCwCTKwCzFwCDPwiDCIMLKxcHKws7GKszFCQgMMRDseyh7YuOmxPYizeZfigVMZ/XYg9rydZQf2zru6D8R+oblw/+UK1v0g9j27XttD2iZgNWpGLHbfYq+B9Uq37LJ/c17cAcQ+m67nYMN5Eiy+sE/L4QH3ZFsQO1d+tb2HkgLYHLHcxfYXv7AfALF/iQftPTTVBMz2vqPkIO9gDWa73ll6YOLKPrB6j7UTD/jqZILNXCvhdaA19T5Y3OroxP2eVXJgewvNDtlr7ykHs2M2Nzr0qwqC2Ylbqh32nLYCq5dnlnfQn9YLNl8MACz2YCIcp5uKAAACDXpUWHRNT0wgcmRraXQgMjAyMi4wMy40AAB4nH2UXY5bIQyF37MKNhCEjY3N4yQZTapqEqlNu4e+d//qMVUGRkW9N9xcnA/wz3EOKa5vl6+/fqePiy+HQ0rlP5/ee/pZSymH9xQv6fT69uWWzo+X09Nyvv+4Pb4npviUuD+zL4/7+9NC6Z6OnKU175hlM/eOVbmMay7ldA5Q2QrDnNlL6W0D1gBrdtViPR1LFjJm25ASZ1Nm7QVHglSmarohFXvCCh+b44VMu/iGa4MjxoaOYJqJCW84A0dZ1Ehb4lyNS9vF4uA4G2txxQK1goM3XB9ca9J1uG/k8fM/HJVxcK2IxCLixqpcdyQNkq3hjsQTS6VdFokjjbAr4uZAnanJ9vgKFDUUaV2D7KzmfUdKuqajZvJK1aNKRIKK7lANVDICkRLJ7lS87rJOLciaSc2Qd2xqouy7xCPS6zi1OjyAz2KNaZd6cpAoDUSGnaA7K962fnaAgjRVEsGO3KxvEwp5X7FRr+wukVnj3mzbEwQSvrko5IMcCIv6rpyvt8untvvbiKf77TIbkWPMdgtDnU0VU5mdQxg62wOT1GYXCIZNsQuGT00LRp/SFQxaJUrjQYsUOR7Ei+RoWOqiLIlVugiIwmtZZDIMuqhhGNpSc4LfZEtpCZ6TLyUk+E59KRXBe14rEm7he838mueYP/8s8X74A6dTBpwPEFdkAAABSnpUWHRTTUlMRVMgcmRraXQgMjAyMi4wMy40AAB4nG2QvW4CQQyEXyUlSHvW2uu/5ZSKhlSkRxQRFVIiUELJw2f2UoFS3J48+3k83sPuuN+uXvfrE59Wh91x/c/Jf8R2qZYD38t9JaSpFmVSUlHLMguZKgShLha9zEwSHovCok3KPFWqxtJFh5jCrgNrzXvBnYuZtOHkrt0L6GA4N+pNMgcS0j14ICFWe2GyqIZhCufGWoTEo8uwRTx2CC2kxhCM0V4aZdT0MlfE6rUnTDw0EGVi4pbZ0KThwoOpnhg5wnBY11woMTSOPCbcwqBhe4kqwCRrRfghqaMZ9hGJ/zxhtFldFlFkEaSajDhH8DGb1YaXEt5BYdW55vJujdgCIQCFGpZYl4/b5ev9+3LdVDr/vH1dP8+n8414449lPpb8RLM81c98f6zlsf/+C9aqiewIkV/zAAAAAElFTkSuQmCC\n", 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m2" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1209acc4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rotatable_bonds = Chem.rdMolDescriptors.CalcNumRotatableBonds(m2)\n", + "rotatable_bonds" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "07d0df61", + "metadata": {}, + "outputs": [], + "source": [ + "# Setup the parameters for the embedding\n", + "params = getattr(rdDistGeom, \"ETDG\")()\n", + "params.randomSeed = 0\n", + "params.enforceChirality = True\n", + "params.useRandomCoords = True\n", + "params.numThreads = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c7c224c9", + "metadata": {}, + "outputs": [], + "source": [ + "# EMbed conformers\n", + "confs = rdDistGeom.EmbedMultipleConfs(m2, numConfs=50, params=params)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8453b480", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "50" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(confs)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "642925b0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0,\n", + " 1,\n", + " 2,\n", + " 3,\n", + " 4,\n", + " 5,\n", + " 6,\n", + " 7,\n", + " 8,\n", + " 9,\n", + " 10,\n", + " 11,\n", + " 12,\n", + " 13,\n", + " 14,\n", + " 15,\n", + " 16,\n", + " 17,\n", + " 18,\n", + " 19,\n", + " 20,\n", + " 21,\n", + " 22,\n", + " 23,\n", + " 24,\n", + " 25,\n", + " 26,\n", + " 27,\n", + " 28,\n", + " 29,\n", + " 30,\n", + " 31,\n", + " 32,\n", + " 33,\n", + " 34,\n", + " 35,\n", + " 36,\n", + " 37,\n", + " 38,\n", + " 39,\n", + " 40,\n", + " 41,\n", + " 42,\n", + " 43,\n", + " 44,\n", + " 45,\n", + " 46,\n", + " 47,\n", + " 48,\n", + " 49]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Minimize energy\n", + "energy_iterations = 200\n", + "results = rdForceFieldHelpers.UFFOptimizeMoleculeConfs(m2, maxIters=energy_iterations)\n", + "energies = [energy for _, energy in results]\n", + "energies = []\n", + "for conf in m2.GetConformers():\n", + " ff = rdForceFieldHelpers.UFFGetMoleculeForceField(m2, confId=conf.GetId())\n", + " energies.append(ff.CalcEnergy())\n", + "energies = np.array(energies)\n", + "# Add the energy as a property to each conformers\n", + "[conf.SetDoubleProp(\"rdkit_uff_energy\", energy) for energy, conf in zip(energies, m2.GetConformers())]\n", + "\n", + "# Now we reorder conformers according to their energies,\n", + "# so the lowest energies conformers are first.\n", + "mol_clone = copy.deepcopy(m2)\n", + "ordered_conformers = [\n", + " conf for _, conf in sorted(zip(energies, mol_clone.GetConformers()), key=lambda x: x[0])\n", + "]\n", + "m2.RemoveAllConformers()\n", + "[m2.AddConformer(conf, assignId=True) for conf in ordered_conformers]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "74756772", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Align conformers to each others\n", + "rdMolAlign.AlignMolConformers(m2)\n", + "m2" + ] + }, + { + "cell_type": "markdown", + "id": "4a5e643a", + "metadata": {}, + "source": [ + "As a beginner, this can be a bit overwhelming and complicated to get the hang of. Let’s see how this would look in Datamol\n", + "\n", + "## Datamol Example" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "aa5b82da", + "metadata": {}, + "outputs": [], + "source": [ + "import datamol as dm" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "9f55f956", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "smiles = \"O=C(C)Oc1ccccc1C(=O)O\"\n", + "mol = dm.to_mol(smiles)\n", + "mol" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "c03e90f5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# generate conformers\n", + "mol = dm.conformers.generate(mol, align_conformers=True)\n", + "mol" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "912fdfe2", + "metadata": {}, + "outputs": [], + "source": [ + "# Get all conformers as a list\n", + "conformers = mol.GetConformers()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "a1b296b0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "50" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(conformers)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "81fb899c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 2.03936006, -1.45912965, -0.53172218],\n", + " [ 1.9925788 , -1.56446348, 0.71921383],\n", + " [ 3.17465024, -2.13438585, 1.41003403],\n", + " [ 0.83770987, -1.13908313, 1.35789793],\n", + " [-0.24737227, -0.60904676, 0.65495089],\n", + " [-0.31723469, 0.74706314, 0.41869327],\n", + " [-1.37632791, 1.30134851, -0.27275517],\n", + " [-2.40159499, 0.50563021, -0.74962797],\n", + " [-2.35444012, -0.85472732, -0.52586563],\n", + " [-1.27290921, -1.39633958, 0.17670332],\n", + " [-1.21424131, -2.82512313, 0.41699016],\n", + " [-0.25080752, -3.31946086, 1.04455633],\n", + " [-2.22014558, -3.68260765, -0.03978946]])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get the 3D atom positions of the first conformer\n", + "positions = mol.GetConformer(0).GetPositions()\n", + "positions" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "9864a4c2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'rdkit_UFF_energy': 35.39391759550579, 'rdkit_UFF_delta_energy': 0.0}\n" + ] + } + ], + "source": [ + "# If minimization has been enabled (default to True)\n", + "# you can access the computed energy.\n", + "conf = mol.GetConformer(0)\n", + "props = conf.GetPropsAsDict()\n", + "print(props)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "056e862c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[6.61254163e-08, 1.01515980e+00, 1.01196417e+00, ...,\n", + " 5.56023152e-02, 1.00244193e+00, 6.36410194e-02],\n", + " [1.01515980e+00, 4.67577303e-08, 3.61762165e-02, ...,\n", + " 1.02178695e+00, 5.29240969e-02, 1.01919051e+00],\n", + " [1.01196417e+00, 3.61762165e-02, 4.67577303e-08, ...,\n", + " 1.01924556e+00, 7.12401285e-02, 1.01739384e+00],\n", + " ...,\n", + " [5.56023152e-02, 1.02178695e+00, 1.01924556e+00, ...,\n", + " 0.00000000e+00, 1.00778322e+00, 3.87585886e-02],\n", + " [1.00244193e+00, 5.29240969e-02, 7.12401285e-02, ...,\n", + " 1.00778322e+00, 0.00000000e+00, 1.00438156e+00],\n", + " [6.36410194e-02, 1.01919051e+00, 1.01739384e+00, ...,\n", + " 3.87585886e-02, 1.00438156e+00, 0.00000000e+00]])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Compute RMSD\n", + "rmsd = dm.conformers.rmsd(mol)\n", + "rmsd" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "5b1878d2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(50, 50)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rmsd.shape" + ] + }, + { + "cell_type": "markdown", + "id": "1a2d80ef", + "metadata": {}, + "source": [ + "**In essentially one line of code, you can generate a list of conformers.** What’s important to understand are some of the key parameters that are factored into this process. In general, sticking with the defaults in Datamol will suffice in most cases, but if you want to make specific modifications, you can. If you’re interested in learning more about all the algorithms underlying conformer generation, read [this](https://pubs.acs.org/doi/10.1021/acs.jcim.7b00221). \n", + "\n", + " A few parameters to highlight: \n", + "\n", + "- **n_confs** - Specifying the number of conformers to generate. This is based on the number of rotatable bonds and, by default, this is set to 200 if there are more than 8 rotatable bonds and 50 if there are less than 8. Theoretically, there are an unlimited number of conformers that can be derived from a single rotatable bond, however, in practice, not all the conformer structures make sense since only “stable” conformers are relevant in this context. This is why the defaults are set in place. Hypothetically, if you only have 2 rotatable bonds and you set n_confs to 2,000,000, not only will this be computationally expensive but a lot of the conformers generated will start to have non-relevant structures that are not useful.\n", + "- **add_hs** - By default, hydrogen atoms are added before embedding because it is critical to generating high quality 3D conformations.\n", + "- **minimize_energy** - Minimizing energy releases the strain of the generated conformation to the closest local minima enabling you to find a more relevant conformation. In other words, ***finding the conformer that is most likely to exist***. There are multiple force fields that you can apply.\n", + "- **method -** Within the ETKDG method, there are various versions that can be selected to generate conformers.\n", + "- **energy_iterations -** This options allows you to specify how many iterations of conformer generation you want to go through if you have enabled energy minimization. In general, the more iterations you specify, the more accurate the conformers. However, there is a trade off between the number of iterations and computation speed. Running through 1000 iterations will be significantly more expensive computationally as opposed to 100 iterations.\n", + "- **rms_cutoff** is the max RMSD value for which two conformers are considered to be the same.\n", + "\n", + "The full table of parameters along with their definitions is shown below:" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python [conda env:datamol]", + "language": "python", + "name": "conda-env-datamol-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/tutorials/Descriptors.ipynb b/docs/tutorials/Descriptors.ipynb new file mode 100644 index 00000000..0f177e16 --- /dev/null +++ b/docs/tutorials/Descriptors.ipynb @@ -0,0 +1,1418 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "db5685a5", + "metadata": {}, + "source": [ + "# Descriptors\n", + "\n", + "## Molecular Descriptors\n", + "\n", + "> Molecular descriptors can be defined as mathematical representations of molecules’ properties that are generated by algorithms. The numerical values of molecular descriptors are used to quantitatively describe the physical and chemical information of the molecules. An example of molecular descriptors is the LogP which is a quantitative representation of the [lipophilicity](https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/lipophilicity) of the molecules, it is obtained by measuring the partitioning of the molecule between an aqueous phase and a lipophilic phase which consists usually of water/*n*-octanol. - [source](https://www.sciencedirect.com/topics/medicine-and-dentistry/molecular-descriptor)\n", + "> \n", + "\n", + "Molecular descriptors can generally classified in four ways:\n", + "\n", + "![Descriptors_1.png](./images/Descriptors_1.png)\n", + "\n", + "([source](https://chem.libretexts.org/Courses/Intercollegiate_Courses/Cheminformatics_OLCC_(2019)/05%3A_5._Quantitative_Structure_Property_Relationships/5.03%3A_Molecular_Descriptors))\n", + "\n", + "## Tutorial\n", + "\n", + "In this tutorial, we’ll show how descriptors can be useful as filters in the drug discovery process. This tutorial was inspired from the [TeachOpenCADD talktorial](https://projects.volkamerlab.org/teachopencadd/talktorials/T002_compound_adme.html?highlight=descriptors), we highly encourage you to read through the theory, understand ADME and why we care about it in the drug discovery process from the talktorial before diving into this tutorial. It provides the necessary background information to fully understand the purpose of this tutorial. \n", + "\n", + "The set of descriptors that will be focused on today are: \n", + "\n", + "- Molecular weight ≤ 500 Da\n", + "- Number of hydrogen bond acceptors (HBAs) ≤ 10\n", + "- Number of hydrogen bond donors (HBD) ≤ 5\n", + "- Calculated LogP (octanol-water coefficient) ≤ 5\n", + "\n", + "These descriptors and their limits are collectively known as **[Lipinkski’s rule of five (Ro5)](https://www.sciencedirect.com/science/article/abs/pii/S0169409X96004231)**, this is a method used to estimate a compounds bioavailability based solely on its chemical structure. If a molecule violates any of the rules listed above (i.e. a molecular weight of 700 Da), it’s probable that the compound will **exhibit poor absorption or permeation** and subsequently be removed from your list.\n", + "\n", + "This tutorial will show you a real-world scenario of \n", + "\n", + "- **Part 1:** Obtaining a virtual screening library from **[Enamine](https://enamine.net/compound-libraries/targeted-libraries/dna-library)**\n", + " - The DNA library is designed to identify novel active compounds against proteins which are essential for DNA stability. At 5530 compounds, this is one of Enamine’s smaller libraries. The same functions could easily be applied to some of the larger libraries using Datamol’s parallelize functions.\n", + " - _Note: for this tutorial, we are loading a truncated VS Enamine library._\n", + "- **Part 2:** Then calculate the relevant molecular properties for the Ro5 for the list\n", + "- **Part 3:** Investigate compliance with Ro5\n", + "- **Part 4:** And finally, revealing the statistics for the dataset of compounds using Ro5 as a filter. With this, we will be able to find the answer to our question; how many fulfill vs. violate Ro5?\n", + " - Subsequently, we can show different ways of displaying the data to make it more visually appealing using Matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c2136858", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", 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+ "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import datamol as dm\n", + "\n", + "# Part 1: Obtain a list of molecules and visualize\n", + "# Load sdf downloaded from Enamine with the flag as_df set to True\n", + "# This will automatically create a 'smiles' column from the sdf file\n", + "data = dm.read_sdf(\"./data/Enamine_DNA_Libary_5530cmpds_20200831_SMALL.sdf\", as_df=True)\n", + "\n", + "data[\"mol\"] = data[\"smiles\"].apply(dm.to_mol)\n", + "\n", + "mols = data[\"mol\"].tolist()\n", + "\n", + "dm.to_image(mols[:12], mol_size=(200, 150))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "396835e5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of aromatic atoms in the compound is 6\n" + ] + }, + { + "data": { + "image/png": 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VSU1NzXPPPQdgw4YNrLPovytXrijPHszIyGCdRQ1z5swBMGHCBN7JR48etWzZEgZzarXeoxpVSWxsLIDu3bvz7kghgnjjjTcATJo0iXUQNaSnp/v4+GRmZvJOzp07F8C4ceNESEVEQPfU83v8+LGzs3NBQcHJkyfHjx/POo5BePTokbOzc0lJyenTp0eNGsU6jpCuXbvWv39/5apS5cHVRNfRpYP8IiIiCgoKvL29qUNF06FDh9DQ0BUrVrz11ltLly6VSnXytpuBAwcqN7n+XXBwsFwuX7x4MXWo/mD9OKwDYmNjW7RokZSUxDqIYSkvL2/VqlXnzp1Z/y/SdK+88kpYWNjf/1CHDx8GnfOtd+hplN/FixfLy8v37dvn5eXFOosBiY+PLyoqkkgkCxYsMDIyYh1HbYWFhUeOHElMTJw+fbryxLzKysqwsDAAa9asUX7ERPQE6x7XAWptUpTJZLGxscOHD9fdfTias3Tp0pkzZ+bm5vJO6sem2yf2y0dGRgLo06dPbW0t22BEWFSjKlF9k2JFRYVyH87evXvFyaYrMjMzlXvAVLkvRD823T5xelNMTIyVlRWd861/qEZVUr9J8cCBA7zDhw4dAtChQwd6/+vvxo4dC2DevHm8k/q06faJs0QLCwtZJyLCoxpVleqbFOtvXQ4NDRUnm/Y7c+YMVL5LWZ823dafbB8VFcU6C9EUqlFVqbVJ8aefflLuw0lPTxchm5arqalxc3MDEBMTwzusf5tulRfb0T1LeoxqVA1qbVL08/MDMHHiRBGCabmYmBgATk5OvHvA9HXTLd36qd+oRtWj+ibF3Nxc5aIWVQ6q0GN5eXmtWrUC8L///Y93WF833dId9PqNalQ99af4nDlzhnc4Ojoaqh2bpsfmz58P4KWXXuKdLCgoUF4Dd/LkSRGCiWz58uUAPD09dXrtAWkQ1aja1q5dC8DNza2mpqbxSbUO8dVL169fNzIyMjY2vnXrFu+wv78/AG9vbxGCia+0tNTOzg7AwYMHWWchAqMaVVtVVZWTkxOAHTt28A6rdaWE/hk+fDiAxYsX804qr4EzNja+efOmCMGY2LdvH4BOnTqVlpayzkKERDXaFP/9738BtG7dOj8/n3dY9QvO9ExCQoLyv1JBQQHvsPIYp8DAQBGCsaJQKAYMGAAgIiKCdRYiJKrRJho5ciSAoKAg3smff/5ZefujKtft6o3KysquXbsC2LlzJ+/wsWPHVP+xpNOSk5MlEom5ufndu3dZZyGCoRptolu3bqn+IjQgIADAiBEjRAimJVavXq3uO8jbt28XJxtbM2bMAODr68s6CBEM1WjTLViwQMXPoB8/fqz8DPrEiRMiBGMuOzvbyspKxfUMUVFRyvUMvIWrH+r/45w9e5Z1FiIMqtGmq1+g88UXX/AOb926FYCjo6OerYhskOoPXPULyE6fPi1CMC2h+qM60QlUo82yZcsWFZeL19bWKg+djI6OFicbK2q9/Tdr1iwAPj4+IgTTHmq9cUy0H9Vos9RvXty4cSPvsHJvtT7tFv83tT6M1tEbQAWh1jIGouWoRpvr66+/VpajKmcXTZw4EYCfn58IwZjYv3+/iksjFQrFkCFDACxfvlycbNpG9UW1RMtRjQrglVdeAfDWW2/xTv7666/KkzQvXbokQjCRqbVRR/WDB/WVWlu8iDajGhVA/bnuP/74I+/wsmXLAAwcOFD/9larvm28/hjs/fv3i5NNO82bNw/A2LGvsA5CmoVqVBhLly5V8dKL+luGPvnkE3GyiUOtQ4wiIiIA9O3b18BvrMrLyxs0aJyLyzUVTr8i2otqVBglJSUdO3YEcPjwYd7hjz76SPkGYllZmQjZxKH6kZpZWVmqXxGo92JiOIBzcuIMYCGc3qIaFczu3bsBdO7cuby8vPFJuVyu/Dj7vffeEyebpiUlJal+wLuvry+AGTNmiBBM+8lknIsLB3Aq3AxAtBTVqGDkcnm/fv0ArFq1inf4hx9+kEgkFhYWv/32m+ajaZZa1w2dP39eIpFYWlrev39fhGw64cwZDuBsbDgV1noQbUQ1KqQLFy4oy/HevXu8w9OmTQMwZcoUEYJp1BOXXzZCLpd7eHgAWL16tTjZdMXYsRzAqXBrKtFGVKMCmzx5MoDp06fzTv7+++8tWrQA8N1334kQTEOeuIq9cTt37gRgb2/P+76HocnM5ExNOamUu3KFdRSiPqpRgdV/fnLu3Dne4cjISAB9+vSpra0VIZsmBAYGAvDy8uKdLC4uVn4Kl5CQIEIwnbN4MQdwgwdzercQTv9RjQrvvffeA+Du7s67mqeiokK5t3r37t3iZBPW7du3lUep3rhxg3d48eLFAAYPHqx/C2YFUVzMdezIARz9lNE5VKPCq6ioUK4t37t3L+/w4cOHAbRv376oqEiEbMIaPXo0gIULF/JOZmZmmpqaSqXSK/Sq9el27uQAzt6eo/c8dAvVqEYcOnRIudNRlXIcOnQogJCQEBGCCejkyZOqXzM1ZswYAPPnzxchmO6SyzkPDw7g3n+fdRSiDqpRjVAoFC+++CKA0NBQ3uGrV68aGRmZmpr+8ssvImQTRHV1tbOzM4DY2Fje4TNnzgCwsbFR5fQWA3fhAieRcBYWHK0H0yFUo5ry008/qX4K3Ny5cwGMGzdOhGCC2LBhAwBXV1eZTNb4pEwmc3FxARBD68tV4+vLARztTtAhVKMaNHv2bACvvvoq7+SjR49atmwJ4KuvvhIhWDPVpz116hTvcExMDAAnJydDOPZfEFlZnKUlJ5FwtFdWV1CNalD9DRmJiYm8wxs3bgTQo0cP3uc75ubMmQNg/PjxvJN5eXmtWrUC8D86e0MdEREcwPXtyxn2yS06g2pUs9atW6e8r423HNV6t5Gh+ndy09PTeYeVB8GNHDlShGD6pLyc69KFA7h9+1hHISqgGtWs+tuDt23bxjus1mffrKi+roCOJW6Ogwc5gOvQgTPUU611CdWoxh0/flxZjvn5+bzDypWY/v7+IgRrgvj4eNVXudIlGc2hUHBDhnAAFxbGOgrhQzUqhlGjRgFYtGgR72T9vqDr16+LEEwtFRUVDg4OAPbs2cM7TFe2Nd9PP3FSKWdqyhnejX86RgqieVu2bDE2Nt6xY8fNmzcbn3R1dfX397e3ty8qKhInm+ry8/O7dOni7u7+5ptvNj5ZVVWlvCslKiqqdevWoqTTQ337YuZMyGQIDWUdhTSOdY8bioCAAAAjRozgnSwtLeU9cY4hVd63ff/99wE8//zzunvkipbIzeVsbDiAO32adRTydBKO41g3uUEoLCx89tlnCwoKTpw4obxmWV89ePDAxcWlvLz87NmzyrdHSXOsW4fwcPTsiRs3YGzMOg1pCL2oF4mtre3KlSsBLFmypLq6mnUcDQoLCysvL/f19aUOFcSSJXB0xO3bOHqUdRTyFPQ0Kh65XO7u7n7z5s3169cr3zrUPykpKYMGDTIzM7t9+3a3bt1Yx9ETp04hLw8zZ0JKjz1aiWpUVN9+++1LL71kbW2dnp6uvGZZn3Ac5+npefny5YiIiNWrV7OOQ4hI6KebqLy9vSdMmFBaWqq8qF3PHDhw4PLly506dQqlj5aFFhWFyZMxeTJ+/LHhgfh4TJ6MoCAAqKrC1KmYOhXff//Ub3jiBKZOhZ+fRtIaHLafcBmgO3fumJmZSaXSS5cusc4ipNLSUjs7OwAHDx5knUUPjR7NARzA9e7NNbivODycAzhnZ47juJKSuuFG9pKuXcsBnJWVhvIaFnoaFVv37t2DgoIUCkVwcDCnR++oREVF5eTkeHp6vv7666yz6LPUVPzf/7EOQf6JapSBiIiIZ555Jjk5WXmDiB64e/fuli1bJBJJbGysRCJhHUdvtWsHACtXIjubdRTyN1SjDFhbWys/gVm2bFl5eTnrOAIICQmpqqp64403XnjhBdZZ9NmECXB3R2lp3XugREtQjbLh5+fXv3//7Oxs5TGjOi0pKen48eNWVlZRUVGss+g5qRTR0QDw2Wf48kvWacifqEbZkEqlcXFxEolkw4YN9+7dYx2n6eRyufLm5PDwcOVHTESjRo3ChAkAEBCAsjLWaQgAqlGGBg4cOHXq1MrKyrCwMNZZmm7Xrl2pqamOjo7KMiUi2LwZZmbIygLv2tyqKpSWNvyPTCZKVsNAe3RZWr9+/eeff56QkJCdna28bkS3KBSKCxcuANi4caO5uTnrOIaie3e88w7WrMHmzZg+Hc8//9RJf3/4+4uYzFBRjbJkb2+/adOmlJSUjz/+mHWWJhoyZMigQYMmTZrEOohhCQ/Hp5/i7l0EBOD8eTxtcYSx8VP3j8rlkMs1F9CwUI0y5u/v//LLL0+ZMoV1kCZq3759v379WKcwOBYW2L4dY8bg4kUcOoSZMxse27MHs2c3/EtRUVixQmP5DAzVKHuOjo6Ojo6sUxAdM3o0Jk7E559j2TLo9cmLOoA+YiJEV8XFwdISubnQ/VVzuo1qlBBd5eBQ98I8JgZZWazTGDCqUUJ0WEgIevRAZSX0ZV+xTqL3RgnRYaam2LkTXl6orW3ut8rNxe7duHIFEgnc3LBgAbp0ESKiAaCnUUJ027BhaP5Cj+vX0bs3tm2DjQ0sLREbC3d3ZGYKkc8AUI0SovO2bEHLls36Djt3ol07pKXh0CHEx+P0aRQWYtMmgfLpO7pEhBAdcO0aCgrQqRNcXRseuHkTjx7B0hKDBkEuR3IyADg7o337huezs3HvHoyN4elZ95XSUlhb/zXQpQvs7JCSIuAfQm9RjRJCGtC5Mzp1wqVLrHPoAnpRTwh5UmoqHjyAlxfrHDqCnkYJIf+gUODll3H1KjIy0KYN6zS6gBY8EUL+wnEIDsbZs4iPpw5VFdUoIaROdTUCArB3Lz78EK+9xjqN7qAaJYQAQHY2XnsNt24hIQG+vqzT6BR6b5QQgm+/xbRpUCiwdy+ee+6vr3ft+tQTS0k9qlFCCDp1Qk5OA18vKfnHYlLSIHpRTwjBqVOoqWng65aWokfRQfQ0SgghzUJvexBCSLNQjRJCSLNQjRJCSLNQjRJCSLNQjRJCSLP8P8/kZ9d2DV4fAAAAv3pUWHRyZGtpdFBLTCByZGtpdCAyMDIyLjAzLjQAAHice79v7T0GIOBlgABGIOaE4gZGdoYMIM3MyMTGoAFisHBAaCY2BwifzQGsgJkRk8EOUcIMF+BmYFRgZNJgYmRWYGbJYGJhTWBly2BiY09g58hg4mBOEGFkY+ZgZ2NlES+DOgQMOENDVR0eui3bD+KsXvXK3rIvWRXEhorbQ8RX2Vn2PV4MEb+6D6Y+NPTrfoRerQPMEeaqSOL2SOrBbDEAxI4w5lvd3WsAAAD3elRYdE1PTCByZGtpdCAyMDIyLjAzLjQAAHichVJbasQwDPz3KeYCa+RHnOhzkyyllHWgTXuH/u/9qZQlVULBlSywrclYGsVB7X1++37g1+LsHECNxcz4SkTk7tANxtvLa8W0Xsf9Zlo+6/oBFif1M/K6Lvf9JqAi+m5IYrgEH5kpDSBPm9mnEROC33DpkP6DS1iMsMGXhY98evJdGoSdACWfQymlb5ZYFBk8/4/sFRl9KRuy8fgg8hhlg5FPVTaAtzqfBvAcybjU2UaiHk149WT6Sg7ZZFTvTCw5oJgiWaK3trPEYM1lCbYWssax0GNZet7/Mdm7H/5fg8bDiVzuAAAAdnpUWHRTTUlMRVMgcmRraXQgMjAyMi4wMy40AAB4nPNz1rD110w2TE5Ozks2VKjRMNIztTA2NtbRNdQzsrQ0MNGxNtQDCRjrGOhYwyThcgZ6xsYwSV0DPRNDMzMzcyS9QJalIZqQkZ4ZWJUBkjRCFm4ITEizBgBegCInN0TnUwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate a specific descriptor for a compound\n", + "n_aromatic_atoms = dm.descriptors.n_aromatic_atoms(dm.copy_mol(mols[0]))\n", + "print(\"Number of aromatic atoms in the compound is\", n_aromatic_atoms)\n", + "\n", + "mols[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "41388855", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'mw': 210.009913052,\n", + " 'fsp3': 0.125,\n", + " 'n_lipinski_hba': 5,\n", + " 'n_lipinski_hbd': 1,\n", + " 'n_rings': 2,\n", + " 'n_hetero_atoms': 6,\n", + " 'n_heavy_atoms': 14,\n", + " 'n_rotatable_bonds': 1,\n", + " 'n_radical_electrons': 0,\n", + " 'tpsa': 71.66999999999999,\n", + " 'qed': 0.7539078378657419,\n", + " 'clogp': 0.7626199999999999,\n", + " 'sas': 2.5248498164613675,\n", + " 'n_aliphatic_carbocycles': 0,\n", + " 'n_aliphatic_heterocyles': 0,\n", + " 'n_aliphatic_rings': 0,\n", + " 'n_aromatic_carbocycles': 0,\n", + " 'n_aromatic_heterocyles': 2,\n", + " 'n_aromatic_rings': 2,\n", + " 'n_saturated_carbocycles': 0,\n", + " 'n_saturated_heterocyles': 0,\n", + " 'n_saturated_rings': 0}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Part 2: Calculate the relevant molecular properties for the Ro5 for the list\n", + "\n", + "# Calculate many descriptors for a compound\n", + "dm.descriptors.compute_many_descriptors(mols[150])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "108c4837", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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mwfsp3n_lipinski_hban_lipinski_hbdn_ringsn_hetero_atomsn_heavy_atomsn_rotatable_bondsn_radical_electronstpsa...sasn_aliphatic_carbocyclesn_aliphatic_heterocylesn_aliphatic_ringsn_aromatic_carbocyclesn_aromatic_heterocylesn_aromatic_ringsn_saturated_carbocyclesn_saturated_heterocylesn_saturated_rings
0122.0480130.000000321391055.98...1.690816000011000
1133.0639970.0000003323100054.70...2.795444000112000
2169.0406750.0000003324110054.70...2.381662000112000
3152.0044340.0000003124100045.75...2.591944000022000
4133.0639970.0000003323100054.70...2.232651000112000
..................................................................
876337.0925780.1764714127245055.40...1.761027000202000
877335.1092910.384615751102370113.68...2.716945000101000
878426.2168090.2400007157325065.77...2.215072011224011
879460.0873080.0869577059326082.52...2.379198000235000
880312.1637920.3157893134235032.34...1.851829011202000
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881 rows × 22 columns

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" + ], + "text/plain": [ + " mw fsp3 n_lipinski_hba n_lipinski_hbd n_rings \\\n", + "0 122.048013 0.000000 3 2 1 \n", + "1 133.063997 0.000000 3 3 2 \n", + "2 169.040675 0.000000 3 3 2 \n", + "3 152.004434 0.000000 3 1 2 \n", + "4 133.063997 0.000000 3 3 2 \n", + ".. ... ... ... ... ... \n", + "876 337.092578 0.176471 4 1 2 \n", + "877 335.109291 0.384615 7 5 1 \n", + "878 426.216809 0.240000 7 1 5 \n", + "879 460.087308 0.086957 7 0 5 \n", + "880 312.163792 0.315789 3 1 3 \n", + "\n", + " n_hetero_atoms n_heavy_atoms n_rotatable_bonds n_radical_electrons \\\n", + "0 3 9 1 0 \n", + "1 3 10 0 0 \n", + "2 4 11 0 0 \n", + "3 4 10 0 0 \n", + "4 3 10 0 0 \n", + ".. ... ... ... ... \n", + "876 7 24 5 0 \n", + "877 10 23 7 0 \n", + "878 7 32 5 0 \n", + "879 9 32 6 0 \n", + "880 4 23 5 0 \n", + "\n", + " tpsa ... sas n_aliphatic_carbocycles n_aliphatic_heterocyles \\\n", + "0 55.98 ... 1.690816 0 0 \n", + "1 54.70 ... 2.795444 0 0 \n", + "2 54.70 ... 2.381662 0 0 \n", + "3 45.75 ... 2.591944 0 0 \n", + "4 54.70 ... 2.232651 0 0 \n", + ".. ... ... ... ... ... \n", + "876 55.40 ... 1.761027 0 0 \n", + "877 113.68 ... 2.716945 0 0 \n", + "878 65.77 ... 2.215072 0 1 \n", + "879 82.52 ... 2.379198 0 0 \n", + "880 32.34 ... 1.851829 0 1 \n", + "\n", + " n_aliphatic_rings n_aromatic_carbocycles n_aromatic_heterocyles \\\n", + "0 0 0 1 \n", + "1 0 1 1 \n", + "2 0 1 1 \n", + "3 0 0 2 \n", + "4 0 1 1 \n", + ".. ... ... ... \n", + "876 0 2 0 \n", + "877 0 1 0 \n", + "878 1 2 2 \n", + "879 0 2 3 \n", + "880 1 2 0 \n", + "\n", + " n_aromatic_rings n_saturated_carbocycles n_saturated_heterocyles \\\n", + "0 1 0 0 \n", + "1 2 0 0 \n", + "2 2 0 0 \n", + "3 2 0 0 \n", + "4 2 0 0 \n", + ".. ... ... ... \n", + "876 2 0 0 \n", + "877 1 0 0 \n", + "878 4 0 1 \n", + "879 5 0 0 \n", + "880 2 0 0 \n", + "\n", + " n_saturated_rings \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + ".. ... \n", + "876 0 \n", + "877 0 \n", + "878 1 \n", + "879 0 \n", + "880 0 \n", + "\n", + "[881 rows x 22 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Batch compute many descriptors for a list of compounds\n", + "df = dm.descriptors.batch_compute_many_descriptors(mols)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "6733ca23", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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mwfsp3n_lipinski_hban_lipinski_hbdn_ringsn_hetero_atomsn_heavy_atomsn_rotatable_bondsn_radical_electronstpsa...sasn_aliphatic_carbocyclesn_aliphatic_heterocylesn_aliphatic_ringsn_aromatic_carbocyclesn_aromatic_heterocylesn_aromatic_ringsn_saturated_carbocyclesn_saturated_heterocylesn_saturated_rings
0122.0480130.000000321391055.98...1.690816000011000
1133.0639970.0000003323100054.70...2.795444000112000
2169.0406750.0000003324110054.70...2.381662000112000
3152.0044340.0000003124100045.75...2.591944000022000
4133.0639970.0000003323100054.70...2.232651000112000
..................................................................
875363.1153810.1578956047265069.63...2.148096000224000
876337.0925780.1764714127245055.40...1.761027000202000
877335.1092910.384615751102370113.68...2.716945000101000
878426.2168090.2400007157325065.77...2.215072011224011
880312.1637920.3157893134235032.34...1.851829011202000
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863 rows × 22 columns

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" + ], + "text/plain": [ + " mw fsp3 n_lipinski_hba n_lipinski_hbd n_rings \\\n", + "0 122.048013 0.000000 3 2 1 \n", + "1 133.063997 0.000000 3 3 2 \n", + "2 169.040675 0.000000 3 3 2 \n", + "3 152.004434 0.000000 3 1 2 \n", + "4 133.063997 0.000000 3 3 2 \n", + ".. ... ... ... ... ... \n", + "875 363.115381 0.157895 6 0 4 \n", + "876 337.092578 0.176471 4 1 2 \n", + "877 335.109291 0.384615 7 5 1 \n", + "878 426.216809 0.240000 7 1 5 \n", + "880 312.163792 0.315789 3 1 3 \n", + "\n", + " n_hetero_atoms n_heavy_atoms n_rotatable_bonds n_radical_electrons \\\n", + "0 3 9 1 0 \n", + "1 3 10 0 0 \n", + "2 4 11 0 0 \n", + "3 4 10 0 0 \n", + "4 3 10 0 0 \n", + ".. ... ... ... ... \n", + "875 7 26 5 0 \n", + "876 7 24 5 0 \n", + "877 10 23 7 0 \n", + "878 7 32 5 0 \n", + "880 4 23 5 0 \n", + "\n", + " tpsa ... sas n_aliphatic_carbocycles n_aliphatic_heterocyles \\\n", + "0 55.98 ... 1.690816 0 0 \n", + "1 54.70 ... 2.795444 0 0 \n", + "2 54.70 ... 2.381662 0 0 \n", + "3 45.75 ... 2.591944 0 0 \n", + "4 54.70 ... 2.232651 0 0 \n", + ".. ... ... ... ... ... \n", + "875 69.63 ... 2.148096 0 0 \n", + "876 55.40 ... 1.761027 0 0 \n", + "877 113.68 ... 2.716945 0 0 \n", + "878 65.77 ... 2.215072 0 1 \n", + "880 32.34 ... 1.851829 0 1 \n", + "\n", + " n_aliphatic_rings n_aromatic_carbocycles n_aromatic_heterocyles \\\n", + "0 0 0 1 \n", + "1 0 1 1 \n", + "2 0 1 1 \n", + "3 0 0 2 \n", + "4 0 1 1 \n", + ".. ... ... ... \n", + "875 0 2 2 \n", + "876 0 2 0 \n", + "877 0 1 0 \n", + "878 1 2 2 \n", + "880 1 2 0 \n", + "\n", + " n_aromatic_rings n_saturated_carbocycles n_saturated_heterocyles \\\n", + "0 1 0 0 \n", + "1 2 0 0 \n", + "2 2 0 0 \n", + "3 2 0 0 \n", + "4 2 0 0 \n", + ".. ... ... ... \n", + "875 4 0 0 \n", + "876 2 0 0 \n", + "877 1 0 0 \n", + "878 4 0 1 \n", + "880 2 0 0 \n", + "\n", + " n_saturated_rings \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + ".. ... \n", + "875 0 \n", + "876 0 \n", + "877 0 \n", + "878 1 \n", + "880 0 \n", + "\n", + "[863 rows x 22 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Part 3: Investigate compliance with Ro5\n", + "\n", + "df = df[df[\"mw\"] <= 500]\n", + "df = df[df[\"n_lipinski_hba\"] <= 10]\n", + "df = df[df[\"n_lipinski_hbd\"] <= 5]\n", + "df = df[df[\"clogp\"] <= 5]\n", + "df\n", + "\n", + "# 863 of the 881 compounds in the dataset satisfy all criteria in the rule of 5" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "dc9f95ed", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Part 4: Reveal the statistics for the dataset of compounds using Ro5 as a filter. How many fulfill vs. violate Ro5?\n", + "# Plotting the RO5 descriptors\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "fig, axs = plt.subplots(ncols=4, figsize=(25, 4))\n", + "\n", + "sns.histplot(df, x=\"mw\", ax=axs[0])\n", + "sns.histplot(df, x=\"n_lipinski_hba\", ax=axs[1])\n", + "sns.histplot(df, x=\"n_lipinski_hbd\", ax=axs[2])\n", + "sns.histplot(df, x=\"clogp\", ax=axs[3])" + ] + }, + { + "cell_type": "markdown", + "id": "415ac881", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "If you’re curious to learn more about some of the other established rules in the drug discovery industry, feel free to run this list through a Google search: \n", + "\n", + "- Rules of CNS\n", + "- BBB score\n", + "- Rule of Egan\n", + "- Rule-of-5\n", + "- Beyond Rule-of-5\n", + "- Rule-of-4\n", + "- Ghose Filter\n", + "- Zinc Rule\n", + "- Rule of GSK (4/400)\n", + "- Lead-Like Soft Rule\n", + "- Oprea’s Rule\n", + "- Pfizer Rule (3/75)\n", + "- REOS Filter\n", + "- Rule-of-3\n", + "- Extended Rule-of-3\n", + "- Veber Filter\n", + "\n", + "## References:\n", + "\n", + "- TeachOpenCADD - [https://projects.volkamerlab.org/teachopencadd/talktorials/T002_compound_adme.html?highlight=descriptors](https://projects.volkamerlab.org/teachopencadd/talktorials/T002_compound_adme.html?highlight=descriptors)\n", + "- ADME criteria ([Wikipedia](https://en.wikipedia.org/wiki/ADME) and [Mol Pharm. (2010), 7(5), 1388-1405](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3025274/))\n", + "- What are lead compounds? ([Wikipedia](https://en.wikipedia.org/wiki/Lead_compound))\n", + "- What is the LogP value? ([Wikipedia](https://en.wikipedia.org/wiki/Partition_coefficient))\n", + "- Lipinski et al. “Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings.” ([Adv. Drug Deliv. Rev. (1997), 23, 3-25](https://www.sciencedirect.com/science/article/pii/S0169409X96004231))" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python [conda env:datamol]", + "language": "python", + "name": "conda-env-datamol-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/tutorials/Fragment.ipynb b/docs/tutorials/Fragment.ipynb index e5832fd5..5a382ec8 100644 --- a/docs/tutorials/Fragment.ipynb +++ b/docs/tutorials/Fragment.ipynb @@ -1,17 +1,6 @@ { 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" 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" 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" - } - }, "cell_type": "markdown", "id": "7f18a74d", "metadata": {}, @@ -32,7 +21,7 @@ "\n", "Existing molecules can also be split into smaller fragments, a good visual is shown below of the fragmentation process: \n", "\n", - "![16717480-e193-4f3b-b39a-650801293024.png](attachment:16717480-e193-4f3b-b39a-650801293024.png)\n", + "![Fragment_1.png](./images/Fragment_1.png)\n", "\n", "[***Source***](https://www.frontiersin.org/articles/10.3389/fchem.2018.00229/full)\n", "\n", @@ -48,7 +37,7 @@ "\n", "## Fragment Generation\n", "\n", - "![image.png](attachment:ddd64e95-9256-46e4-9c25-2560deadf01c.png)\n", + "![Fragment_2.png](./images/Fragment_2.png)\n", "\n", "***[Source](https://www.researchgate.net/figure/A-schematic-overview-of-a-molecular-fragmentation-process-For-a-single-step_fig2_353714355)***\n", "\n", @@ -68,7 +57,7 @@ "\n", "Once molecules are fragmented, the next step is typically a matched molecular pair analysis (MMPA). This analysis compares the chemical structure of two molecules that only differ by a **single chemical transformation** (i.e. changing one functional group). MMP’s are useful to analyze a large collection of compounds because the minimal structural differences make it much easier to interpret any observable changes in physical or biological properties. We will not cover MMPA’s in this tutorial. See below for a visualization of a matched molecular pair: \n", "\n", - "![image.png](attachment:053aeef2-86e6-4191-bd4a-f82359a8efc4.png)\n", + "![Fragment_3.png](./images/Fragment_3.png)\n", "\n", "[Source](https://en.wikipedia.org/wiki/Matched_molecular_pair_analysis#:~:text=Matched%20molecular%20pair%20analysis%20(MMPA,matched%20molecular%20pairs%20(MMP).)\n", "\n", @@ -91,29 +80,7 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "af6f446c-052b-4d77-8ad4-89af2500f2fc", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mol" - ] - }, - { - "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "id": "a5e049ff", "metadata": {}, "outputs": [ @@ -121,10 +88,10 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -134,7 +101,6 @@ "from rdkit.Chem.Draw import IPythonConsole, MolsToGridImage\n", "from rdkit.Chem import BRICS\n", "from rdkit.Chem import Recap\n", - "from rdkit.Chem import rdMMPA\n", "\n", "from rdkit.Chem.Fraggle import FraggleSim\n", "\n", @@ -146,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 2, "id": "a393217f", "metadata": {}, "outputs": [ @@ -157,7 +123,7 @@ "" ] }, - "execution_count": 20, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -175,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 3, "id": "fe058c37", "metadata": {}, "outputs": [ @@ -183,10 +149,10 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -201,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 4, "id": "7c2059f8", "metadata": {}, "outputs": [ @@ -368,7 +334,7 @@ "" ] }, - "execution_count": 25, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -383,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 5, "id": "99027b1a", "metadata": {}, "outputs": [ @@ -562,7 +528,7 @@ "" ] }, - "execution_count": 26, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -577,7 +543,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 6, "id": "89850686", "metadata": {}, "outputs": [ @@ -654,7 +620,7 @@ "" ] }, - "execution_count": 27, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -684,7 +650,7 @@ }, { "cell_type": "code", - 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+ "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "" ], "text/plain": [ "" ] }, - "execution_count": 31, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1231,7 +1203,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 8, "id": "93f77ef0", "metadata": {}, "outputs": [ @@ -1248,10 +1220,10 @@ " 'c1ccc(-c2ccccn2)cc1',\n", " 'c1ccccc1',\n", " 'c1ccncc1'},\n", - " )" + " )" ] }, - "execution_count": 34, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } diff --git a/docs/tutorials/Fuzzy_Scaffolds.ipynb b/docs/tutorials/Fuzzy_Scaffolds.ipynb new file mode 100644 index 00000000..4d9db7fc --- /dev/null +++ b/docs/tutorials/Fuzzy_Scaffolds.ipynb @@ -0,0 +1,1567 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fa974713", + "metadata": {}, + "source": [ + "# Fuzzy Scaffolds\n", + "\n", + "Fuzzy scaffolding is a concept useful for scaffold decoration and constrained scaffolding. If you want finer control over the generation of your scaffolds, you can use the fuzzy scaffold function to **enforce specific groups** that need to appear in the core as a sort of pharmacophore requirement.\n", + "\n", + "**Note:** A pharmacophore is essentially “[a part of a molecular structure that is responsible for a particular biological or pharmacological interaction that it undergoes](https://link.springer.com/referenceworkentry/10.1007/978-3-642-16483-5_4502)”. \n", + "\n", + "You can also force R groups to be included as well, allowing for flexibility to reconstruct specified positions (attachment points) in the scaffold. Overall, it allows you to build a highly specific [molecular series to be used for MMPA](https://pubs.acs.org/doi/10.1021/jm500022q#:~:text=A%20matched%20molecular%20series%20is,groups%20at%20the%20same%20position.). \n", + "\n", + "## Understanding Key Parameters\n", + "\n", + "- **enforce_subs -** this lets you specify what substructure(s) you want to enforce on the scaffold\n", + "- **n_atom_cuttoff** - the minimum number of atoms a core should have. The smaller the number, the smaller the new scaffolds will be or the lesser number of new scaffolds will be generated, vice versa is true.\n", + "- **ignore_non_ring -** Some scaffolds might be a simple aliphatic chain, in other words, a molecule that only contains straight/branched chains with no rings. Most of the time, you want to ***ignore these scaffolds*** as they typically don’t translate well in a drug like context.\n", + "- **mcs_params -** This is quite a niche parameter. If two molecules in your dataset have a different Murcko scaffold but the same Minimum Common scaffold, toggling this argument will categorize these molecules into the same bucket using a [maximum common substructure algorithm](https://www.rdkit.org/docs/GettingStartedInPython.html#maximum-common-substructure).\n", + "\n", + "## Datamol Example" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "da29c9a0-c136-48f8-9be8-63c50eca0652", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + 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mols\n", + "data = dm.data.cdk2()\n", + "\n", + "data[\"mol\"] = data[\"smiles\"].apply(dm.to_mol)\n", + "\n", + "dm.to_image(data[\"mol\"].iloc[:12].tolist(), mol_size=(200, 150))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "54bc03cc-95d9-49b8-a071-b38837a57bd6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", 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+ "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with dm.without_rdkit_log():\n", + " scaffolds, scf2infos, scf2groups = dm.scaffold.fuzzy_scaffolding(data[\"mol\"].tolist())\n", + "\n", + "sfs = [dm.to_mol(s) for s in list(scaffolds)]\n", + "dm.to_image(sfs, mol_size=(200, 150), max_mols=12)" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python [conda env:datamol]", + "language": "python", + "name": "conda-env-datamol-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/tutorials/Preprocessing.ipynb b/docs/tutorials/Preprocessing.ipynb new file mode 100644 index 00000000..8b033384 --- /dev/null +++ b/docs/tutorials/Preprocessing.ipynb @@ -0,0 +1,1013 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0a462b7a", + "metadata": {}, + "source": [ + "# Pre-processing Molecules\n", + "\n", + "You’ve probably heard the adage “garbage in, garbage out” before in reference to the importance of data quality when it comes to AI/ML. The same holds true in the field of drug discovery. Given the scarcity and often non-consistent quality of available data for drug discovery, an initial clean up is almost always required to ensure the use of high quality data in the generation of your models. If you don’t do this, the use of lesser quality data would definitely impact the accuracy of your models in any downstream task. Pre-processing of data and molecules is extremely important, let’s dive in!\n", + "\n", + "## Representing Molecules\n", + "\n", + "There are many ways in which molecules can be represented. In other words, how can we effectively express the complexity of a molecule in a way that machines can understand? Here are some existing methods: \n", + "\n", + "- [Molfile](https://en.wikipedia.org/wiki/Chemical_table_file) - A table that holds information about the atoms, bonds, connectivity and coordinates of a molecule\n", + "- **SMILES** - stands for **S**implified **M**olecular **I**nput **L**ine-**E**ntry **S**ystem and the name essentially describes it. It’s a line notation for encoding molecular structures where atoms are represented by their standard abbreviation as a chemical element (i.e. C for carbon, N for nitrogen etc.). Multiple symbols are then used to define elements with charges, bonds, rings, aromaticity, stereochemistry, and much more. For more detail, read [here](https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system#Terminology).\n", + " - As an example, CCO, OCC and C(O)C all refer to ethanol. Having a number of equally valid SMILES strings for a given molecule can be an issue, therefore, canonicalization algorithms can be used to generate canonical SMILES to produce unique and consistent SMILES strings.\n", + " - Although SMILES are commonly used, they are not perfect. In a generative model using SMILES as inputs and outputs, there are often invalid SMILES strings that are produced (i.e. the SMILE string corresponds to an invalid molecule that violates basic chemical rules).\n", + "- **SELFIES** - stands for **SELF**-referenc**I**ng **E**mbedded **S**trings, it is another string-based representation for molecules that is generally more suitable for ML models and exhibits more robustness (i.e. more SELFIE strings corresponds to a valid molecule). Read more [here](https://aspuru.substack.com/p/molecular-graph-representations-and).\n", + "- **InChi** - another string-based method of representing chemical structures developed by IUPAC. Read more [here](https://iupac.org/100/stories/what-on-earth-is-inchi/). On the other hand, [InChi key](https://dev.drugbank.com/guides/terms/inchi-key) is a newer version of InChi that is only useful to identify molecules, however, it is impossible to reconstruct a molecule from an InChi key.\n", + "\n", + "See below for a graphic that summarizes some of the methods discussed in the section above: \n", + "\n", + "![Preprocess_1.png](./images/Preprocess_1.png)\n", + "\n", + "***[Source](https://www.researchgate.net/publication/344906202_Chemoinformatics-based_enumeration_of_chemical_libraries_a_tutorial)***\n", + "\n", + "**Note:** it’s important to understand that all forms of molecular representation have their pro’s and con’s. It’s less of a “one-size-fits-all” and more about picking and choosing specific methods to represent a molecule given your specific use case. \n", + "\n", + "## Sanitize and Standardize\n", + "\n", + "***Molecular sanitization*** is the process of ensuring that the molecules in your dataset ***are realistic***. You can read more about the sanitization procedure as applied in the RDKit [here](https://www.rdkit.org/docs/RDKit_Book.html#molecular-sanitization). In Datamol, there are a few extra steps as well, sanitization is done under the following procedure: \n", + "\n", + "1. Adjusting for nitrogen aromaticity since faulty valence for nitrogen in aromatic rings is [currently](https://github.com/rdkit/rdkit/issues/2011) an issue in RDKit through the Sanifix algorithm. \n", + "2. An extra conversion is done from mol → smiles → mol to ensure that the molecules are valid SMILES.\n", + "3. Charge neutralization - this is NOT charge removal, it attempts to correct valence issues arising from incorrect charges being placed on atoms.\n", + "\n", + "Users can control the application of the sanifix algorithim or charge neutralization, users can toggle the respective parameters ***sanifix*** and ***charge_neutral*** to be TRUE/FALSE. \n", + "\n", + "The process of **standardization** is used to generate ***canonical SMILES.*** It is currently done using the following procedure which can be controlled by the user through the described parameters below:\n", + "\n", + "- ***disconnect_metals -*** metal disconnection\n", + " - Depending on the source of the database, some compounds may be reported in salt form or associated to metallic ions (e.g. the sodium salt of a carboxylic compound). In most cases, these counter-ions are not relevant so the use of this function is required before further utilization of the dataset.)\n", + " - More details [here](https://molvs.readthedocs.io/en/latest/guide/standardize.html#disconnect-metals)\n", + "- ***normalize -*** ion (charge) and functional groups normalization\n", + " - It corrects drawing errors and standardizes functional groups in the molecule as well as ensuring the overall proper charge of the compound\n", + " - More details [here](https://molvs.readthedocs.io/en/latest/guide/standardize.html#apply-normalization-rules)\n", + "- ***reionize -*** reionization of the molecule (protonation following the acidity order)\n", + " - If one or more acidic functionalities are present in the molecule, this option ensures the correct neutral/ionized state for such functional groups. Molecules are uncharged by adding and/or removing hydrogens. For zwitterions, hydrogens are moved to eliminate charges where possible. However, in cases where there is a positive charge that is not neutralizable, an attempt is made to also preserve the corresponding negative charge\n", + " - Read more [here](https://molvs.readthedocs.io/en/latest/guide/standardize.html#reionize-acids)\n", + "- ***uncharge* -** charge removal\n", + " - This option neutralize the molecule by reversing the protonation state of protonated and deprotonated groups, if present (e.g. a carboxylate is re-protonated to the corresponding carboxylic acid).\n", + " - In cases where there is a positive charge that is not neutralizable, an attempt is made to also preserve the corresponding negative charge to ensure a net zero charge.\n", + "- ***stereo -*** stereochemistry proper reassignment if missing.\n", + " - Stereochemical information is corrected and/or added if missing using built-in RDKit functionality to force a clean recalculation of stereochemistry\n", + "\n", + "The actual processes for sanitization and standardization described can get a bit too detailed, with lots of chemistry terminology. We recommend just sticking with the defaults already set in Datamol. It’s enough just to understand the importance of why we sanitize and standardize our datasets as a key step in the pre-processing, as you continue spending time in the AI/ML for drug discovery field, you will get more familiar with the details. \n", + "\n", + "## Tutorial\n", + "\n", + "In this tutorial, let’s walk through how to load a dataset and then apply the described pre-processing pipeline which will take a list of molecules and: \n", + "\n", + "- Convert to a mol.\n", + "- Fix common errors in the mol.\n", + "- Sanitize the mol.\n", + "- Standardize the mol.\n", + "- Generate a standardized SMILES.\n", + "- Generate SELFIES.\n", + "- Generate InChi and InChi key.\n", + "- Save the results as CSV or SDF file formats.\n", + "\n", + "From here, we will generate a table where it can more easily visualized. The option of parallelization will also be shown: \n", + "\n", + "**Note:** parallelizing the preprocessing will only be faster if your dataset is very large. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2a3c8bf5", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import datamol as dm\n", + "\n", + "dm.disable_rdkit_log()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "fc621492", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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iupacsmilesexptcalc
04-methoxy-N,N-dimethyl-benzamideCN(C)C(=O)c1ccc(cc1)OC-11.01-9.625
1methanesulfonyl chlorideCS(=O)(=O)Cl-4.87-6.219
23-methylbut-1-eneCC(C)C=C1.832.452
32-ethylpyrazineCCc1cnccn1-5.45-5.809
4heptan-1-olCCCCCCCO-4.21-2.917
...............
637methyl octanoateCCCCCCCC(=O)OC-2.04-3.035
638pyrrolidineC1CCNC1-5.48-4.278
6394-hydroxybenzaldehydec1cc(ccc1C=O)O-8.83-10.050
6401-chloroheptaneCCCCCCCCl0.291.467
6411,4-dioxaneC1COCCO1-5.06-4.269
\n", + "

642 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " iupac smiles expt calc\n", + "0 4-methoxy-N,N-dimethyl-benzamide CN(C)C(=O)c1ccc(cc1)OC -11.01 -9.625\n", + "1 methanesulfonyl chloride CS(=O)(=O)Cl -4.87 -6.219\n", + "2 3-methylbut-1-ene CC(C)C=C 1.83 2.452\n", + "3 2-ethylpyrazine CCc1cnccn1 -5.45 -5.809\n", + "4 heptan-1-ol CCCCCCCO -4.21 -2.917\n", + ".. ... ... ... ...\n", + "637 methyl octanoate CCCCCCCC(=O)OC -2.04 -3.035\n", + "638 pyrrolidine C1CCNC1 -5.48 -4.278\n", + "639 4-hydroxybenzaldehyde c1cc(ccc1C=O)O -8.83 -10.050\n", + "640 1-chloroheptane CCCCCCCCl 0.29 1.467\n", + "641 1,4-dioxane C1COCCO1 -5.06 -4.269\n", + "\n", + "[642 rows x 4 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load a dataset\n", + "data = dm.data.freesolv()\n", + "\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f7f710f1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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iupacsmilesexptcalcstandard_smilesselfiesinchiinchikey
04-methoxy-N,N-dimethyl-benzamideCN(C)C(=O)c1ccc(cc1)OC-11.01-9.625COc1ccc(C(=O)N(C)C)cc1[C][O][C][=C][C][=C][Branch1][#Branch2][C][=Br...InChI=1S/C10H13NO2/c1-11(2)10(12)8-4-6-9(13-3)...OCGXPFSUJVHRHA-UHFFFAOYSA-N
1methanesulfonyl chlorideCS(=O)(=O)Cl-4.87-6.219CS(=O)(=O)Cl[C][S][=Branch1][C][=O][=Branch1][C][=O][Cl]InChI=1S/CH3ClO2S/c1-5(2,3)4/h1H3QARBMVPHQWIHKH-UHFFFAOYSA-N
23-methylbut-1-eneCC(C)C=C1.832.452C=CC(C)C[C][=C][C][Branch1][C][C][C]InChI=1S/C5H10/c1-4-5(2)3/h4-5H,1H2,2-3H3YHQXBTXEYZIYOV-UHFFFAOYSA-N
32-ethylpyrazineCCc1cnccn1-5.45-5.809CCc1cnccn1[C][C][C][=C][N][=C][C][=N][Ring1][=Branch1]InChI=1S/C6H8N2/c1-2-6-5-7-3-4-8-6/h3-5H,2H2,1H3KVFIJIWMDBAGDP-UHFFFAOYSA-N
4heptan-1-olCCCCCCCO-4.21-2.917CCCCCCCO[C][C][C][C][C][C][C][O]InChI=1S/C7H16O/c1-2-3-4-5-6-7-8/h8H,2-7H2,1H3BBMCTIGTTCKYKF-UHFFFAOYSA-N
...........................
637methyl octanoateCCCCCCCC(=O)OC-2.04-3.035CCCCCCCC(=O)OC[C][C][C][C][C][C][C][C][=Branch1][C][=O][O][C]InChI=1S/C9H18O2/c1-3-4-5-6-7-8-9(10)11-2/h3-8...JGHZJRVDZXSNKQ-UHFFFAOYSA-N
638pyrrolidineC1CCNC1-5.48-4.278C1CCNC1[C][C][C][N][C][Ring1][Branch1]InChI=1S/C4H9N/c1-2-4-5-3-1/h5H,1-4H2RWRDLPDLKQPQOW-UHFFFAOYSA-N
6394-hydroxybenzaldehydec1cc(ccc1C=O)O-8.83-10.050O=Cc1ccc(O)cc1[O][=C][C][=C][C][=C][Branch1][C][O][C][=C][Ri...InChI=1S/C7H6O2/c8-5-6-1-3-7(9)4-2-6/h1-5,9HRGHHSNMVTDWUBI-UHFFFAOYSA-N
6401-chloroheptaneCCCCCCCCl0.291.467CCCCCCCCl[C][C][C][C][C][C][C][Cl]InChI=1S/C7H15Cl/c1-2-3-4-5-6-7-8/h2-7H2,1H3DZMDPHNGKBEVRE-UHFFFAOYSA-N
6411,4-dioxaneC1COCCO1-5.06-4.269C1COCCO1[C][C][O][C][C][O][Ring1][=Branch1]InChI=1S/C4H8O2/c1-2-6-4-3-5-1/h1-4H2RYHBNJHYFVUHQT-UHFFFAOYSA-N
\n", + "

642 rows × 8 columns

\n", + "
" + ], + "text/plain": [ + " iupac smiles expt calc \\\n", + "0 4-methoxy-N,N-dimethyl-benzamide CN(C)C(=O)c1ccc(cc1)OC -11.01 -9.625 \n", + "1 methanesulfonyl chloride CS(=O)(=O)Cl -4.87 -6.219 \n", + "2 3-methylbut-1-ene CC(C)C=C 1.83 2.452 \n", + "3 2-ethylpyrazine CCc1cnccn1 -5.45 -5.809 \n", + "4 heptan-1-ol CCCCCCCO -4.21 -2.917 \n", + ".. ... ... ... ... \n", + "637 methyl octanoate CCCCCCCC(=O)OC -2.04 -3.035 \n", + "638 pyrrolidine C1CCNC1 -5.48 -4.278 \n", + "639 4-hydroxybenzaldehyde c1cc(ccc1C=O)O -8.83 -10.050 \n", + "640 1-chloroheptane CCCCCCCCl 0.29 1.467 \n", + "641 1,4-dioxane C1COCCO1 -5.06 -4.269 \n", + "\n", + " standard_smiles \\\n", + "0 COc1ccc(C(=O)N(C)C)cc1 \n", + "1 CS(=O)(=O)Cl \n", + "2 C=CC(C)C \n", + "3 CCc1cnccn1 \n", + "4 CCCCCCCO \n", + ".. ... \n", + "637 CCCCCCCC(=O)OC \n", + "638 C1CCNC1 \n", + "639 O=Cc1ccc(O)cc1 \n", + "640 CCCCCCCCl \n", + "641 C1COCCO1 \n", + "\n", + " selfies \\\n", + "0 [C][O][C][=C][C][=C][Branch1][#Branch2][C][=Br... \n", + "1 [C][S][=Branch1][C][=O][=Branch1][C][=O][Cl] \n", + "2 [C][=C][C][Branch1][C][C][C] \n", + "3 [C][C][C][=C][N][=C][C][=N][Ring1][=Branch1] \n", + "4 [C][C][C][C][C][C][C][O] \n", + ".. ... \n", + "637 [C][C][C][C][C][C][C][C][=Branch1][C][=O][O][C] \n", + "638 [C][C][C][N][C][Ring1][Branch1] \n", + "639 [O][=C][C][=C][C][=C][Branch1][C][O][C][=C][Ri... \n", + "640 [C][C][C][C][C][C][C][Cl] \n", + "641 [C][C][O][C][C][O][Ring1][=Branch1] \n", + "\n", + " inchi \\\n", + "0 InChI=1S/C10H13NO2/c1-11(2)10(12)8-4-6-9(13-3)... \n", + "1 InChI=1S/CH3ClO2S/c1-5(2,3)4/h1H3 \n", + "2 InChI=1S/C5H10/c1-4-5(2)3/h4-5H,1H2,2-3H3 \n", + "3 InChI=1S/C6H8N2/c1-2-6-5-7-3-4-8-6/h3-5H,2H2,1H3 \n", + "4 InChI=1S/C7H16O/c1-2-3-4-5-6-7-8/h8H,2-7H2,1H3 \n", + ".. ... \n", + "637 InChI=1S/C9H18O2/c1-3-4-5-6-7-8-9(10)11-2/h3-8... \n", + "638 InChI=1S/C4H9N/c1-2-4-5-3-1/h5H,1-4H2 \n", + "639 InChI=1S/C7H6O2/c8-5-6-1-3-7(9)4-2-6/h1-5,9H \n", + "640 InChI=1S/C7H15Cl/c1-2-3-4-5-6-7-8/h2-7H2,1H3 \n", + "641 InChI=1S/C4H8O2/c1-2-6-4-3-5-1/h1-4H2 \n", + "\n", + " inchikey \n", + "0 OCGXPFSUJVHRHA-UHFFFAOYSA-N \n", + "1 QARBMVPHQWIHKH-UHFFFAOYSA-N \n", + "2 YHQXBTXEYZIYOV-UHFFFAOYSA-N \n", + "3 KVFIJIWMDBAGDP-UHFFFAOYSA-N \n", + "4 BBMCTIGTTCKYKF-UHFFFAOYSA-N \n", + ".. ... \n", + "637 JGHZJRVDZXSNKQ-UHFFFAOYSA-N \n", + "638 RWRDLPDLKQPQOW-UHFFFAOYSA-N \n", + "639 RGHHSNMVTDWUBI-UHFFFAOYSA-N \n", + "640 DZMDPHNGKBEVRE-UHFFFAOYSA-N \n", + "641 RYHBNJHYFVUHQT-UHFFFAOYSA-N \n", + "\n", + "[642 rows x 8 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "smiles_column = \"smiles\"\n", + "\n", + "\n", + "def _preprocess(row):\n", + " mol = dm.to_mol(row[smiles_column], ordered=True)\n", + " mol = dm.fix_mol(mol)\n", + " mol = dm.sanitize_mol(mol, sanifix=True, charge_neutral=False)\n", + " mol = dm.standardize_mol(\n", + " mol,\n", + " disconnect_metals=False,\n", + " normalize=True,\n", + " reionize=True,\n", + " uncharge=False,\n", + " stereo=True,\n", + " )\n", + "\n", + " row[\"standard_smiles\"] = dm.standardize_smiles(dm.to_smiles(mol))\n", + " row[\"selfies\"] = dm.to_selfies(mol)\n", + " row[\"inchi\"] = dm.to_inchi(mol)\n", + " row[\"inchikey\"] = dm.to_inchikey(mol)\n", + " return row\n", + "\n", + "\n", + "data_clean = data.apply(_preprocess, axis=1)\n", + "data_clean" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "66298eaf", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f4c36eb9eae1484a9fa0a31521692cd2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/642 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
iupacsmilesexptcalcstandard_smilesselfiesinchiinchikey
04-methoxy-N,N-dimethyl-benzamideCN(C)C(=O)c1ccc(cc1)OC-11.01-9.625COc1ccc(C(=O)N(C)C)cc1[C][O][C][=C][C][=C][Branch1][#Branch2][C][=Br...InChI=1S/C10H13NO2/c1-11(2)10(12)8-4-6-9(13-3)...OCGXPFSUJVHRHA-UHFFFAOYSA-N
1methanesulfonyl chlorideCS(=O)(=O)Cl-4.87-6.219CS(=O)(=O)Cl[C][S][=Branch1][C][=O][=Branch1][C][=O][Cl]InChI=1S/CH3ClO2S/c1-5(2,3)4/h1H3QARBMVPHQWIHKH-UHFFFAOYSA-N
23-methylbut-1-eneCC(C)C=C1.832.452C=CC(C)C[C][=C][C][Branch1][C][C][C]InChI=1S/C5H10/c1-4-5(2)3/h4-5H,1H2,2-3H3YHQXBTXEYZIYOV-UHFFFAOYSA-N
32-ethylpyrazineCCc1cnccn1-5.45-5.809CCc1cnccn1[C][C][C][=C][N][=C][C][=N][Ring1][=Branch1]InChI=1S/C6H8N2/c1-2-6-5-7-3-4-8-6/h3-5H,2H2,1H3KVFIJIWMDBAGDP-UHFFFAOYSA-N
4heptan-1-olCCCCCCCO-4.21-2.917CCCCCCCO[C][C][C][C][C][C][C][O]InChI=1S/C7H16O/c1-2-3-4-5-6-7-8/h8H,2-7H2,1H3BBMCTIGTTCKYKF-UHFFFAOYSA-N
...........................
637methyl octanoateCCCCCCCC(=O)OC-2.04-3.035CCCCCCCC(=O)OC[C][C][C][C][C][C][C][C][=Branch1][C][=O][O][C]InChI=1S/C9H18O2/c1-3-4-5-6-7-8-9(10)11-2/h3-8...JGHZJRVDZXSNKQ-UHFFFAOYSA-N
638pyrrolidineC1CCNC1-5.48-4.278C1CCNC1[C][C][C][N][C][Ring1][Branch1]InChI=1S/C4H9N/c1-2-4-5-3-1/h5H,1-4H2RWRDLPDLKQPQOW-UHFFFAOYSA-N
6394-hydroxybenzaldehydec1cc(ccc1C=O)O-8.83-10.050O=Cc1ccc(O)cc1[O][=C][C][=C][C][=C][Branch1][C][O][C][=C][Ri...InChI=1S/C7H6O2/c8-5-6-1-3-7(9)4-2-6/h1-5,9HRGHHSNMVTDWUBI-UHFFFAOYSA-N
6401-chloroheptaneCCCCCCCCl0.291.467CCCCCCCCl[C][C][C][C][C][C][C][Cl]InChI=1S/C7H15Cl/c1-2-3-4-5-6-7-8/h2-7H2,1H3DZMDPHNGKBEVRE-UHFFFAOYSA-N
6411,4-dioxaneC1COCCO1-5.06-4.269C1COCCO1[C][C][O][C][C][O][Ring1][=Branch1]InChI=1S/C4H8O2/c1-2-6-4-3-5-1/h1-4H2RYHBNJHYFVUHQT-UHFFFAOYSA-N
\n", + "

642 rows × 8 columns

\n", + "" + ], + "text/plain": [ + " iupac smiles expt calc \\\n", + "0 4-methoxy-N,N-dimethyl-benzamide CN(C)C(=O)c1ccc(cc1)OC -11.01 -9.625 \n", + "1 methanesulfonyl chloride CS(=O)(=O)Cl -4.87 -6.219 \n", + "2 3-methylbut-1-ene CC(C)C=C 1.83 2.452 \n", + "3 2-ethylpyrazine CCc1cnccn1 -5.45 -5.809 \n", + "4 heptan-1-ol CCCCCCCO -4.21 -2.917 \n", + ".. ... ... ... ... \n", + "637 methyl octanoate CCCCCCCC(=O)OC -2.04 -3.035 \n", + "638 pyrrolidine C1CCNC1 -5.48 -4.278 \n", + "639 4-hydroxybenzaldehyde c1cc(ccc1C=O)O -8.83 -10.050 \n", + "640 1-chloroheptane CCCCCCCCl 0.29 1.467 \n", + "641 1,4-dioxane C1COCCO1 -5.06 -4.269 \n", + "\n", + " standard_smiles \\\n", + "0 COc1ccc(C(=O)N(C)C)cc1 \n", + "1 CS(=O)(=O)Cl \n", + "2 C=CC(C)C \n", + "3 CCc1cnccn1 \n", + "4 CCCCCCCO \n", + ".. ... \n", + "637 CCCCCCCC(=O)OC \n", + "638 C1CCNC1 \n", + "639 O=Cc1ccc(O)cc1 \n", + "640 CCCCCCCCl \n", + "641 C1COCCO1 \n", + "\n", + " selfies \\\n", + "0 [C][O][C][=C][C][=C][Branch1][#Branch2][C][=Br... \n", + "1 [C][S][=Branch1][C][=O][=Branch1][C][=O][Cl] \n", + "2 [C][=C][C][Branch1][C][C][C] \n", + "3 [C][C][C][=C][N][=C][C][=N][Ring1][=Branch1] \n", + "4 [C][C][C][C][C][C][C][O] \n", + ".. ... \n", + "637 [C][C][C][C][C][C][C][C][=Branch1][C][=O][O][C] \n", + "638 [C][C][C][N][C][Ring1][Branch1] \n", + "639 [O][=C][C][=C][C][=C][Branch1][C][O][C][=C][Ri... \n", + "640 [C][C][C][C][C][C][C][Cl] \n", + "641 [C][C][O][C][C][O][Ring1][=Branch1] \n", + "\n", + " inchi \\\n", + "0 InChI=1S/C10H13NO2/c1-11(2)10(12)8-4-6-9(13-3)... \n", + "1 InChI=1S/CH3ClO2S/c1-5(2,3)4/h1H3 \n", + "2 InChI=1S/C5H10/c1-4-5(2)3/h4-5H,1H2,2-3H3 \n", + "3 InChI=1S/C6H8N2/c1-2-6-5-7-3-4-8-6/h3-5H,2H2,1H3 \n", + "4 InChI=1S/C7H16O/c1-2-3-4-5-6-7-8/h8H,2-7H2,1H3 \n", + ".. ... \n", + "637 InChI=1S/C9H18O2/c1-3-4-5-6-7-8-9(10)11-2/h3-8... \n", + "638 InChI=1S/C4H9N/c1-2-4-5-3-1/h5H,1-4H2 \n", + "639 InChI=1S/C7H6O2/c8-5-6-1-3-7(9)4-2-6/h1-5,9H \n", + "640 InChI=1S/C7H15Cl/c1-2-3-4-5-6-7-8/h2-7H2,1H3 \n", + "641 InChI=1S/C4H8O2/c1-2-6-4-3-5-1/h1-4H2 \n", + "\n", + " inchikey \n", + "0 OCGXPFSUJVHRHA-UHFFFAOYSA-N \n", + "1 QARBMVPHQWIHKH-UHFFFAOYSA-N \n", + "2 YHQXBTXEYZIYOV-UHFFFAOYSA-N \n", + "3 KVFIJIWMDBAGDP-UHFFFAOYSA-N \n", + "4 BBMCTIGTTCKYKF-UHFFFAOYSA-N \n", + ".. ... \n", + "637 JGHZJRVDZXSNKQ-UHFFFAOYSA-N \n", + "638 RWRDLPDLKQPQOW-UHFFFAOYSA-N \n", + "639 RGHHSNMVTDWUBI-UHFFFAOYSA-N \n", + "640 DZMDPHNGKBEVRE-UHFFFAOYSA-N \n", + "641 RYHBNJHYFVUHQT-UHFFFAOYSA-N \n", + "\n", + "[642 rows x 8 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "smiles_column = \"smiles\"\n", + "\n", + "\n", + "def _preprocess(i, row):\n", + "\n", + " dm.disable_rdkit_log()\n", + "\n", + " mol = dm.to_mol(row[smiles_column], ordered=True)\n", + " mol = dm.fix_mol(mol)\n", + " mol = dm.sanitize_mol(mol, sanifix=True, charge_neutral=False)\n", + " mol = dm.standardize_mol(\n", + " mol, disconnect_metals=False, normalize=True, reionize=True, uncharge=False, stereo=True\n", + " )\n", + "\n", + " row[\"standard_smiles\"] = dm.standardize_smiles(dm.to_smiles(mol))\n", + " row[\"selfies\"] = dm.to_selfies(mol)\n", + " row[\"inchi\"] = dm.to_inchi(mol)\n", + " row[\"inchikey\"] = dm.to_inchikey(mol)\n", + " return row\n", + "\n", + "\n", + "data_clean = dm.parallelized(_preprocess, data.iterrows(), arg_type=\"args\", progress=True, total=len(data))\n", + "data_clean = pd.DataFrame(data_clean)\n", + "data_clean" + ] + }, + { + "cell_type": "markdown", + "id": "bc123d3a", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "- [https://depth-first.com/articles/2020/07/27/a-guide-to-molecular-standardization/](https://depth-first.com/articles/2020/07/27/a-guide-to-molecular-standardization/)\n", + "- Wikipedia - [https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system#Terminology](https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system#Terminology)" + ] + } + ], + 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Scaffolds are important in drug discovery as they help us uncover structure-activity relationships ([SAR](https://info.collaborativedrug.com/tofu-content-what-is-sar)) and often are found to be essential for the bioactivity of a given class of compounds. The general idea behind this approach is finding relationships between the structure of a compound and its properties such as biological activity and/or physicochemical properties. \n", + "\n", + "There are multiple ways to define a scaffold and this can make it hard to compare the results of different, independent studies that involve scaffolds. If you’re interested, you can read more about scaffolding [here](https://datagrok.ai/help/domains/chem/functions/murcko-scaffolds) and [here](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3328312/). Most of the time, we rely on the Murcko scaffold which is also known as the [Bemis-Murcko](https://pubs.acs.org/doi/10.1021/jm9602928) framework. \n", + "\n", + "From [Chemaxon](https://docs.chemaxon.com/display/docs/bemis-murcko-clustering.md): “Bemis and Murcko outlined a popular method for deriving scaffolds from molecules by removing side chain atoms. A molecular framework can be interpreted as a graph containing nodes and edges representing atom and bond types, respectively. Removing atom and bond labels or agglomerating nodes by chemotype yields a hierarchy of reduced graphs, or molecular equivalence classes, that represent sets of related molecules. Likewise, a framework can be further decomposed into individual rings (or the core ring assembly) using chemically intuitive rules: the rings can individually or jointly be considered as scaffolds derived from the original compound.” \n", + "\n", + "Scaffolds are generally useful in two main ways: \n", + "\n", + "1. Identifying core structures that have preferential activity against some specific target classes. This can then serve as a “building block” to further optimize active compounds on certain properties through the modification of R groups that are attached to the scaffold (sometimes referred to as scaffold decorations).\n", + "2. Scaffold hopping - finding structurally distinct compounds that have the same activity\n", + "\n", + "Scaffold hopping is particularly useful in [ligand](https://en.wikipedia.org/wiki/Ligand_(biochemistry))-based virtual screening methods where the information of known active compounds is used for hit identification and optimization rather than the available structural data for the target protein. In this approach, you start with a search template (i.e. the scaffold of the known active compound with all the decorations), keep the decorations the same and replace the scaffold itself with a similar molecular structure\n", + "\n", + "Below is an image showing a network of possible scaffolds for a given molecule A: \n", + "\n", + "![Scaffolds_1.png](./images/Scaffolds_1.png)\n", + "\n", + "If you’re interested in learning more about scaffolds and how we explore scaffolds computationally, read this [paper](https://pubs.acs.org/doi/10.1021/acs.jmedchem.5b01746). \n", + "\n", + "## Tutorial\n", + "\n", + "So, what does this look like in practice? This tutorial will show you some of the basics of using scaffolds in drug discovery. \n", + "\n", + "1. Load an example dataset/list of molecules\n", + "2. Identify the scaffolds\n", + "3. This will then enable you to create a chemical series which can be used in a MMPA (see the [fragmentation](https://www.notion.so/Fragmenting-Compounds-8c861697ae6c44f3991cb215fd93e393) tutorial)\n", + " 1. A molecular series refers to a set of two or more molecules with the same scaffold but different R groups at the same position, read more [here](https://pubs.acs.org/doi/10.1021/jm500022q#:~:text=A%20matched%20molecular%20series%20is,groups%20at%20the%20same%20position.). Once a molecular series is generated, it enables scientists to focus on studying molecular properties and how changes in the structure are associated in the changes of these values (e.g. SAR studies).\n", + "\n", + "## RDKit Example" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "766be1ec-45ab-4794-80c2-3445d7b98e87", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from rdkit import Chem\n", + "from rdkit.Chem.Scaffolds import MurckoScaffold\n", + "from rdkit.Chem.Draw import IPythonConsole, MolsToGridImage\n", + "\n", + "# Load a list of molecules\n", + "smiles_list = [\n", + " \"CCOC1=CC=CC=C1C(=O)OCC(=O)NC1=CC=CC=C1\",\n", + " \"NC(=O)C1=C(NC(=O)COC2=CC=CC=C2C(F)(F)F)SC=C1\",\n", + " \"CC(C)NC(=O)CSCC1=CC=CC=C1Br\",\n", + " \"CC1=CC=C(C(=O)NC(C)C)C=C1NC(=O)C1=CC=CO1\",\n", + " \"O=C(CN1CCCCCC1=O)NCC1=CC=C(N2C=CN=C2)C(F)=C1\",\n", + "]\n", + "mol_list = [Chem.MolFromSmiles(smi) for smi in smiles_list]\n", + "\n", + "MolsToGridImage(mol_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7aebaa62-5cc1-46e3-8988-8e4092cc05cb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scaffolds = [MurckoScaffold.GetScaffoldForMol(mol) for mol in mol_list]\n", + "\n", + "MolsToGridImage(scaffolds)" + ] + }, + { + "cell_type": "markdown", + "id": "83230b24-9f2a-4ade-8fa9-23e052b26675", + "metadata": {}, + "source": [ + "## Datamol Example" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d5b3010f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import datamol as dm\n", + "\n", + "# Load a list of molecules\n", + "smiles_list = [\n", + " \"CCOC1=CC=CC=C1C(=O)OCC(=O)NC1=CC=CC=C1\",\n", + " \"NC(=O)C1=C(NC(=O)COC2=CC=CC=C2C(F)(F)F)SC=C1\",\n", + " \"CC(C)NC(=O)CSCC1=CC=CC=C1Br\",\n", + " \"CC1=CC=C(C(=O)NC(C)C)C=C1NC(=O)C1=CC=CO1\",\n", + " \"O=C(CN1CCCCCC1=O)NCC1=CC=C(N2C=CN=C2)C(F)=C1\",\n", + "]\n", + "mol_list = [dm.to_mol(smi) for smi in smiles_list]\n", + "dm.to_image(mol_list, mol_size=(200, 150))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7f735671", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Extracting Murcko scaffolds from list of compounds\n", + "scaffolds = [dm.to_scaffold_murcko(mol) for mol in mol_list]\n", + "\n", + "dm.to_image(scaffolds, mol_size=(200, 150))" + ] + }, + { + "cell_type": "markdown", + "id": "1e167bb7", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "- What is SAR? [https://info.collaborativedrug.com/tofu-content-what-is-sar](https://info.collaborativedrug.com/tofu-content-what-is-sar)\n", + "- [https://datagrok.ai/help/domains/chem/functions/murcko-scaffolds](https://datagrok.ai/help/domains/chem/functions/murcko-scaffolds)\n", + "- [http://practicalcheminformatics.blogspot.com/2021/10/exploratory-data-analysis-with.html](http://practicalcheminformatics.blogspot.com/2021/10/exploratory-data-analysis-with.html)" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python [conda env:datamol]", + "language": "python", + "name": "conda-env-datamol-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/tutorials/data/Enamine_DNA_Libary_5530cmpds_20200831_SMALL.sdf b/docs/tutorials/data/Enamine_DNA_Libary_5530cmpds_20200831_SMALL.sdf new file mode 100644 index 00000000..fb9a1546 --- /dev/null +++ b/docs/tutorials/data/Enamine_DNA_Libary_5530cmpds_20200831_SMALL.sdf @@ -0,0 +1,77062 @@ +Compound 1 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 9 9 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3163 -0.7352 0.0 0 +M V30 3 N -2.6327 0.0237 0.0 0 +M V30 4 C -1.3282 -2.2295 0.0 0 +M V30 5 C -2.6445 -2.9647 0.0 0 +M V30 6 C -0.0355 -2.9647 0.0 0 +M V30 7 N -2.6564 -4.459 0.0 0 +M V30 8 C -0.0474 -4.459 0.0 0 +M V30 9 C -1.3637 -5.1942 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 2 7 9 +M V30 9 1 8 9 +M V30 END BOND +M V30 END CTAB +M END +> +1 + +> +Z33546463 + +> +122.125 + +> +-0.206 + +> +1 + +> +55.980 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1140 + +> +1.0 + +$$$$ +Compound 2 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 10 11 0 0 0 +M V30 BEGIN ATOM +M V30 1 N 0.0 0.0 0.0 0 +M V30 2 N -0.9031 -1.212 0.0 0 +M V30 3 C 1.4259 -0.4515 0.0 0 +M V30 4 C -0.0237 -2.4241 0.0 0 +M V30 5 C 1.414 -1.9488 0.0 0 +M V30 6 C 2.7212 0.3089 0.0 0 +M V30 7 C 2.7093 -2.6855 0.0 0 +M V30 8 C 4.0164 -0.4277 0.0 0 +M V30 9 C 4.0045 -1.925 0.0 0 +M V30 10 N 5.3117 0.3327 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 1 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 2 4 5 +M V30 11 1 8 9 +M V30 END BOND +M V30 END CTAB +M END +> +2 + +> +Z2335631566 + +> +133.151 + +> +1.036 + +> +2 + +> +54.700 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL559818 + +> +0.9 + +$$$$ +Compound 3 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 11 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 N 3.1551 -0.0901 0.0 0 +M V30 3 N 4.0227 -1.2958 0.0 0 +M V30 4 C 4.0227 1.1381 0.0 0 +M V30 5 C 5.4426 -0.8225 0.0 0 +M V30 6 C 5.4426 0.6873 0.0 0 +M V30 7 C 6.7384 -1.5662 0.0 0 +M V30 8 C 6.7384 1.4536 0.0 0 +M V30 9 C 8.0343 -0.8 0.0 0 +M V30 10 C 8.0343 0.7099 0.0 0 +M V30 11 N 9.3301 1.4761 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 2 3 +M V30 2 2 2 4 +M V30 3 1 3 5 +M V30 4 1 4 6 +M V30 5 1 5 7 +M V30 6 1 6 8 +M V30 7 2 7 9 +M V30 8 2 8 10 +M V30 9 1 10 11 +M V30 10 2 5 6 +M V30 11 1 9 10 +M V30 END BOND +M V30 END CTAB +M END +> +3 + +> +Z319217146 + +> +133.151 + +> +1.036 + +> +2 + +> +54.700 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL559818 + +> +0.89 + +$$$$ +Compound 4 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 10 11 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.4163 0.4721 0.0 0 +M V30 3 C -0.897 1.2274 0.0 0 +M V30 4 N 2.7028 -0.2596 0.0 0 +M V30 5 C 1.4045 1.9828 0.0 0 +M V30 6 C -0.0236 2.4549 0.0 0 +M V30 7 C 3.9892 0.4957 0.0 0 +M V30 8 C 2.691 2.7382 0.0 0 +M V30 9 N 3.9774 2.0064 0.0 0 +M V30 10 O 2.6791 4.2489 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 1 7 9 +M V30 9 2 8 10 +M V30 10 1 5 6 +M V30 11 1 8 9 +M V30 END BOND +M V30 END CTAB +M END +> +4 + +> +Z56761824 + +> +152.174 + +> +0.124 + +> +1 + +> +41.460 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL2432028 + +> +1.0 + +$$$$ +Compound 5 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 10 11 0 0 0 +M V30 BEGIN ATOM +M V30 1 N 0.0 0.0 0.0 0 +M V30 2 N 0.8761 -1.2076 0.0 0 +M V30 3 C 0.8761 1.2313 0.0 0 +M V30 4 C 2.2968 -0.734 0.0 0 +M V30 5 C 2.2968 0.7814 0.0 0 +M V30 6 C 3.5874 -1.4681 0.0 0 +M V30 7 C 3.5874 1.5391 0.0 0 +M V30 8 C 4.8779 -0.7103 0.0 0 +M V30 9 N 3.5755 3.0546 0.0 0 +M V30 10 C 4.8779 0.805 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 1 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 2 7 10 +M V30 10 2 4 5 +M V30 11 1 8 10 +M V30 END BOND +M V30 END CTAB +M END +> +5 + +> +Z381673574 + +> +133.151 + +> +1.036 + +> +2 + +> +54.700 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL559818 + +> +0.86 + +$$$$ +Compound 6 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 10 11 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4479 1.438 0.0 0 +M V30 3 N -0.4479 2.6639 0.0 0 +M V30 4 C 1.8624 1.9095 0.0 0 +M V30 5 N 0.4243 3.8898 0.0 0 +M V30 6 C 1.8506 3.4183 0.0 0 +M V30 7 C 3.1472 1.1669 0.0 0 +M V30 8 C 3.1354 4.1845 0.0 0 +M V30 9 C 4.4321 1.9331 0.0 0 +M V30 10 C 4.4203 3.4419 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 2 8 10 +M V30 10 1 5 6 +M V30 11 1 9 10 +M V30 END BOND +M V30 END CTAB +M END +> +6 + +> +Z586248764 + +> +134.135 + +> +1.529 + +> +2 + +> +41.130 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL2001613 + +> +0.86 + +$$$$ +Compound 7 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 10 10 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.5116 0.0 0 +M V30 3 N -1.3226 2.2674 0.0 0 +M V30 4 C 1.2754 2.2674 0.0 0 +M V30 5 C 2.5627 1.5352 0.0 0 +M V30 6 C 1.2636 3.7791 0.0 0 +M V30 7 N 3.8499 2.291 0.0 0 +M V30 8 C 2.5508 4.5349 0.0 0 +M V30 9 C 3.8381 3.8027 0.0 0 +M V30 10 C 2.539 6.0465 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 1 8 9 +M V30 END BOND +M V30 END CTAB +M END +> +7 + +> +Z1203579033 + +> +136.151 + +> +0.293 + +> +1 + +> +55.980 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1140 + +> +0.9 + +$$$$ +Compound 8 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 12 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5071 0.0 0 +M V30 3 N -1.3187 2.2725 0.0 0 +M V30 4 C 1.2716 2.2725 0.0 0 +M V30 5 C -1.3305 3.7796 0.0 0 +M V30 6 C 1.2598 3.7796 0.0 0 +M V30 7 C 2.555 1.5307 0.0 0 +M V30 8 N -0.047 4.545 0.0 0 +M V30 9 C 2.5433 4.545 0.0 0 +M V30 10 C 3.8385 2.296 0.0 0 +M V30 11 C 3.8267 3.8032 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 2 9 11 +M V30 11 1 6 8 +M V30 12 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +8 + +> +Z56862715 + +> +146.146 + +> +0.305 + +> +1 + +> +41.460 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL266540 + +> +1.0 + +$$$$ +Compound 9 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 11 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3143 -0.7341 0.0 0 +M V30 3 N -2.6286 0.0236 0.0 0 +M V30 4 C -1.3261 -2.226 0.0 0 +M V30 5 C -2.6404 1.5392 0.0 0 +M V30 6 C -2.6404 -2.9601 0.0 0 +M V30 7 C -0.0355 -2.9601 0.0 0 +M V30 8 C -2.6522 -4.452 0.0 0 +M V30 9 C -0.0473 -4.452 0.0 0 +M V30 10 N -3.9666 -5.1861 0.0 0 +M V30 11 C -1.3616 -5.1861 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 2 8 11 +M V30 11 1 9 11 +M V30 END BOND +M V30 END CTAB +M END +> +9 + +> +Z57040519 + +> +150.178 + +> +-0.046 + +> +2 + +> +55.120 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL81977 + +> +0.86 + +$$$$ +Compound 10 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 11 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3035 0.7516 0.0 0 +M V30 3 N -2.6071 0.0117 0.0 0 +M V30 4 C -1.3153 2.2548 0.0 0 +M V30 5 C -0.0352 3.0064 0.0 0 +M V30 6 C -2.6189 3.0064 0.0 0 +M V30 7 C -0.0469 4.5097 0.0 0 +M V30 8 C -2.6306 4.5097 0.0 0 +M V30 9 C -1.3505 5.2613 0.0 0 +M V30 10 O -1.3623 6.7645 0.0 0 +M V30 11 C -2.6659 7.5162 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 2 7 9 +M V30 9 1 9 10 +M V30 10 1 10 11 +M V30 11 1 8 9 +M V30 END BOND +M V30 END CTAB +M END +> +10 + +> +Z33546492 + +> +151.163 + +> +0.836 + +> +1 + +> +52.320 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL123617 + +> +0.86 + +$$$$ +Compound 11 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 11 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.2896 0.7572 0.0 0 +M V30 3 C 2.5793 0.0236 0.0 0 +M V30 4 C 1.2778 2.2717 0.0 0 +M V30 5 F 2.5675 -1.4671 0.0 0 +M V30 6 C 3.869 0.7809 0.0 0 +M V30 7 C 2.5675 3.0289 0.0 0 +M V30 8 C 3.8571 2.2953 0.0 0 +M V30 9 C 2.5556 4.5434 0.0 0 +M V30 10 O 3.8453 5.3006 0.0 0 +M V30 11 N 1.2423 5.3006 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 1 7 8 +M V30 END BOND +M V30 END CTAB +M END +> +11 + +> +Z82670082 + +> +157.118 + +> +1.098 + +> +1 + +> +43.090 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL125596 + +> +0.88 + +$$$$ +Compound 12 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 12 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5071 0.0 0 +M V30 3 N -1.3187 2.2725 0.0 0 +M V30 4 C 1.2716 2.2725 0.0 0 +M V30 5 N -1.3305 3.7796 0.0 0 +M V30 6 C 1.2598 3.7796 0.0 0 +M V30 7 C 2.555 1.5307 0.0 0 +M V30 8 C -0.047 4.545 0.0 0 +M V30 9 C 2.5433 4.545 0.0 0 +M V30 10 C 3.8385 2.296 0.0 0 +M V30 11 C 3.8267 3.8032 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 1 9 11 +M V30 11 1 6 8 +M V30 12 2 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +12 + +> +Z203045280 + +> +146.146 + +> +1.356 + +> +1 + +> +46.010 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL124706 + +> +1.0 + +$$$$ +Compound 13 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 11 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3035 0.7516 0.0 0 +M V30 3 N -2.6071 0.0117 0.0 0 +M V30 4 C -1.3153 2.2548 0.0 0 +M V30 5 C -2.6189 3.0064 0.0 0 +M V30 6 C -0.0352 3.0064 0.0 0 +M V30 7 C -2.6306 4.5097 0.0 0 +M V30 8 C -0.0469 4.5097 0.0 0 +M V30 9 N -3.9342 5.2613 0.0 0 +M V30 10 C -1.3505 5.2613 0.0 0 +M V30 11 C -1.3623 6.7645 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 2 7 10 +M V30 10 1 10 11 +M V30 11 1 8 10 +M V30 END BOND +M V30 END CTAB +M END +> +13 + +> +Z57912417 + +> +150.178 + +> +0.197 + +> +2 + +> +69.110 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL81977 + +> +0.88 + +$$$$ +Compound 14 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 11 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 O 6.7548 -3.2836 0.0 0 +M V30 3 C 5.4531 -2.5213 0.0 0 +M V30 4 N 4.1514 -3.2601 0.0 0 +M V30 5 C 5.4414 -1.0202 0.0 0 +M V30 6 C 6.7197 -0.2697 0.0 0 +M V30 7 C 4.1397 -0.2697 0.0 0 +M V30 8 C 6.7079 1.2313 0.0 0 +M V30 9 C 4.1279 1.2313 0.0 0 +M V30 10 C 5.4062 1.9936 0.0 0 +M V30 11 N 5.3945 3.4947 0.0 0 +M V30 12 C 4.0928 4.2569 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 1 3 4 +M V30 3 1 3 5 +M V30 4 2 5 6 +M V30 5 1 5 7 +M V30 6 1 6 8 +M V30 7 2 7 9 +M V30 8 2 8 10 +M V30 9 1 10 11 +M V30 10 1 11 12 +M V30 11 1 9 10 +M V30 END BOND +M V30 END CTAB +M END +> +14 + +> +Z744754554 + +> +150.178 + +> +0.500 + +> +2 + +> +55.120 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL332444 + +> +0.86 + +$$$$ +Compound 15 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 12 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4905 0.0 0 +M V30 3 C -1.3249 -2.2358 0.0 0 +M V30 4 C 1.2776 -2.2358 0.0 0 +M V30 5 C -1.3368 -3.7264 0.0 0 +M V30 6 C -2.7682 -1.7626 0.0 0 +M V30 7 C 1.2658 -3.7264 0.0 0 +M V30 8 N -2.78 -4.176 0.0 0 +M V30 9 C -0.0473 -4.4717 0.0 0 +M V30 10 N -3.6673 -2.9693 0.0 0 +M V30 11 C -3.2532 -5.5956 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 8 11 +M V30 11 1 7 9 +M V30 12 1 8 10 +M V30 END BOND +M V30 END CTAB +M END +> +15 + +> +Z336028554 + +> +150.178 + +> +0.298 + +> +0 + +> +34.890 + +> +0 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1981107 + +> +1.0 + +$$$$ +Compound 16 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 12 0 0 0 +M V30 BEGIN ATOM +M V30 1 N 0.0 0.0 0.0 0 +M V30 2 C -1.307 0.7536 0.0 0 +M V30 3 C -0.0117 -1.4836 0.0 0 +M V30 4 N -1.3188 2.2608 0.0 0 +M V30 5 C -2.614 0.0235 0.0 0 +M V30 6 N -1.3188 -2.2137 0.0 0 +M V30 7 C -2.6258 3.0144 0.0 0 +M V30 8 N -4.0506 0.4945 0.0 0 +M V30 9 C -2.6258 -1.4601 0.0 0 +M V30 10 C -4.9455 -0.7065 0.0 0 +M V30 11 N -4.0623 -1.9075 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 1 6 9 +M V30 12 2 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +16 + +> +Z372962306 + +> +149.153 + +> +0.682 + +> +2 + +> +66.490 + +> +1 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL407391 + +> +0.91 + +$$$$ +Compound 17 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 11 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.2952 -0.7367 0.0 0 +M V30 3 C 2.5904 0.0237 0.0 0 +M V30 4 C 1.2833 -2.2339 0.0 0 +M V30 5 C 3.8857 -0.7129 0.0 0 +M V30 6 C 2.5786 1.5447 0.0 0 +M V30 7 C 2.5786 -2.9707 0.0 0 +M V30 8 C 3.8738 -2.2102 0.0 0 +M V30 9 C 2.5667 -4.4679 0.0 0 +M V30 10 O 3.8619 -5.2166 0.0 0 +M V30 11 N 1.2477 -5.2166 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 7 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 1 7 8 +M V30 END BOND +M V30 END CTAB +M END +> +17 + +> +Z116821650 + +> +153.154 + +> +1.465 + +> +1 + +> +43.090 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL125759 + +> +0.87 + +$$$$ +Compound 18 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 11 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -1.3041 0.7519 0.0 0 +M V30 3 C -2.6083 0.0234 0.0 0 +M V30 4 C -1.3159 2.2558 0.0 0 +M V30 5 C -3.9124 0.7754 0.0 0 +M V30 6 C -2.62 3.0077 0.0 0 +M V30 7 F -5.2166 0.0469 0.0 0 +M V30 8 C -3.9242 2.2793 0.0 0 +M V30 9 C -2.6318 4.5116 0.0 0 +M V30 10 O -1.3511 5.2636 0.0 0 +M V30 11 N -3.9359 5.2636 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 1 6 8 +M V30 END BOND +M V30 END CTAB +M END +> +18 + +> +Z33545379 + +> +157.118 + +> +1.168 + +> +1 + +> +43.090 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL125596 + +> +0.88 + +$$$$ +Compound 19 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 12 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5071 0.0 0 +M V30 3 N -1.3187 2.2725 0.0 0 +M V30 4 C 1.2716 2.2725 0.0 0 +M V30 5 C -1.3305 3.7796 0.0 0 +M V30 6 C 1.2598 3.7796 0.0 0 +M V30 7 C 2.555 1.5307 0.0 0 +M V30 8 C -0.047 4.545 0.0 0 +M V30 9 C 2.5433 4.545 0.0 0 +M V30 10 C 3.8385 2.296 0.0 0 +M V30 11 C 3.8267 3.8032 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 2 9 11 +M V30 11 1 6 8 +M V30 12 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +19 + +> +Z56801375 + +> +147.174 + +> +0.996 + +> +1 + +> +29.100 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1688212 + +> +1.0 + +$$$$ +Compound 20 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 12 0 0 0 +M V30 BEGIN ATOM +M V30 1 N 0.0 0.0 0.0 0 +M V30 2 C -1.307 0.7536 0.0 0 +M V30 3 C -0.0117 -1.4836 0.0 0 +M V30 4 N -1.3188 2.2608 0.0 0 +M V30 5 C -2.614 0.0235 0.0 0 +M V30 6 N -1.3188 -2.2137 0.0 0 +M V30 7 C -2.6258 3.0144 0.0 0 +M V30 8 C -2.6258 -1.4601 0.0 0 +M V30 9 C -4.0506 0.4945 0.0 0 +M V30 10 N -4.0623 -1.9075 0.0 0 +M V30 11 N -4.9455 -0.7065 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 1 6 8 +M V30 12 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +20 + +> +Z57101324 + +> +149.153 + +> +0.822 + +> +2 + +> +66.490 + +> +1 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1969787 + +> +0.91 + +$$$$ +Compound 21 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 12 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5071 0.0 0 +M V30 3 N -1.3187 2.2725 0.0 0 +M V30 4 C 1.2716 2.2725 0.0 0 +M V30 5 C -1.3305 3.7796 0.0 0 +M V30 6 C 1.2598 3.7796 0.0 0 +M V30 7 C 2.555 1.5307 0.0 0 +M V30 8 C -0.047 4.545 0.0 0 +M V30 9 C 2.5433 4.545 0.0 0 +M V30 10 C 3.8385 2.296 0.0 0 +M V30 11 N 3.8267 3.8032 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 1 9 11 +M V30 11 1 6 8 +M V30 12 2 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +21 + +> +Z1198157282 + +> +146.146 + +> +1.264 + +> +1 + +> +46.010 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL339695 + +> +0.93 + +$$$$ +Compound 22 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 12 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.4241 -0.4509 0.0 0 +M V30 3 C -0.9019 -1.2105 0.0 0 +M V30 4 N 2.7178 0.3204 0.0 0 +M V30 5 C 1.4123 -1.9464 0.0 0 +M V30 6 C -0.0237 -2.4211 0.0 0 +M V30 7 C 4.0114 -0.4272 0.0 0 +M V30 8 C 2.7059 -2.6941 0.0 0 +M V30 9 C -0.4984 -3.8453 0.0 0 +M V30 10 N 3.9996 -1.9226 0.0 0 +M V30 11 O 2.6941 -4.1895 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 5 6 +M V30 12 1 8 10 +M V30 END BOND +M V30 END CTAB +M END +> +22 + +> +Z1198152023 + +> +166.200 + +> +0.623 + +> +1 + +> +41.460 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL2432028 + +> +0.89 + +$$$$ +Compound 23 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 11 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2878 0.7561 0.0 0 +M V30 3 C 1.276 2.2684 0.0 0 +M V30 4 C 2.5756 0.0236 0.0 0 +M V30 5 C 2.5638 3.0246 0.0 0 +M V30 6 C 3.8634 0.7797 0.0 0 +M V30 7 C 2.552 4.5369 0.0 0 +M V30 8 C 3.8516 2.292 0.0 0 +M V30 9 O 3.8398 5.293 0.0 0 +M V30 10 N 1.2405 5.293 0.0 0 +M V30 11 C 1.2287 6.8053 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 10 11 +M V30 11 1 6 8 +M V30 END BOND +M V30 END CTAB +M END +> +23 + +> +Z32016383 + +> +169.608 + +> +1.776 + +> +1 + +> +29.100 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL419245 + +> +0.85 + +$$$$ +Compound 24 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 12 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5071 0.0 0 +M V30 3 N -1.3187 2.2725 0.0 0 +M V30 4 C 1.2716 2.2725 0.0 0 +M V30 5 C -1.3305 3.7796 0.0 0 +M V30 6 N 2.555 1.5307 0.0 0 +M V30 7 C 1.2598 3.7796 0.0 0 +M V30 8 C -0.047 4.545 0.0 0 +M V30 9 C 3.8385 2.296 0.0 0 +M V30 10 C 2.5433 4.545 0.0 0 +M V30 11 C 3.8267 3.8032 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 2 9 11 +M V30 11 1 7 8 +M V30 12 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +24 + +> +Z1201626988 + +> +146.146 + +> +1.474 + +> +1 + +> +46.010 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL107738 + +> +0.94 + +$$$$ +Compound 25 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 12 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4905 0.0 0 +M V30 3 C -1.3249 -2.2358 0.0 0 +M V30 4 C 1.2776 -2.2358 0.0 0 +M V30 5 N -2.7682 -1.7626 0.0 0 +M V30 6 C -1.3368 -3.7264 0.0 0 +M V30 7 C 1.2658 -3.7264 0.0 0 +M V30 8 N -3.6673 -2.9693 0.0 0 +M V30 9 C -2.78 -4.176 0.0 0 +M V30 10 C -0.0473 -4.4717 0.0 0 +M V30 11 N -3.2532 -5.5956 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 6 10 +M V30 10 1 9 11 +M V30 11 1 7 10 +M V30 12 2 8 9 +M V30 END BOND +M V30 END CTAB +M END +> +25 + +> +Z1201624116 + +> +167.596 + +> +2.085 + +> +2 + +> +54.700 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1980161 + +> +1.0 + +$$$$ +Compound 26 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 12 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 N -0.8962 1.2264 0.0 0 +M V30 3 C 1.4151 0.4717 0.0 0 +M V30 4 C -0.0235 2.4528 0.0 0 +M V30 5 C 1.4033 1.9811 0.0 0 +M V30 6 C 2.7005 -0.2594 0.0 0 +M V30 7 N -0.4952 3.8915 0.0 0 +M V30 8 C 2.6887 2.7358 0.0 0 +M V30 9 C 3.9858 0.4952 0.0 0 +M V30 10 C 3.9741 2.0047 0.0 0 +M V30 11 C 2.6769 4.2453 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 2 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 2 8 10 +M V30 10 1 8 11 +M V30 11 1 4 5 +M V30 12 1 9 10 +M V30 END BOND +M V30 END CTAB +M END +> +26 + +> +Z1203653600 + +> +148.162 + +> +1.652 + +> +1 + +> +52.050 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1980376 + +> +0.89 + +$$$$ +Compound 27 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 12 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.3036 0.7516 0.0 0 +M V30 3 C -1.3153 2.2548 0.0 0 +M V30 4 C -2.6072 0.0234 0.0 0 +M V30 5 Cl -0.0352 3.0065 0.0 0 +M V30 6 C -2.6189 3.0065 0.0 0 +M V30 7 C -3.9108 0.7751 0.0 0 +M V30 8 C -3.9225 2.2783 0.0 0 +M V30 9 N -5.3436 0.3288 0.0 0 +M V30 10 N -5.3553 2.7481 0.0 0 +M V30 11 N -6.2361 1.5502 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 1 7 8 +M V30 12 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +27 + +> +Z1251354024 + +> +188.014 + +> +2.714 + +> +1 + +> +41.570 + +> +0 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1996923 + +> +1.0 + +$$$$ +Compound 28 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 11 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.3045 0.7521 0.0 0 +M V30 3 C -2.609 0.0235 0.0 0 +M V30 4 C -1.3162 2.2564 0.0 0 +M V30 5 C -3.9135 0.7756 0.0 0 +M V30 6 C -2.6208 3.0086 0.0 0 +M V30 7 C -5.2181 0.047 0.0 0 +M V30 8 C -3.9253 2.2799 0.0 0 +M V30 9 C -2.6325 4.5129 0.0 0 +M V30 10 O -5.2298 -1.4338 0.0 0 +M V30 11 N -6.5226 0.7991 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 7 11 +M V30 11 1 6 8 +M V30 END BOND +M V30 END CTAB +M END +> +28 + +> +Z1255368207 + +> +169.608 + +> +2.035 + +> +1 + +> +43.090 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL419245 + +> +0.9 + +$$$$ +Compound 29 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 12 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.4226 -0.4505 0.0 0 +M V30 3 C -0.901 -1.2092 0.0 0 +M V30 4 C 2.7149 0.3082 0.0 0 +M V30 5 C 1.4108 -1.9443 0.0 0 +M V30 6 C -0.0237 -2.4185 0.0 0 +M V30 7 O 2.703 1.8257 0.0 0 +M V30 8 N 4.0071 -0.4268 0.0 0 +M V30 9 N 2.703 -2.6793 0.0 0 +M V30 10 C 1.3871 2.5845 0.0 0 +M V30 11 C 3.9953 -1.9206 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 5 6 +M V30 12 1 9 11 +M V30 END BOND +M V30 END CTAB +M END +> +29 + +> +Z196125830 + +> +166.200 + +> +2.035 + +> +0 + +> +35.010 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1891423 + +> +0.86 + +$$$$ +Compound 30 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 12 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.49 0.0 0 +M V30 3 C -1.3245 -2.2232 0.0 0 +M V30 4 C 1.2772 -2.2232 0.0 0 +M V30 5 C -1.3363 -3.7133 0.0 0 +M V30 6 C -2.7672 -1.7502 0.0 0 +M V30 7 C 1.2653 -3.7133 0.0 0 +M V30 8 N -2.7791 -4.1627 0.0 0 +M V30 9 C -0.0473 -4.4584 0.0 0 +M V30 10 N -3.666 -2.9565 0.0 0 +M V30 11 C -5.1798 -2.9446 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 10 11 +M V30 11 1 7 9 +M V30 12 1 8 10 +M V30 END BOND +M V30 END CTAB +M END +> +30 + +> +Z1255491588 + +> +150.178 + +> +0.298 + +> +0 + +> +34.890 + +> +0 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1981107 + +> +0.91 + +$$$$ +Compound 31 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 11 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.5133 0.0 0 +M V30 3 N -1.3242 2.27 0.0 0 +M V30 4 C 1.2769 2.27 0.0 0 +M V30 5 C 2.5656 1.537 0.0 0 +M V30 6 C 1.265 3.7834 0.0 0 +M V30 7 C 3.8544 2.2937 0.0 0 +M V30 8 C 2.5538 4.5401 0.0 0 +M V30 9 N 5.1431 1.5606 0.0 0 +M V30 10 C 3.8425 3.8071 0.0 0 +M V30 11 C 2.542 6.0535 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 1 8 10 +M V30 END BOND +M V30 END CTAB +M END +> +31 + +> +Z1255434530 + +> +150.178 + +> +0.247 + +> +2 + +> +69.110 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL81977 + +> +0.9 + +$$$$ +Compound 32 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 12 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4925 0.0 0 +M V30 3 C -1.3266 -2.2269 0.0 0 +M V30 4 C 1.2793 -2.2269 0.0 0 +M V30 5 C -1.3385 -3.7194 0.0 0 +M V30 6 C -2.7718 -1.7531 0.0 0 +M V30 7 C 1.2674 -3.7194 0.0 0 +M V30 8 C -2.7836 -4.1696 0.0 0 +M V30 9 C -0.0473 -4.4539 0.0 0 +M V30 10 N -3.6721 -2.9613 0.0 0 +M V30 11 O -3.2575 -5.591 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 2 8 11 +M V30 11 1 7 9 +M V30 12 1 8 10 +M V30 END BOND +M V30 END CTAB +M END +> +32 + +> +Z1258930120 + +> +212.043 + +> +1.902 + +> +1 + +> +29.100 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1984500 + +> +1.0 + +$$$$ +Compound 33 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 12 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C -1.3091 -0.7312 0.0 0 +M V30 3 C -2.6183 0.0235 0.0 0 +M V30 4 C -1.3209 -2.2173 0.0 0 +M V30 5 C -3.9275 -0.7076 0.0 0 +M V30 6 C -2.6301 -2.9485 0.0 0 +M V30 7 C -3.9393 -2.1937 0.0 0 +M V30 8 C -5.3664 -0.2358 0.0 0 +M V30 9 C -5.3782 -2.6419 0.0 0 +M V30 10 N -6.2627 -1.4389 0.0 0 +M V30 11 O -5.8499 -4.0572 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 1 6 7 +M V30 12 1 9 10 +M V30 END BOND +M V30 END CTAB +M END +> +33 + +> +Z1269136562 + +> +212.043 + +> +1.902 + +> +1 + +> +29.100 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1984500 + +> +0.89 + +$$$$ +Compound 34 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 12 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C -1.3075 -0.7303 0.0 0 +M V30 3 C -2.615 0.0235 0.0 0 +M V30 4 C -1.3193 -2.2145 0.0 0 +M V30 5 C -3.9226 -0.7067 0.0 0 +M V30 6 C -2.6268 -2.9449 0.0 0 +M V30 7 C -5.3597 -0.2355 0.0 0 +M V30 8 C -3.9344 -2.191 0.0 0 +M V30 9 O -5.8309 1.2015 0.0 0 +M V30 10 N -6.2549 -1.4371 0.0 0 +M V30 11 C -5.3715 -2.6386 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 1 6 8 +M V30 12 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +34 + +> +Z1269144955 + +> +212.043 + +> +1.902 + +> +1 + +> +29.100 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1984500 + +> +0.9 + +$$$$ +Compound 35 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 12 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C -1.3014 0.762 0.0 0 +M V30 3 C -1.3131 2.2628 0.0 0 +M V30 4 C -2.6028 0.0234 0.0 0 +M V30 5 N -2.6145 3.0249 0.0 0 +M V30 6 C -3.9042 0.7855 0.0 0 +M V30 7 C -3.9159 2.2862 0.0 0 +M V30 8 C -5.3346 0.34 0.0 0 +M V30 9 N -5.3463 2.7552 0.0 0 +M V30 10 C -6.2257 1.5593 0.0 0 +M V30 11 C -5.8036 -1.0669 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 6 8 +M V30 8 1 7 9 +M V30 9 2 8 10 +M V30 10 1 8 11 +M V30 11 1 6 7 +M V30 12 1 9 10 +M V30 END BOND +M V30 END CTAB +M END +> +35 + +> +Z1269219081 + +> +211.059 + +> +2.621 + +> +1 + +> +28.680 + +> +0 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1992634 + +> +0.85 + +$$$$ +Compound 36 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 11 12 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4906 0.0 0 +M V30 3 C -1.325 -2.2241 0.0 0 +M V30 4 C 1.2777 -2.2241 0.0 0 +M V30 5 C -2.7683 -1.7509 0.0 0 +M V30 6 C -1.3368 -3.7148 0.0 0 +M V30 7 C 1.2658 -3.7148 0.0 0 +M V30 8 O -3.2416 -0.3075 0.0 0 +M V30 9 N -3.6675 -2.9576 0.0 0 +M V30 10 C -0.0473 -4.4483 0.0 0 +M V30 11 C -2.7802 -4.1644 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 1 7 10 +M V30 12 1 9 11 +M V30 END BOND +M V30 END CTAB +M END +> +36 + +> +Z1269173290 + +> +212.043 + +> +1.902 + +> +1 + +> +29.100 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1984500 + +> +0.96 + +$$$$ +Compound 37 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2896 0.769 0.0 0 +M V30 3 C 2.5792 0.0236 0.0 0 +M V30 4 C 1.2777 2.2834 0.0 0 +M V30 5 C 3.8688 0.7927 0.0 0 +M V30 6 C 2.5674 3.0525 0.0 0 +M V30 7 C 5.1585 0.0473 0.0 0 +M V30 8 C 3.857 2.3071 0.0 0 +M V30 9 O 5.1466 -1.4434 0.0 0 +M V30 10 N 6.4481 0.8163 0.0 0 +M V30 11 N 5.1466 3.0761 0.0 0 +M V30 12 C 6.4363 2.3307 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 1 10 12 +M V30 12 1 6 8 +M V30 13 2 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +37 + +> +Z57980339 + +> +180.591 + +> +1.076 + +> +1 + +> +41.460 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL266540 + +> +0.87 + +$$$$ +Compound 38 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O -1.3142 0.7696 0.0 0 +M V30 3 O 0.6038 1.3853 0.0 0 +M V30 4 N -0.8998 -1.2077 0.0 0 +M V30 5 C 1.4208 -0.4499 0.0 0 +M V30 6 C -0.0236 -2.4154 0.0 0 +M V30 7 C 1.409 -1.9418 0.0 0 +M V30 8 C 2.7114 0.3196 0.0 0 +M V30 9 O -0.4972 -3.8362 0.0 0 +M V30 10 C 2.6996 -2.6877 0.0 0 +M V30 11 C 4.002 -0.4262 0.0 0 +M V30 12 C 3.9902 -1.9181 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 2 10 12 +M V30 12 1 6 7 +M V30 13 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +38 + +> +Z256708526 + +> +183.185 + +> +0.718 + +> +1 + +> +63.240 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1967612 + +> +0.9 + +$$$$ +Compound 39 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.506 0.0 0 +M V30 3 N -1.3177 2.2708 0.0 0 +M V30 4 C 1.2707 2.2708 0.0 0 +M V30 5 C -1.3295 3.7768 0.0 0 +M V30 6 C 1.2589 3.7768 0.0 0 +M V30 7 C 2.5531 1.5295 0.0 0 +M V30 8 N -0.047 4.5416 0.0 0 +M V30 9 C -2.6355 4.5416 0.0 0 +M V30 10 C 2.5414 4.5416 0.0 0 +M V30 11 C 3.8356 2.2943 0.0 0 +M V30 12 C 3.8238 3.8003 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 10 12 +M V30 12 1 6 8 +M V30 13 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +39 + +> +Z56895688 + +> +160.173 + +> +0.804 + +> +1 + +> +41.460 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL395092 + +> +1.0 + +$$$$ +Compound 40 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2896 0.769 0.0 0 +M V30 3 C 2.5792 0.0236 0.0 0 +M V30 4 C 1.2778 2.2834 0.0 0 +M V30 5 C 3.8689 0.7927 0.0 0 +M V30 6 C 2.5674 3.0407 0.0 0 +M V30 7 N 5.1585 0.0473 0.0 0 +M V30 8 C 3.857 2.3071 0.0 0 +M V30 9 C 6.4481 0.8163 0.0 0 +M V30 10 C 5.1467 3.0643 0.0 0 +M V30 11 N 6.4363 2.3308 0.0 0 +M V30 12 O 5.1348 4.5788 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 1 6 8 +M V30 13 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +40 + +> +Z57683681 + +> +180.591 + +> +1.076 + +> +1 + +> +41.460 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL266540 + +> +0.87 + +$$$$ +Compound 41 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C 1.2896 0.769 0.0 0 +M V30 3 C 2.5792 0.0236 0.0 0 +M V30 4 C 1.2778 2.2834 0.0 0 +M V30 5 C 3.8689 0.7927 0.0 0 +M V30 6 C 2.5674 3.0407 0.0 0 +M V30 7 N 5.1585 0.0473 0.0 0 +M V30 8 C 3.857 2.3071 0.0 0 +M V30 9 C 6.4481 0.8163 0.0 0 +M V30 10 C 5.1467 3.0643 0.0 0 +M V30 11 N 6.4363 2.3308 0.0 0 +M V30 12 O 5.1348 4.5788 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 1 6 8 +M V30 13 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +41 + +> +Z152448286 + +> +225.042 + +> +1.226 + +> +1 + +> +41.460 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL266540 + +> +0.87 + +$$$$ +Compound 42 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.2896 0.769 0.0 0 +M V30 3 C 2.5792 0.0236 0.0 0 +M V30 4 C 1.2778 2.2834 0.0 0 +M V30 5 C 3.8689 0.7927 0.0 0 +M V30 6 C 2.5674 3.0407 0.0 0 +M V30 7 N 5.1585 0.0473 0.0 0 +M V30 8 C 3.857 2.3071 0.0 0 +M V30 9 C 6.4481 0.8163 0.0 0 +M V30 10 C 5.1467 3.0643 0.0 0 +M V30 11 N 6.4363 2.3308 0.0 0 +M V30 12 O 5.1348 4.5788 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 1 6 8 +M V30 13 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +42 + +> +Z133595436 + +> +164.137 + +> +0.506 + +> +1 + +> +41.460 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL266540 + +> +0.87 + +$$$$ +Compound 43 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2896 0.769 0.0 0 +M V30 3 N 1.2778 2.2834 0.0 0 +M V30 4 N 2.5792 0.0236 0.0 0 +M V30 5 C 2.5674 3.0407 0.0 0 +M V30 6 C 3.8689 0.7927 0.0 0 +M V30 7 O 2.5556 4.5551 0.0 0 +M V30 8 C 3.857 2.3071 0.0 0 +M V30 9 C 5.1585 0.0473 0.0 0 +M V30 10 C 5.1467 3.0643 0.0 0 +M V30 11 C 6.4481 0.8163 0.0 0 +M V30 12 C 6.4363 2.3308 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 2 10 12 +M V30 12 2 6 8 +M V30 13 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +43 + +> +Z57177786 + +> +162.145 + +> +0.538 + +> +2 + +> +58.200 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL421646 + +> +1.0 + +$$$$ +Compound 44 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4975 0.0 0 +M V30 3 N -1.3311 -2.2344 0.0 0 +M V30 4 C 1.2836 -2.2344 0.0 0 +M V30 5 N -1.343 -3.7319 0.0 0 +M V30 6 C 1.2717 -3.7319 0.0 0 +M V30 7 C 2.579 -1.4737 0.0 0 +M V30 8 C -0.0475 -4.4807 0.0 0 +M V30 9 C 2.5672 -4.4807 0.0 0 +M V30 10 C 3.8745 -2.2106 0.0 0 +M V30 11 C -0.0594 -5.9782 0.0 0 +M V30 12 C 3.8626 -3.7081 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 6 8 +M V30 13 1 10 12 +M V30 END BOND +M V30 END CTAB +M END +> +44 + +> +Z56877312 + +> +160.173 + +> +0.654 + +> +1 + +> +41.460 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL66761 + +> +0.86 + +$$$$ +Compound 45 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C 1.2896 0.769 0.0 0 +M V30 3 C 2.5792 0.0236 0.0 0 +M V30 4 C 1.2777 2.2834 0.0 0 +M V30 5 C 3.8688 0.7927 0.0 0 +M V30 6 C 2.5674 3.0525 0.0 0 +M V30 7 C 5.1585 0.0473 0.0 0 +M V30 8 C 3.857 2.3071 0.0 0 +M V30 9 O 5.1466 -1.4434 0.0 0 +M V30 10 N 6.4481 0.8163 0.0 0 +M V30 11 N 5.1466 3.0761 0.0 0 +M V30 12 C 6.4363 2.3307 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 1 10 12 +M V30 12 1 6 8 +M V30 13 2 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +45 + +> +Z56893149 + +> +225.042 + +> +1.226 + +> +1 + +> +41.460 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL562488 + +> +1.0 + +$$$$ +Compound 46 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5006 0.0 0 +M V30 3 N 1.2661 2.2627 0.0 0 +M V30 4 C -1.313 2.2627 0.0 0 +M V30 5 C 1.2544 3.7634 0.0 0 +M V30 6 C -1.3248 3.7634 0.0 0 +M V30 7 C -2.6144 1.5241 0.0 0 +M V30 8 N -0.0468 4.5254 0.0 0 +M V30 9 C -2.6261 4.5254 0.0 0 +M V30 10 C -3.9158 2.2861 0.0 0 +M V30 11 C -3.9275 3.7868 0.0 0 +M V30 12 N -5.2171 1.5475 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 2 9 11 +M V30 11 1 10 12 +M V30 12 1 6 8 +M V30 13 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +46 + +> +Z203240798 + +> +161.161 + +> +-0.116 + +> +2 + +> +67.480 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL266540 + +> +0.92 + +$$$$ +Compound 47 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5084 0.0 0 +M V30 3 C 1.2727 2.2744 0.0 0 +M V30 4 C -1.3198 2.2744 0.0 0 +M V30 5 N 2.5572 1.532 0.0 0 +M V30 6 C 1.2609 3.7828 0.0 0 +M V30 7 C -1.3316 3.7828 0.0 0 +M V30 8 C 3.8417 2.298 0.0 0 +M V30 9 C 2.5454 4.537 0.0 0 +M V30 10 C -0.0471 4.537 0.0 0 +M V30 11 N 3.83 3.8064 0.0 0 +M V30 12 O 2.5336 6.0455 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 6 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 7 10 +M V30 13 1 9 11 +M V30 END BOND +M V30 END CTAB +M END +> +47 + +> +Z440678062 + +> +225.042 + +> +1.226 + +> +1 + +> +41.460 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL562488 + +> +0.92 + +$$$$ +Compound 48 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5084 0.0 0 +M V30 3 C 1.2727 2.2744 0.0 0 +M V30 4 C -1.3198 2.2744 0.0 0 +M V30 5 C 2.5572 1.5319 0.0 0 +M V30 6 C 1.2609 3.7828 0.0 0 +M V30 7 C -1.3316 3.7828 0.0 0 +M V30 8 O 2.5454 0.0471 0.0 0 +M V30 9 N 3.8417 2.2979 0.0 0 +M V30 10 N 2.5454 4.5488 0.0 0 +M V30 11 C -0.0471 4.5488 0.0 0 +M V30 12 C 3.8299 3.8063 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 6 11 +M V30 11 1 9 12 +M V30 12 1 7 11 +M V30 13 2 10 12 +M V30 END BOND +M V30 END CTAB +M END +> +48 + +> +Z818727146 + +> +164.137 + +> +0.506 + +> +1 + +> +41.460 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1949843 + +> +0.85 + +$$$$ +Compound 49 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.2896 0.769 0.0 0 +M V30 3 C 2.5792 0.0236 0.0 0 +M V30 4 C 1.2777 2.2834 0.0 0 +M V30 5 C 3.8688 0.7927 0.0 0 +M V30 6 C 2.5674 3.0525 0.0 0 +M V30 7 C 5.1585 0.0473 0.0 0 +M V30 8 C 3.857 2.3071 0.0 0 +M V30 9 O 5.1466 -1.4434 0.0 0 +M V30 10 N 6.4481 0.8163 0.0 0 +M V30 11 N 5.1466 3.0761 0.0 0 +M V30 12 C 6.4363 2.3307 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 1 10 12 +M V30 12 1 6 8 +M V30 13 2 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +49 + +> +Z728795402 + +> +164.137 + +> +0.506 + +> +1 + +> +41.460 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL266540 + +> +0.88 + +$$$$ +Compound 50 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 N 0.0 0.0 0.0 0 +M V30 2 C -1.3078 0.754 0.0 0 +M V30 3 C -0.0117 -1.4845 0.0 0 +M V30 4 N -1.3195 2.2621 0.0 0 +M V30 5 C -2.6156 0.0235 0.0 0 +M V30 6 N -1.3195 -2.215 0.0 0 +M V30 7 C -2.6273 3.0162 0.0 0 +M V30 8 C -0.0353 3.0162 0.0 0 +M V30 9 N -4.053 0.4948 0.0 0 +M V30 10 C -2.6273 -1.4609 0.0 0 +M V30 11 C -4.9484 -0.7069 0.0 0 +M V30 12 N -4.0648 -1.9086 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 4 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 2 5 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 1 6 10 +M V30 13 2 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +50 + +> +Z372962146 + +> +163.180 + +> +0.738 + +> +1 + +> +57.700 + +> +1 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL407391 + +> +1.0 + +$$$$ +Compound 51 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.506 0.0 0 +M V30 3 N -1.3177 2.2708 0.0 0 +M V30 4 C 1.2707 2.2708 0.0 0 +M V30 5 C -1.3295 3.7768 0.0 0 +M V30 6 C 1.2589 3.7768 0.0 0 +M V30 7 C 2.5531 1.5295 0.0 0 +M V30 8 N -0.047 4.5416 0.0 0 +M V30 9 N -2.6355 4.5416 0.0 0 +M V30 10 C 2.5414 4.5416 0.0 0 +M V30 11 C 3.8356 2.2943 0.0 0 +M V30 12 C 3.8238 3.8003 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 10 12 +M V30 12 1 6 8 +M V30 13 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +51 + +> +Z840267872 + +> +161.161 + +> +0.153 + +> +2 + +> +67.480 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL266540 + +> +0.87 + +$$$$ +Compound 52 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4912 0.0 0 +M V30 3 N 1.2782 -2.225 0.0 0 +M V30 4 C -1.3255 -2.225 0.0 0 +M V30 5 C 1.2663 -3.7162 0.0 0 +M V30 6 C -1.3373 -3.7162 0.0 0 +M V30 7 C -2.6392 -1.4675 0.0 0 +M V30 8 N -0.0473 -4.4618 0.0 0 +M V30 9 C -2.651 -4.4618 0.0 0 +M V30 10 C -3.9529 -2.2013 0.0 0 +M V30 11 C -3.9648 -3.6925 0.0 0 +M V30 12 C -2.6629 -5.9531 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 2 9 11 +M V30 11 1 9 12 +M V30 12 1 6 8 +M V30 13 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +52 + +> +Z314790678 + +> +160.173 + +> +0.804 + +> +1 + +> +41.460 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL266540 + +> +0.93 + +$$$$ +Compound 53 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 O 9.2966 -2.5354 0.0 0 +M V30 3 C 9.2848 -1.0329 0.0 0 +M V30 4 N 10.5643 -0.2817 0.0 0 +M V30 5 C 7.9819 -0.2699 0.0 0 +M V30 6 C 10.5525 1.2207 0.0 0 +M V30 7 C 7.9701 1.2325 0.0 0 +M V30 8 C 6.6789 -1.0094 0.0 0 +M V30 9 C 9.2496 1.9837 0.0 0 +M V30 10 C 6.6672 1.9837 0.0 0 +M V30 11 C 5.376 -0.2582 0.0 0 +M V30 12 C 5.3643 1.2559 0.0 0 +M V30 13 N 4.0731 -0.986 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 1 3 4 +M V30 3 1 3 5 +M V30 4 1 4 6 +M V30 5 2 5 7 +M V30 6 1 5 8 +M V30 7 1 6 9 +M V30 8 1 7 10 +M V30 9 2 8 11 +M V30 10 2 10 12 +M V30 11 1 11 13 +M V30 12 1 7 9 +M V30 13 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +53 + +> +Z1269638456 + +> +162.189 + +> +0.089 + +> +2 + +> +55.120 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL594759 + +> +0.93 + +$$$$ +Compound 54 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4897 0.0 0 +M V30 3 N 1.2769 -2.2228 0.0 0 +M V30 4 C -1.3242 -2.2228 0.0 0 +M V30 5 C 1.2651 -3.7125 0.0 0 +M V30 6 C -1.336 -3.7125 0.0 0 +M V30 7 C -2.6366 -1.4661 0.0 0 +M V30 8 C -0.0472 -4.4574 0.0 0 +M V30 9 C -2.6484 -4.4574 0.0 0 +M V30 10 C -3.949 -2.1991 0.0 0 +M V30 11 C -3.9608 -3.6889 0.0 0 +M V30 12 N -5.2732 -4.4338 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 2 9 11 +M V30 11 1 11 12 +M V30 12 1 6 8 +M V30 13 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +54 + +> +Z1198280382 + +> +162.189 + +> +0.089 + +> +2 + +> +55.120 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL594759 + +> +0.92 + +$$$$ +Compound 55 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.2997 0.7611 0.0 0 +M V30 3 C -2.5994 0.0234 0.0 0 +M V30 4 C -1.3114 2.2599 0.0 0 +M V30 5 C -3.8992 0.7845 0.0 0 +M V30 6 C -2.6111 3.021 0.0 0 +M V30 7 N -5.1989 0.0468 0.0 0 +M V30 8 C -3.9109 2.2833 0.0 0 +M V30 9 C -6.4987 0.8079 0.0 0 +M V30 10 C -5.2106 3.0444 0.0 0 +M V30 11 O -7.7984 0.0702 0.0 0 +M V30 12 C -6.5104 2.3067 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 7 9 +M V30 9 2 8 10 +M V30 10 1 9 11 +M V30 11 2 9 12 +M V30 12 1 6 8 +M V30 13 1 10 12 +M V30 END BOND +M V30 END CTAB +M END +> +55 + +> +Z1198179771 + +> +179.603 + +> +3.126 + +> +1 + +> +33.120 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL298450 + +> +0.86 + +$$$$ +Compound 56 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5084 0.0 0 +M V30 3 C 1.2727 2.2744 0.0 0 +M V30 4 C -1.3198 2.2744 0.0 0 +M V30 5 N 2.5572 1.532 0.0 0 +M V30 6 C 1.2609 3.7828 0.0 0 +M V30 7 C -1.3316 3.7828 0.0 0 +M V30 8 C 3.8417 2.298 0.0 0 +M V30 9 C 2.5454 4.537 0.0 0 +M V30 10 C -0.0471 4.537 0.0 0 +M V30 11 N 3.83 3.8064 0.0 0 +M V30 12 O 2.5336 6.0455 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 6 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 7 10 +M V30 13 1 9 11 +M V30 END BOND +M V30 END CTAB +M END +> +56 + +> +Z1198266474 + +> +164.137 + +> +0.506 + +> +1 + +> +41.460 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1949843 + +> +0.92 + +$$$$ +Compound 57 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5084 0.0 0 +M V30 3 C 1.2727 2.2744 0.0 0 +M V30 4 C -1.3198 2.2744 0.0 0 +M V30 5 N 2.5572 1.532 0.0 0 +M V30 6 C 1.2609 3.7828 0.0 0 +M V30 7 C -1.3316 3.7828 0.0 0 +M V30 8 C 3.8417 2.298 0.0 0 +M V30 9 C 2.5454 4.537 0.0 0 +M V30 10 C -0.0471 4.537 0.0 0 +M V30 11 N 3.83 3.8064 0.0 0 +M V30 12 O 2.5336 6.0455 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 6 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 7 10 +M V30 13 1 9 11 +M V30 END BOND +M V30 END CTAB +M END +> +57 + +> +Z1198266475 + +> +180.591 + +> +1.076 + +> +1 + +> +41.460 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1949844 + +> +0.91 + +$$$$ +Compound 58 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4897 0.0 0 +M V30 3 N 1.2769 -2.2228 0.0 0 +M V30 4 C -1.3242 -2.2228 0.0 0 +M V30 5 C 1.2651 -3.7125 0.0 0 +M V30 6 C -1.336 -3.7125 0.0 0 +M V30 7 C -2.6366 -1.4661 0.0 0 +M V30 8 C -0.0472 -4.4574 0.0 0 +M V30 9 C -2.6484 -4.4574 0.0 0 +M V30 10 C -3.949 -2.1991 0.0 0 +M V30 11 C -3.9608 -3.6889 0.0 0 +M V30 12 O -5.2732 -4.4338 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 2 9 11 +M V30 11 1 11 12 +M V30 12 1 6 8 +M V30 13 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +58 + +> +Z1198175558 + +> +163.173 + +> +0.668 + +> +2 + +> +49.330 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3629676 + +> +0.98 + +$$$$ +Compound 59 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4912 0.0 0 +M V30 3 N 1.2782 -2.225 0.0 0 +M V30 4 C -1.3255 -2.225 0.0 0 +M V30 5 C 1.2663 -3.7162 0.0 0 +M V30 6 C -1.3373 -3.7162 0.0 0 +M V30 7 C -2.6392 -1.4675 0.0 0 +M V30 8 C -0.0473 -4.4618 0.0 0 +M V30 9 C -2.651 -4.4618 0.0 0 +M V30 10 C -3.9529 -2.2013 0.0 0 +M V30 11 N -2.6629 -5.9531 0.0 0 +M V30 12 C -3.9648 -3.6925 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 9 11 +M V30 11 2 9 12 +M V30 12 1 6 8 +M V30 13 1 10 12 +M V30 END BOND +M V30 END CTAB +M END +> +59 + +> +Z1741975980 + +> +160.173 + +> +-0.040 + +> +2 + +> +55.120 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL446240 + +> +1.0 + +$$$$ +Compound 60 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4975 0.0 0 +M V30 3 N -1.3311 -2.2344 0.0 0 +M V30 4 C 1.2836 -2.2344 0.0 0 +M V30 5 C -1.343 -3.7319 0.0 0 +M V30 6 C 1.2717 -3.7319 0.0 0 +M V30 7 C 2.579 -1.4737 0.0 0 +M V30 8 C -0.0475 -4.4807 0.0 0 +M V30 9 C 2.5672 -4.4807 0.0 0 +M V30 10 C 3.8745 -2.2106 0.0 0 +M V30 11 O -0.0594 -5.9782 0.0 0 +M V30 12 C 3.8626 -3.7081 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 2 8 11 +M V30 11 2 9 12 +M V30 12 1 6 8 +M V30 13 1 10 12 +M V30 END BOND +M V30 END CTAB +M END +> +60 + +> +Z1255462822 + +> +161.157 + +> +0.639 + +> +1 + +> +46.170 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL338314 + +> +0.88 + +$$$$ +Compound 61 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5006 0.0 0 +M V30 3 N 1.2661 2.2627 0.0 0 +M V30 4 C -1.313 2.2627 0.0 0 +M V30 5 C 1.2544 3.7634 0.0 0 +M V30 6 C -1.3248 3.7634 0.0 0 +M V30 7 C -2.6144 1.5241 0.0 0 +M V30 8 C -0.0468 4.5254 0.0 0 +M V30 9 C -2.6261 4.5254 0.0 0 +M V30 10 C -3.9158 2.2861 0.0 0 +M V30 11 C -3.9275 3.7868 0.0 0 +M V30 12 C -5.2171 1.5475 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 2 9 11 +M V30 11 1 10 12 +M V30 12 1 6 8 +M V30 13 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +61 + +> +Z1198164330 + +> +161.200 + +> +1.495 + +> +1 + +> +29.100 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1688212 + +> +0.95 + +$$$$ +Compound 62 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2896 0.769 0.0 0 +M V30 3 C 2.5792 0.0236 0.0 0 +M V30 4 C 1.2777 2.2834 0.0 0 +M V30 5 C 3.8688 0.7927 0.0 0 +M V30 6 C 2.5674 3.0525 0.0 0 +M V30 7 C 5.1585 0.0473 0.0 0 +M V30 8 C 3.857 2.3071 0.0 0 +M V30 9 O 5.1466 -1.4434 0.0 0 +M V30 10 N 6.4481 0.8163 0.0 0 +M V30 11 C 5.1466 3.0761 0.0 0 +M V30 12 C 6.4363 2.3307 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 1 10 12 +M V30 12 1 6 8 +M V30 13 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +62 + +> +Z1198164332 + +> +181.619 + +> +1.911 + +> +1 + +> +29.100 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3416132 + +> +1.0 + +$$$$ +Compound 63 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.2896 0.769 0.0 0 +M V30 3 C 2.5792 0.0236 0.0 0 +M V30 4 C 1.2777 2.2834 0.0 0 +M V30 5 C 3.8688 0.7927 0.0 0 +M V30 6 C 2.5674 3.0525 0.0 0 +M V30 7 C 5.1585 0.0473 0.0 0 +M V30 8 C 3.857 2.3071 0.0 0 +M V30 9 O 5.1466 -1.4434 0.0 0 +M V30 10 N 6.4481 0.8163 0.0 0 +M V30 11 C 5.1466 3.0761 0.0 0 +M V30 12 C 6.4363 2.3307 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 1 10 12 +M V30 12 1 6 8 +M V30 13 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +63 + +> +Z1198164337 + +> +165.164 + +> +1.341 + +> +1 + +> +29.100 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3629672 + +> +1.0 + +$$$$ +Compound 64 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.506 0.0 0 +M V30 3 N -1.3177 2.2708 0.0 0 +M V30 4 C 1.2707 2.2708 0.0 0 +M V30 5 C -1.3295 3.7768 0.0 0 +M V30 6 C 1.2589 3.7768 0.0 0 +M V30 7 C 2.5531 1.5295 0.0 0 +M V30 8 C -0.047 4.5416 0.0 0 +M V30 9 C -2.6355 4.5416 0.0 0 +M V30 10 C 2.5414 4.5416 0.0 0 +M V30 11 C 3.8356 2.2943 0.0 0 +M V30 12 C 3.8238 3.8003 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 10 12 +M V30 12 1 6 8 +M V30 13 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +64 + +> +Z1198162880 + +> +159.185 + +> +1.311 + +> +1 + +> +29.100 + +> +0 + +> +Tankyrase-1 + +> +CHEMBL3764754 + +> +0.87 + +$$$$ +Compound 65 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 N 0.0 0.0 0.0 0 +M V30 2 N -0.4696 -1.4088 0.0 0 +M V30 3 C -1.2209 0.8922 0.0 0 +M V30 4 C -1.9723 -1.397 0.0 0 +M V30 5 C -2.4419 0.0234 0.0 0 +M V30 6 C -1.2327 2.395 0.0 0 +M V30 7 C -3.8742 0.493 0.0 0 +M V30 8 C -4.9896 -0.493 0.0 0 +M V30 9 C -4.1912 1.9606 0.0 0 +M V30 10 C -6.4219 -0.0234 0.0 0 +M V30 11 C -5.6236 2.4302 0.0 0 +M V30 12 C -6.7389 1.444 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 1 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 5 7 +M V30 7 2 7 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 2 10 12 +M V30 12 2 4 5 +M V30 13 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +65 + +> +Z1201618222 + +> +158.200 + +> +2.098 + +> +1 + +> +28.680 + +> +1 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL540487 + +> +0.94 + +$$$$ +Compound 66 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.3198 -1.457 0.0 0 +M V30 3 N 1.7768 -1.7768 0.0 0 +M V30 4 C -0.8647 -2.3809 0.0 0 +M V30 5 C 2.4165 -3.1154 0.0 0 +M V30 6 C -0.8765 -3.8735 0.0 0 +M V30 7 C -2.1795 -1.6228 0.0 0 +M V30 8 C 1.7531 -4.4539 0.0 0 +M V30 9 C -2.1914 -4.6198 0.0 0 +M V30 10 C 0.2842 -4.7974 0.0 0 +M V30 11 C -3.4944 -2.3572 0.0 0 +M V30 12 C -3.5063 -3.8498 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 9 12 +M V30 12 1 8 10 +M V30 13 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +66 + +> +Z1201622867 + +> +161.200 + +> +1.405 + +> +1 + +> +29.100 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1688212 + +> +0.88 + +$$$$ +Compound 67 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C 1.2896 0.769 0.0 0 +M V30 3 C 2.5792 0.0236 0.0 0 +M V30 4 C 1.2777 2.2834 0.0 0 +M V30 5 C 3.8688 0.7927 0.0 0 +M V30 6 C 2.5674 3.0525 0.0 0 +M V30 7 C 5.1585 0.0473 0.0 0 +M V30 8 C 3.857 2.3071 0.0 0 +M V30 9 O 5.1466 -1.4434 0.0 0 +M V30 10 N 6.4481 0.8163 0.0 0 +M V30 11 C 5.1466 3.0761 0.0 0 +M V30 12 C 6.4363 2.3307 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 1 10 12 +M V30 12 1 6 8 +M V30 13 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +67 + +> +Z1209452761 + +> +226.070 + +> +2.061 + +> +1 + +> +29.100 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3629673 + +> +1.0 + +$$$$ +Compound 68 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 O 9.2966 -2.5354 0.0 0 +M V30 3 C 9.2848 -1.0329 0.0 0 +M V30 4 N 10.5643 -0.2817 0.0 0 +M V30 5 C 7.9819 -0.2699 0.0 0 +M V30 6 C 10.5525 1.2207 0.0 0 +M V30 7 C 7.9701 1.2325 0.0 0 +M V30 8 C 6.6789 -1.0094 0.0 0 +M V30 9 C 9.2496 1.9837 0.0 0 +M V30 10 C 6.6672 1.9837 0.0 0 +M V30 11 C 5.376 -0.2582 0.0 0 +M V30 12 C 5.3643 1.2559 0.0 0 +M V30 13 N 4.0731 -0.986 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 1 3 4 +M V30 3 1 3 5 +M V30 4 1 4 6 +M V30 5 2 5 7 +M V30 6 1 5 8 +M V30 7 2 6 9 +M V30 8 1 7 10 +M V30 9 2 8 11 +M V30 10 2 10 12 +M V30 11 1 11 13 +M V30 12 1 7 9 +M V30 13 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +68 + +> +Z2353512672 + +> +160.173 + +> +-0.040 + +> +2 + +> +55.120 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL446240 + +> +0.93 + +$$$$ +Compound 69 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5084 0.0 0 +M V30 3 C 1.2727 2.2744 0.0 0 +M V30 4 C -1.3198 2.2744 0.0 0 +M V30 5 C 1.2609 3.7828 0.0 0 +M V30 6 C 2.5572 1.532 0.0 0 +M V30 7 C -1.3316 3.7828 0.0 0 +M V30 8 C 2.5454 4.537 0.0 0 +M V30 9 C -0.0471 4.537 0.0 0 +M V30 10 C 3.8417 2.298 0.0 0 +M V30 11 O 2.5336 6.0455 0.0 0 +M V30 12 N 3.83 3.8064 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 7 9 +M V30 13 1 10 12 +M V30 END BOND +M V30 END CTAB +M END +> +69 + +> +Z1218937020 + +> +226.070 + +> +2.061 + +> +1 + +> +29.100 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3629673 + +> +0.91 + +$$$$ +Compound 70 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.506 0.0 0 +M V30 3 N -1.3177 2.2708 0.0 0 +M V30 4 C 1.2707 2.2708 0.0 0 +M V30 5 C -1.3295 3.7768 0.0 0 CFG=2 +M V30 6 C 1.2589 3.7768 0.0 0 +M V30 7 C 2.5531 1.5295 0.0 0 +M V30 8 C -0.047 4.5416 0.0 0 +M V30 9 C -2.6355 4.5416 0.0 0 +M V30 10 C 2.5414 4.5416 0.0 0 +M V30 11 C 3.8356 2.2943 0.0 0 +M V30 12 C 3.8238 3.8003 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 CFG=1 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 10 12 +M V30 12 1 6 8 +M V30 13 1 11 12 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 5) +M V30 END COLLECTION +M V30 END CTAB +M END +> +70 + +> +Z1218937049 + +> +161.200 + +> +1.515 + +> +1 + +> +29.100 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1688212 + +> +0.86 + +$$$$ +Compound 71 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 O 7.2992 3.1282 0.0 0 +M V30 3 C 7.2874 1.6352 0.0 0 +M V30 4 N 8.579 0.9005 0.0 0 +M V30 5 C 5.9721 0.9005 0.0 0 +M V30 6 C 8.5671 -0.5924 0.0 0 +M V30 7 C 5.9602 -0.5924 0.0 0 +M V30 8 C 4.6568 1.6589 0.0 0 +M V30 9 C 7.2518 -1.3271 0.0 0 +M V30 10 C 4.6449 -1.3271 0.0 0 +M V30 11 C 3.3415 0.9242 0.0 0 +M V30 12 N 4.6331 -2.8201 0.0 0 +M V30 13 C 3.3297 -0.5687 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 1 3 4 +M V30 3 1 3 5 +M V30 4 1 4 6 +M V30 5 2 5 7 +M V30 6 1 5 8 +M V30 7 1 6 9 +M V30 8 1 7 10 +M V30 9 2 8 11 +M V30 10 1 10 12 +M V30 11 2 10 13 +M V30 12 1 7 9 +M V30 13 1 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +71 + +> +Z1198176443 + +> +162.189 + +> +0.089 + +> +2 + +> +55.120 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL594759 + +> +1.0 + +$$$$ +Compound 72 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 12 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.5112 0.0 0 +M V30 3 N -1.3223 2.2669 0.0 0 +M V30 4 C 1.2751 2.2669 0.0 0 +M V30 5 C 1.2633 3.7781 0.0 0 +M V30 6 C 2.562 1.5348 0.0 0 +M V30 7 C 2.5502 4.5338 0.0 0 +M V30 8 C 3.849 2.2905 0.0 0 +M V30 9 O 2.5384 6.045 0.0 0 +M V30 10 C 3.8372 3.8017 0.0 0 +M V30 11 C 3.8253 6.8007 0.0 0 +M V30 12 C 3.8135 8.3119 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 2 7 10 +M V30 10 1 9 11 +M V30 11 1 11 12 +M V30 12 1 8 10 +M V30 END BOND +M V30 END CTAB +M END +> +72 + +> +Z274459672 + +> +165.189 + +> +1.365 + +> +1 + +> +52.320 + +> +3 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL123978 + +> +0.89 + +$$$$ +Compound 73 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3131 -0.7334 0.0 0 +M V30 3 N -2.6262 0.0236 0.0 0 +M V30 4 C -1.3249 -2.224 0.0 0 +M V30 5 C -2.6381 -2.9575 0.0 0 +M V30 6 C -0.0354 -2.9575 0.0 0 +M V30 7 N -4.0813 -2.4843 0.0 0 +M V30 8 C -2.6499 -4.4481 0.0 0 +M V30 9 C -0.0473 -4.4481 0.0 0 +M V30 10 C -4.9804 -3.6909 0.0 0 +M V30 11 C -1.3604 -5.1815 0.0 0 +M V30 12 C -4.0932 -4.8976 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 9 11 +M V30 13 2 10 12 +M V30 END BOND +M V30 END CTAB +M END +> +73 + +> +Z1255438908 + +> +160.173 + +> +0.837 + +> +2 + +> +58.880 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL502330 + +> +0.93 + +$$$$ +Compound 74 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4912 0.0 0 +M V30 3 N 1.2782 -2.225 0.0 0 +M V30 4 C -1.3255 -2.225 0.0 0 +M V30 5 C 1.2663 -3.7162 0.0 0 +M V30 6 C -1.3373 -3.7162 0.0 0 +M V30 7 C -2.6392 -1.4675 0.0 0 +M V30 8 C -0.0473 -4.4618 0.0 0 +M V30 9 C -2.651 -4.4618 0.0 0 +M V30 10 C -3.9529 -2.2013 0.0 0 +M V30 11 O -2.6629 -5.9531 0.0 0 +M V30 12 C -3.9648 -3.6925 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 1 9 11 +M V30 11 1 9 12 +M V30 12 1 6 8 +M V30 13 2 10 12 +M V30 END BOND +M V30 END CTAB +M END +> +74 + +> +Z1255402655 + +> +161.157 + +> +2.154 + +> +2 + +> +53.350 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL339695 + +> +0.87 + +$$$$ +Compound 75 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3131 -0.7334 0.0 0 +M V30 3 N -2.6262 0.0236 0.0 0 +M V30 4 C -1.3249 -2.224 0.0 0 +M V30 5 C -2.6381 -2.9575 0.0 0 +M V30 6 C -0.0354 -2.9575 0.0 0 +M V30 7 N -4.0813 -2.4843 0.0 0 +M V30 8 C -2.6499 -4.4481 0.0 0 +M V30 9 C -0.0473 -4.4481 0.0 0 +M V30 10 C -4.9804 -3.6909 0.0 0 +M V30 11 N -4.0932 -4.8976 0.0 0 +M V30 12 C -1.3604 -5.1815 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 8 12 +M V30 12 1 9 12 +M V30 13 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +75 + +> +Z1255430314 + +> +161.161 + +> +0.572 + +> +2 + +> +71.770 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL133694 + +> +1.0 + +$$$$ +Compound 76 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4897 0.0 0 +M V30 3 N 1.2769 -2.2228 0.0 0 +M V30 4 C -1.3242 -2.2228 0.0 0 +M V30 5 C 1.2651 -3.7125 0.0 0 +M V30 6 C -1.336 -3.7125 0.0 0 +M V30 7 C -2.6366 -1.4661 0.0 0 +M V30 8 C -0.0472 -4.4574 0.0 0 +M V30 9 C -2.6484 -4.4574 0.0 0 +M V30 10 C -3.949 -2.1991 0.0 0 +M V30 11 C -3.9608 -3.6889 0.0 0 +M V30 12 N -5.2732 -4.4338 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 2 9 11 +M V30 11 1 11 12 +M V30 12 1 6 8 +M V30 13 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +76 + +> +Z1255396439 + +> +160.173 + +> +-0.040 + +> +2 + +> +55.120 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL446240 + +> +0.91 + +$$$$ +Compound 77 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.32 -1.4579 0.0 0 +M V30 3 C -0.8652 -2.3825 0.0 0 +M V30 4 C 1.778 -1.778 0.0 0 +M V30 5 C -0.8771 -3.876 0.0 0 +M V30 6 C -2.3114 -1.9083 0.0 0 +M V30 7 C 2.418 -3.1174 0.0 0 +M V30 8 N -2.3232 -4.3264 0.0 0 +M V30 9 C 0.2844 -4.8006 0.0 0 +M V30 10 N -3.2122 -3.1174 0.0 0 +M V30 11 C 1.7542 -4.4568 0.0 0 +M V30 12 C -2.7973 -5.7488 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 8 10 +M V30 13 1 9 11 +M V30 END BOND +M V30 END CTAB +M END +> +77 + +> +Z1255458525 + +> +164.204 + +> +0.857 + +> +0 + +> +34.890 + +> +0 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1981107 + +> +0.99 + +$$$$ +Compound 78 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C 1.2896 0.769 0.0 0 +M V30 3 C 2.5792 0.0236 0.0 0 +M V30 4 C 1.2778 2.2834 0.0 0 +M V30 5 C 3.8689 0.7927 0.0 0 +M V30 6 C 2.5674 3.0407 0.0 0 +M V30 7 C 3.857 2.3071 0.0 0 +M V30 8 C 5.1585 0.0473 0.0 0 +M V30 9 C 5.1467 3.0643 0.0 0 +M V30 10 C 6.4481 0.8163 0.0 0 +M V30 11 O 5.1348 4.5788 0.0 0 +M V30 12 N 6.4363 2.3308 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 7 9 +M V30 9 2 8 10 +M V30 10 2 9 11 +M V30 11 1 9 12 +M V30 12 1 6 7 +M V30 13 1 10 12 +M V30 END BOND +M V30 END CTAB +M END +> +78 + +> +Z1741974434 + +> +224.054 + +> +1.822 + +> +1 + +> +29.100 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1173518 + +> +0.9 + +$$$$ +Compound 79 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5021 0.0 0 +M V30 3 N 1.2674 2.2649 0.0 0 +M V30 4 C -1.3143 2.2649 0.0 0 +M V30 5 C 1.2556 3.767 0.0 0 +M V30 6 C -2.617 1.5256 0.0 0 +M V30 7 C -1.3261 3.767 0.0 0 +M V30 8 C -0.0469 4.5298 0.0 0 +M V30 9 N -2.6287 0.0469 0.0 0 +M V30 10 C -3.9196 2.2884 0.0 0 +M V30 11 C -2.6287 4.5298 0.0 0 +M V30 12 C -3.9313 3.7905 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 10 12 +M V30 12 1 7 8 +M V30 13 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +79 + +> +Z1262683229 + +> +160.173 + +> +-0.040 + +> +2 + +> +55.120 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL446240 + +> +0.92 + +$$$$ +Compound 80 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5084 0.0 0 +M V30 3 C 1.2727 2.2744 0.0 0 +M V30 4 C -1.3198 2.2744 0.0 0 +M V30 5 C 1.2609 3.7828 0.0 0 +M V30 6 C 2.5572 1.532 0.0 0 +M V30 7 C -1.3316 3.7828 0.0 0 +M V30 8 C 2.5454 4.537 0.0 0 +M V30 9 C -0.0471 4.537 0.0 0 +M V30 10 C 3.8417 2.298 0.0 0 +M V30 11 O 2.5336 6.0455 0.0 0 +M V30 12 N 3.83 3.8064 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 2 6 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 7 9 +M V30 13 1 10 12 +M V30 END BOND +M V30 END CTAB +M END +> +80 + +> +Z1269168088 + +> +224.054 + +> +1.822 + +> +1 + +> +29.100 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1173518 + +> +1.0 + +$$$$ +Compound 81 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 12 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.4161 0.472 0.0 0 +M V30 3 C -0.8968 1.2273 0.0 0 +M V30 4 N 2.7024 -0.2596 0.0 0 +M V30 5 C 1.4043 1.9825 0.0 0 +M V30 6 C -0.0236 2.4546 0.0 0 +M V30 7 C 3.9887 0.4956 0.0 0 +M V30 8 C 2.6906 2.7378 0.0 0 +M V30 9 N 3.9769 2.0061 0.0 0 +M V30 10 O 2.6788 4.2483 0.0 0 +M V30 11 C 5.2632 2.7614 0.0 0 +M V30 12 C 5.2514 4.2719 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 1 7 9 +M V30 9 2 8 10 +M V30 10 1 9 11 +M V30 11 1 11 12 +M V30 12 1 5 6 +M V30 13 1 8 9 +M V30 END BOND +M V30 END CTAB +M END +> +81 + +> +Z153981792 + +> +180.227 + +> +0.955 + +> +0 + +> +32.670 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL2432028 + +> +0.87 + +$$$$ +Compound 82 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3076 -0.7421 0.0 0 +M V30 3 N -2.6152 0.0235 0.0 0 +M V30 4 N -1.3193 -2.2264 0.0 0 +M V30 5 C -3.9228 -0.7185 0.0 0 +M V30 6 C -2.6269 1.5314 0.0 0 +M V30 7 C -2.6269 -2.9686 0.0 0 +M V30 8 N -5.36 -0.2473 0.0 0 +M V30 9 C -3.9346 -2.2029 0.0 0 +M V30 10 O -2.6387 -4.4529 0.0 0 +M V30 11 C -6.2553 -1.4489 0.0 0 +M V30 12 N -5.3717 -2.6505 0.0 0 +M V30 13 C -5.843 -4.0641 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 2 7 10 +M V30 10 2 8 11 +M V30 11 1 9 12 +M V30 12 1 12 13 +M V30 13 1 7 9 +M V30 14 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +82 + +> +Z56347209 + +> +180.164 + +> +-0.672 + +> +1 + +> +67.230 + +> +0 + +> +Serine-protein kinase ATM + +> +CHEMBL113 + +> +0.97 + +$$$$ +Compound 83 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4963 0.0 0 +M V30 3 C 1.2825 -2.2326 0.0 0 +M V30 4 C -1.33 -2.2326 0.0 0 +M V30 5 N 2.577 -1.4725 0.0 0 +M V30 6 C 1.2706 -3.7289 0.0 0 +M V30 7 C -1.3419 -3.7289 0.0 0 +M V30 8 C 3.8714 -2.2088 0.0 0 +M V30 9 C 2.5651 -4.4771 0.0 0 +M V30 10 C -0.0475 -4.4771 0.0 0 +M V30 11 Cl -2.6601 -4.4771 0.0 0 +M V30 12 N 3.8595 -3.7051 0.0 0 +M V30 13 O 2.5532 -5.9734 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 7 10 +M V30 14 1 9 12 +M V30 END BOND +M V30 END CTAB +M END +> +83 + +> +Z56893138 + +> +215.036 + +> +1.809 + +> +1 + +> +41.460 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1949844 + +> +0.89 + +$$$$ +Compound 84 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.4174 0.4724 0.0 0 +M V30 3 C -0.8977 1.2284 0.0 0 +M V30 4 N 2.705 -0.2598 0.0 0 +M V30 5 N 1.4056 1.9844 0.0 0 +M V30 6 C -0.0236 2.4569 0.0 0 +M V30 7 C 3.9925 0.4961 0.0 0 +M V30 8 C 2.6932 2.7404 0.0 0 +M V30 9 C 3.9807 2.0081 0.0 0 +M V30 10 O 2.6814 4.2524 0.0 0 +M V30 11 C 5.2683 2.764 0.0 0 +M V30 12 O 6.5558 2.0317 0.0 0 +M V30 13 O 5.2565 4.276 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 7 9 +M V30 9 2 8 10 +M V30 10 1 9 11 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 1 5 6 +M V30 14 1 8 9 +M V30 END BOND +M V30 END CTAB +M END +> +84 + +> +Z57301587 + +> +196.183 + +> +0.136 + +> +1 + +> +69.970 + +> +1 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1971679 + +> +1.0 + +$$$$ +Compound 85 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4963 0.0 0 +M V30 3 C 1.2825 -2.2326 0.0 0 +M V30 4 C -1.33 -2.2326 0.0 0 +M V30 5 N 2.577 -1.4725 0.0 0 +M V30 6 C 1.2706 -3.7289 0.0 0 +M V30 7 C -1.3419 -3.7289 0.0 0 +M V30 8 C 3.8714 -2.2088 0.0 0 +M V30 9 C 2.5651 -4.4771 0.0 0 +M V30 10 C -0.0475 -4.4771 0.0 0 +M V30 11 Br -2.6601 -4.4771 0.0 0 +M V30 12 N 3.8595 -3.7051 0.0 0 +M V30 13 O 2.5532 -5.9734 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 7 10 +M V30 14 1 9 12 +M V30 END BOND +M V30 END CTAB +M END +> +85 + +> +Z104508912 + +> +303.938 + +> +2.109 + +> +1 + +> +41.460 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL562488 + +> +0.9 + +$$$$ +Compound 86 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 15 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.2427 -0.8521 0.0 0 +M V30 3 C 1.1835 -0.8994 0.0 0 +M V30 4 N -2.7221 -0.497 0.0 0 +M V30 5 C -0.8166 -2.2842 0.0 0 +M V30 6 C 0.6746 -2.3078 0.0 0 +M V30 7 C 2.6747 -0.9231 0.0 0 +M V30 8 C -3.7636 -1.5859 0.0 0 +M V30 9 C -1.8581 -3.373 0.0 0 +M V30 10 C 1.8581 -3.2073 0.0 0 +M V30 11 C 3.1008 -2.3552 0.0 0 +M V30 12 N -3.3375 -3.018 0.0 0 +M V30 13 O -1.432 -4.8051 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 5 6 +M V30 14 1 9 12 +M V30 15 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +86 + +> +Z56887776 + +> +192.238 + +> +1.137 + +> +1 + +> +41.460 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL539906 + +> +0.97 + +$$$$ +Compound 87 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2941 -0.7479 0.0 0 +M V30 3 N 1.2822 -2.2439 0.0 0 +M V30 4 N 2.5882 0.0237 0.0 0 +M V30 5 C 2.5763 -2.9919 0.0 0 +M V30 6 C -0.0356 -2.9919 0.0 0 +M V30 7 C 3.8823 -0.7242 0.0 0 +M V30 8 O 2.5644 -4.4878 0.0 0 +M V30 9 C 3.8704 -2.2201 0.0 0 +M V30 10 C 5.1764 0.0474 0.0 0 +M V30 11 C 5.1646 -2.9681 0.0 0 +M V30 12 C 6.4705 -0.7004 0.0 0 +M V30 13 C 6.4587 -2.1964 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 7 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 2 11 13 +M V30 13 2 7 9 +M V30 14 1 12 13 +M V30 END BOND +M V30 END CTAB +M END +> +87 + +> +Z164707292 + +> +176.172 + +> +1.384 + +> +1 + +> +49.410 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL421646 + +> +0.89 + +$$$$ +Compound 88 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.2886 0.7684 0.0 0 +M V30 3 C 1.2768 2.2817 0.0 0 +M V30 4 C 2.5773 0.0236 0.0 0 +M V30 5 N -0.0354 3.0383 0.0 0 +M V30 6 C 2.5654 3.0383 0.0 0 +M V30 7 C 3.8659 0.7921 0.0 0 +M V30 8 C 3.8541 2.3053 0.0 0 +M V30 9 N 5.1546 0.0472 0.0 0 +M V30 10 C 5.1428 3.062 0.0 0 +M V30 11 C 6.4432 0.8157 0.0 0 +M V30 12 O 5.1309 4.5753 0.0 0 +M V30 13 N 6.4314 2.329 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 2 10 12 +M V30 12 1 10 13 +M V30 13 1 7 8 +M V30 14 1 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +88 + +> +Z166632342 + +> +179.151 + +> +-0.001 + +> +2 + +> +67.480 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1949848 + +> +0.85 + +$$$$ +Compound 89 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5059 0.0 0 +M V30 3 N -1.3176 2.2706 0.0 0 +M V30 4 C 1.2706 2.2706 0.0 0 +M V30 5 C -1.3294 3.7765 0.0 0 +M V30 6 C 1.2588 3.7765 0.0 0 +M V30 7 C 2.5529 1.5294 0.0 0 +M V30 8 N -0.047 4.5412 0.0 0 +M V30 9 C -2.6353 4.5412 0.0 0 +M V30 10 C 2.5412 4.5412 0.0 0 +M V30 11 C 3.8353 2.2941 0.0 0 +M V30 12 O -3.9412 3.8 0.0 0 +M V30 13 C 3.8235 3.8 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 6 8 +M V30 14 1 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +89 + +> +Z94598966 + +> +176.172 + +> +-0.733 + +> +2 + +> +61.690 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL395092 + +> +0.89 + +$$$$ +Compound 90 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 15 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 0.4223 1.443 0.0 0 +M V30 3 C -1.5016 -0.0234 0.0 0 +M V30 4 N -0.8094 2.3111 0.0 0 +M V30 5 N 1.8184 1.9474 0.0 0 +M V30 6 C -2.0061 1.396 0.0 0 +M V30 7 C -2.6983 -0.915 0.0 0 +M V30 8 C -3.5078 1.3726 0.0 0 +M V30 9 C -3.9301 -0.0469 0.0 0 +M V30 10 C -4.5402 2.4754 0.0 0 +M V30 11 C -5.3966 -0.3754 0.0 0 +M V30 12 C -6.0066 2.1469 0.0 0 +M V30 13 C -6.429 0.7273 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 1 6 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 2 11 13 +M V30 13 1 4 6 +M V30 14 2 8 9 +M V30 15 1 12 13 +M V30 END BOND +M V30 END CTAB +M END +> +90 + +> +Z240125452 + +> +188.249 + +> +2.372 + +> +1 + +> +38.910 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1178727 + +> +1.0 + +$$$$ +Compound 91 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 N 5.7613 -1.3222 0.0 0 +M V30 3 C 6.21 0.118 0.0 0 CFG=2 +M V30 4 C 4.2856 -1.6174 0.0 0 +M V30 5 C 7.6267 0.5903 0.0 0 +M V30 6 C 5.3127 1.3458 0.0 0 +M V30 7 C 3.8133 -3.0341 0.0 0 +M V30 8 C 7.6149 2.1014 0.0 0 +M V30 9 C 8.9135 -0.1416 0.0 0 +M V30 10 C 6.1863 2.5737 0.0 0 +M V30 11 C 3.3411 -4.4508 0.0 0 +M V30 12 C 8.9017 2.8688 0.0 0 +M V30 13 C 10.2004 0.6257 0.0 0 +M V30 14 C 10.1886 2.125 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 3 2 CFG=1 +M V30 2 1 2 4 +M V30 3 1 3 5 +M V30 4 1 3 6 +M V30 5 1 4 7 +M V30 6 2 5 8 +M V30 7 1 5 9 +M V30 8 1 6 10 +M V30 9 3 7 11 +M V30 10 1 8 12 +M V30 11 2 9 13 +M V30 12 2 12 14 +M V30 13 1 8 10 +M V30 14 1 13 14 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 3) +M V30 END COLLECTION +M V30 END CTAB +M END +> +91 + +> +Z425484918 + +> +171.238 + +> +2.524 + +> +1 + +> +12.030 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL417002 + +> +1.0 + +$$$$ +Compound 92 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5059 0.0 0 +M V30 3 N -1.3176 2.2706 0.0 0 +M V30 4 C 1.2706 2.2706 0.0 0 +M V30 5 C -1.3294 3.7765 0.0 0 +M V30 6 C 1.2588 3.7765 0.0 0 +M V30 7 C 2.5529 1.5294 0.0 0 +M V30 8 N -0.047 4.5412 0.0 0 +M V30 9 C -2.6353 4.5412 0.0 0 +M V30 10 C 2.5412 4.5412 0.0 0 +M V30 11 C 3.8353 2.2941 0.0 0 +M V30 12 C -3.9412 3.8 0.0 0 +M V30 13 C 3.8235 3.8 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 9 12 +M V30 12 2 10 13 +M V30 13 1 6 8 +M V30 14 1 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +92 + +> +Z362599870 + +> +172.183 + +> +1.029 + +> +1 + +> +41.460 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL395092 + +> +0.87 + +$$$$ +Compound 93 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 Cl 3.5866 0.0943 0.0 0 +M V30 3 O -0.5663 -7.362 0.0 0 +M V30 4 C -0.5781 -5.8519 0.0 0 +M V30 5 N -1.8877 -5.0968 0.0 0 +M V30 6 C 0.7078 -5.0968 0.0 0 +M V30 7 C -1.8995 -3.5866 0.0 0 +M V30 8 C 0.696 -3.5866 0.0 0 +M V30 9 C 1.9938 -5.8283 0.0 0 +M V30 10 N -0.6135 -2.8315 0.0 0 +M V30 11 C -3.2091 -2.8315 0.0 0 +M V30 12 C 1.982 -2.8315 0.0 0 +M V30 13 C 3.2799 -5.0732 0.0 0 +M V30 14 N -4.5187 -3.5748 0.0 0 +M V30 15 C 3.2681 -3.5748 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 3 4 +M V30 2 1 4 5 +M V30 3 1 4 6 +M V30 4 1 5 7 +M V30 5 2 6 8 +M V30 6 1 6 9 +M V30 7 2 7 10 +M V30 8 1 7 11 +M V30 9 1 8 12 +M V30 10 2 9 13 +M V30 11 1 11 14 +M V30 12 2 12 15 +M V30 13 1 8 10 +M V30 14 1 13 15 +M V30 END BOND +M V30 END CTAB +M END +> +93 + +> +Z2335631488 + +> +175.187 + +> +-0.743 + +> +2 + +> +67.480 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL395092 + +> +0.91 + +$$$$ +Compound 94 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.441 -0.4488 0.0 0 +M V30 3 C 0.874 -1.2048 0.0 0 +M V30 4 N -2.8821 0.0236 0.0 0 +M V30 5 C -1.4528 -1.9371 0.0 0 +M V30 6 C 2.3624 -1.193 0.0 0 +M V30 7 C -0.0236 -2.4096 0.0 0 +M V30 8 N -3.7798 -1.1812 0.0 0 +M V30 9 C -3.3546 1.4646 0.0 0 +M V30 10 C -2.8939 -2.386 0.0 0 +M V30 11 O 3.0947 0.1181 0.0 0 +M V30 12 N 3.0947 -2.4805 0.0 0 +M V30 13 C -3.3664 -3.8034 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 1 3 6 +M V30 6 2 3 7 +M V30 7 1 4 8 +M V30 8 1 4 9 +M V30 9 1 5 10 +M V30 10 2 6 11 +M V30 11 1 6 12 +M V30 12 1 10 13 +M V30 13 1 5 7 +M V30 14 2 8 10 +M V30 END BOND +M V30 END CTAB +M END +> +94 + +> +Z229615464 + +> +195.242 + +> +0.605 + +> +1 + +> +60.910 + +> +1 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1986588 + +> +1.0 + +$$$$ +Compound 95 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3059 0.7647 0.0 0 +M V30 3 N -2.6118 0.0235 0.0 0 +M V30 4 C -1.3176 2.2706 0.0 0 +M V30 5 C -0.0352 3.0353 0.0 0 +M V30 6 C -2.6235 3.0353 0.0 0 +M V30 7 N 1.247 2.2941 0.0 0 +M V30 8 C -0.047 4.5412 0.0 0 +M V30 9 C -2.6353 4.5412 0.0 0 +M V30 10 C 2.5294 3.0588 0.0 0 +M V30 11 C 1.2353 5.3059 0.0 0 +M V30 12 C -1.3529 5.3059 0.0 0 +M V30 13 C 2.5176 4.5647 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 4 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 2 10 13 +M V30 13 2 9 12 +M V30 14 1 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +95 + +> +Z62734446 + +> +172.183 + +> +1.019 + +> +1 + +> +55.980 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL502330 + +> +1.0 + +$$$$ +Compound 96 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5082 0.0 0 +M V30 3 N -1.3196 2.2741 0.0 0 +M V30 4 C 1.2725 2.2741 0.0 0 +M V30 5 C -1.3314 3.7823 0.0 0 +M V30 6 C -2.6275 1.5317 0.0 0 +M V30 7 C 1.2607 3.7823 0.0 0 +M V30 8 C 2.5568 1.5317 0.0 0 +M V30 9 N -0.0471 4.5482 0.0 0 +M V30 10 C 2.5451 4.5482 0.0 0 +M V30 11 C 3.8412 2.2976 0.0 0 +M V30 12 C 3.8294 3.8058 0.0 0 +M V30 13 N 5.1255 1.5553 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 2 10 12 +M V30 12 1 11 13 +M V30 13 1 7 9 +M V30 14 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +96 + +> +Z57474223 + +> +175.187 + +> +0.121 + +> +1 + +> +58.690 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1949848 + +> +0.9 + +$$$$ +Compound 97 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.0135 1.1195 0.0 0 +M V30 3 N -2.4866 0.8249 0.0 0 +M V30 4 C -0.5656 2.5573 0.0 0 +M V30 5 C -2.958 -0.5892 0.0 0 +M V30 6 C 0.8485 3.0288 0.0 0 +M V30 7 C -1.4613 3.783 0.0 0 +M V30 8 C 0.8367 4.5373 0.0 0 +M V30 9 C 2.1331 2.2863 0.0 0 +M V30 10 N -0.5892 5.0087 0.0 0 +M V30 11 C 2.1213 5.3033 0.0 0 +M V30 12 C 3.4177 3.0523 0.0 0 +M V30 13 C 3.4059 4.5608 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 4 7 +M V30 7 2 6 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 8 10 +M V30 14 1 12 13 +M V30 END BOND +M V30 END CTAB +M END +> +97 + +> +Z234895483 + +> +174.199 + +> +1.107 + +> +2 + +> +44.890 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL356104 + +> +0.87 + +$$$$ +Compound 98 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.8911 1.2194 0.0 0 +M V30 3 N -2.392 1.2311 0.0 0 +M V30 4 C -0.4455 2.6499 0.0 0 +M V30 5 N -2.861 2.6616 0.0 0 +M V30 6 C -1.665 3.5411 0.0 0 +M V30 7 C -4.2915 3.1307 0.0 0 +M V30 8 N -1.6767 5.0419 0.0 0 +M V30 9 N -4.6081 4.5964 0.0 0 +M V30 10 C -5.4054 2.1457 0.0 0 +M V30 11 C -6.0386 5.0654 0.0 0 +M V30 12 C -6.8359 2.6147 0.0 0 +M V30 13 C -7.1525 4.0804 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 2 11 13 +M V30 13 1 5 6 +M V30 14 1 12 13 +M V30 END BOND +M V30 END CTAB +M END +> +98 + +> +Z801579710 + +> +176.175 + +> +-0.345 + +> +2 + +> +76.960 + +> +1 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1995765 + +> +1.0 + +$$$$ +Compound 99 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5081 0.0 0 +M V30 3 C -1.3196 2.2739 0.0 0 +M V30 4 C 1.2724 2.2739 0.0 0 +M V30 5 Cl -2.6274 1.5317 0.0 0 +M V30 6 C -1.3314 3.7821 0.0 0 +M V30 7 C 1.2607 3.7821 0.0 0 +M V30 8 C 2.5567 1.5317 0.0 0 +M V30 9 C -0.0471 4.5362 0.0 0 +M V30 10 C 2.5449 4.5362 0.0 0 +M V30 11 C 3.841 2.2975 0.0 0 +M V30 12 O 2.5332 6.0443 0.0 0 +M V30 13 N 3.8292 3.8056 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 2 10 12 +M V30 12 1 10 13 +M V30 13 1 7 9 +M V30 14 1 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +99 + +> +Z994559888 + +> +216.064 + +> +2.574 + +> +1 + +> +29.100 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3416132 + +> +0.87 + +$$$$ +Compound 100 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5081 0.0 0 +M V30 3 N -1.3196 2.2739 0.0 0 +M V30 4 C 1.2724 2.2739 0.0 0 +M V30 5 C -1.3314 3.7821 0.0 0 +M V30 6 C -2.6274 1.5317 0.0 0 +M V30 7 C 1.2607 3.7821 0.0 0 +M V30 8 C 2.5567 1.5317 0.0 0 +M V30 9 C -0.0471 4.5362 0.0 0 +M V30 10 C 2.5449 4.5362 0.0 0 +M V30 11 C 3.841 2.2975 0.0 0 +M V30 12 N 2.5332 6.0443 0.0 0 +M V30 13 C 3.8292 3.8056 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 10 12 +M V30 12 2 10 13 +M V30 13 1 7 9 +M V30 14 1 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +100 + +> +Z1262669737 + +> +176.215 + +> +0.145 + +> +1 + +> +46.330 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL594759 + +> +0.91 + +$$$$ +Compound 101 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.2941 -0.7479 0.0 0 +M V30 3 C 1.2822 -2.2439 0.0 0 +M V30 4 C 2.5882 0.0237 0.0 0 +M V30 5 F -0.0356 -2.9919 0.0 0 +M V30 6 C 2.5763 -2.9919 0.0 0 +M V30 7 C 3.8823 -0.7242 0.0 0 +M V30 8 C 3.8704 -2.2201 0.0 0 +M V30 9 N 5.1764 0.0474 0.0 0 +M V30 10 C 5.1646 -2.9681 0.0 0 +M V30 11 C 6.4705 -0.7004 0.0 0 +M V30 12 O 5.1527 -4.4641 0.0 0 +M V30 13 N 6.4587 -2.1964 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 2 10 12 +M V30 12 1 10 13 +M V30 13 1 7 8 +M V30 14 1 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +101 + +> +Z1171978537 + +> +182.127 + +> +0.599 + +> +1 + +> +41.460 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1949843 + +> +0.92 + +$$$$ +Compound 102 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.3194 -1.4553 0.0 0 +M V30 3 N 1.7748 -1.7748 0.0 0 +M V30 4 C -0.8637 -2.3782 0.0 0 +M V30 5 C 2.4137 -3.1119 0.0 0 +M V30 6 C -0.8755 -3.8691 0.0 0 +M V30 7 C -2.1771 -1.6091 0.0 0 +M V30 8 C 1.7511 -4.4489 0.0 0 +M V30 9 C -2.1889 -4.6146 0.0 0 +M V30 10 C 0.2839 -4.792 0.0 0 +M V30 11 C -3.4905 -2.3546 0.0 0 +M V30 12 C -3.5023 -3.8455 0.0 0 +M V30 13 O -4.8157 -4.5909 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 9 12 +M V30 12 1 12 13 +M V30 13 1 8 10 +M V30 14 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +102 + +> +Z1198174116 + +> +177.200 + +> +1.077 + +> +2 + +> +49.330 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3629676 + +> +0.87 + +$$$$ +Compound 103 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4904 0.0 0 +M V30 3 N 1.2775 -2.2239 0.0 0 +M V30 4 C -1.3248 -2.2239 0.0 0 +M V30 5 C 1.2657 -3.7143 0.0 0 +M V30 6 C -1.3367 -3.7143 0.0 0 +M V30 7 C -2.6379 -1.4668 0.0 0 +M V30 8 N -0.0473 -4.4596 0.0 0 +M V30 9 C -2.6497 -4.4596 0.0 0 +M V30 10 C -3.9509 -2.2002 0.0 0 +M V30 11 C -3.9628 -3.6907 0.0 0 +M V30 12 O -5.2758 -4.4359 0.0 0 +M V30 13 C -5.2876 -5.9264 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 2 9 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 1 6 8 +M V30 14 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +103 + +> +Z1198166040 + +> +176.172 + +> +0.662 + +> +1 + +> +50.690 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1949846 + +> +0.89 + +$$$$ +Compound 104 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5014 0.0 0 +M V30 3 C -1.3137 2.2639 0.0 0 +M V30 4 C 1.2668 2.2639 0.0 0 +M V30 5 C -2.6158 1.5249 0.0 0 +M V30 6 C -1.3255 3.7653 0.0 0 +M V30 7 C 1.2551 3.7653 0.0 0 +M V30 8 O -2.6275 0.0469 0.0 0 +M V30 9 N -3.9178 2.2873 0.0 0 +M V30 10 N -2.6275 4.5278 0.0 0 +M V30 11 C -0.0469 4.5278 0.0 0 +M V30 12 C -3.9295 3.7888 0.0 0 +M V30 13 C -5.2316 4.5512 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 6 11 +M V30 11 1 9 12 +M V30 12 1 12 13 +M V30 13 1 7 11 +M V30 14 2 10 12 +M V30 END BOND +M V30 END CTAB +M END +> +104 + +> +Z1198279517 + +> +194.618 + +> +1.575 + +> +1 + +> +41.460 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1949841 + +> +0.86 + +$$$$ +Compound 105 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2895 0.7689 0.0 0 +M V30 3 C 2.579 0.0236 0.0 0 +M V30 4 C 1.2777 2.2833 0.0 0 +M V30 5 C 3.8686 0.7926 0.0 0 +M V30 6 C 2.5672 3.0522 0.0 0 +M V30 7 C 5.1581 0.0473 0.0 0 +M V30 8 C 3.8567 2.3069 0.0 0 +M V30 9 O 5.1463 -1.4433 0.0 0 +M V30 10 N 6.4476 0.8163 0.0 0 +M V30 11 N 5.1463 3.0759 0.0 0 +M V30 12 C 6.4358 2.3306 0.0 0 +M V30 13 O 7.7253 3.0996 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 6 8 +M V30 14 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +105 + +> +Z1201617625 + +> +196.591 + +> +1.503 + +> +2 + +> +58.200 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL421646 + +> +0.88 + +$$$$ +Compound 106 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 13 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2927 -0.7353 0.0 0 +M V30 3 N 2.5854 0.0237 0.0 0 +M V30 4 C 1.2808 -2.2296 0.0 0 +M V30 5 C 2.5736 1.5418 0.0 0 +M V30 6 C -0.0355 -2.965 0.0 0 +M V30 7 C 2.5736 -2.965 0.0 0 +M V30 8 C -0.0474 -4.4593 0.0 0 +M V30 9 C 2.5617 -4.4593 0.0 0 +M V30 10 N -1.3639 -5.1947 0.0 0 +M V30 11 C 1.2453 -5.1947 0.0 0 +M V30 12 C -1.3757 -6.689 0.0 0 +M V30 13 C -2.6803 -4.4356 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 2 8 11 +M V30 11 1 10 12 +M V30 12 1 10 13 +M V30 13 1 9 11 +M V30 END BOND +M V30 END CTAB +M END +> +106 + +> +Z32016575 + +> +178.231 + +> +1.221 + +> +1 + +> +32.340 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL123205 + +> +0.9 + +$$$$ +Compound 107 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 O 9.2954 -2.3473 0.0 0 +M V30 3 C 9.2837 -0.845 0.0 0 +M V30 4 N 10.563 -0.0821 0.0 0 +M V30 5 C 7.9809 -0.0821 0.0 0 +M V30 6 C 10.5512 1.4201 0.0 0 +M V30 7 C 7.9692 1.4201 0.0 0 +M V30 8 C 6.6781 -0.8215 0.0 0 +M V30 9 C 9.2485 2.183 0.0 0 +M V30 10 C 6.6664 2.183 0.0 0 +M V30 11 C 5.3754 -0.0586 0.0 0 +M V30 12 C 5.3636 1.4436 0.0 0 +M V30 13 C 4.0726 -0.798 0.0 0 +M V30 14 N 4.0608 -2.2769 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 1 3 4 +M V30 3 1 3 5 +M V30 4 1 4 6 +M V30 5 2 5 7 +M V30 6 1 5 8 +M V30 7 1 6 9 +M V30 8 1 7 10 +M V30 9 2 8 11 +M V30 10 2 10 12 +M V30 11 1 11 13 +M V30 12 1 13 14 +M V30 13 1 7 9 +M V30 14 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +107 + +> +Z1881583655 + +> +176.215 + +> +-0.052 + +> +2 + +> +55.120 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1688212 + +> +0.93 + +$$$$ +Compound 108 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4963 0.0 0 +M V30 3 C 1.2825 -2.2326 0.0 0 +M V30 4 C -1.33 -2.2326 0.0 0 +M V30 5 C 1.2706 -3.7289 0.0 0 +M V30 6 C 2.577 -1.4725 0.0 0 +M V30 7 C -1.3419 -3.7289 0.0 0 +M V30 8 C 2.5651 -4.4771 0.0 0 +M V30 9 C -0.0475 -4.4771 0.0 0 +M V30 10 C 3.8714 -2.2088 0.0 0 +M V30 11 F -2.6601 -4.4771 0.0 0 +M V30 12 O 2.5532 -5.9734 0.0 0 +M V30 13 N 3.8595 -3.7051 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 1 7 9 +M V30 14 1 10 13 +M V30 END BOND +M V30 END CTAB +M END +> +108 + +> +Z1218937025 + +> +183.155 + +> +1.554 + +> +1 + +> +29.100 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3629672 + +> +0.9 + +$$$$ +Compound 109 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4972 0.0 0 +M V30 3 N -1.3309 -2.234 0.0 0 +M V30 4 C 1.2833 -2.234 0.0 0 +M V30 5 C -1.3427 -3.7313 0.0 0 +M V30 6 C 1.2715 -3.7313 0.0 0 +M V30 7 C 2.5786 -1.4735 0.0 0 +M V30 8 C -0.0475 -4.4799 0.0 0 +M V30 9 C 2.5667 -4.4799 0.0 0 +M V30 10 C 3.8739 -2.2102 0.0 0 +M V30 11 C -1.0338 -5.6207 0.0 0 +M V30 12 C 0.915 -5.6207 0.0 0 +M V30 13 C 3.862 -3.7075 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 6 8 +M V30 14 1 10 13 +M V30 END BOND +M V30 END CTAB +M END +> +109 + +> +Z1251360842 + +> +175.227 + +> +2.034 + +> +1 + +> +29.100 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1688212 + +> +0.87 + +$$$$ +Compound 110 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 N 0.0 0.0 0.0 0 +M V30 2 C -0.0117 -1.4861 0.0 0 +M V30 3 C -1.3092 0.7548 0.0 0 +M V30 4 N -1.321 -2.2174 0.0 0 +M V30 5 N 1.2738 -2.2174 0.0 0 +M V30 6 N -1.321 2.2645 0.0 0 +M V30 7 C -2.6184 0.0235 0.0 0 +M V30 8 C -2.6302 -1.4625 0.0 0 +M V30 9 C -2.6302 3.0194 0.0 0 +M V30 10 C -0.0353 3.0194 0.0 0 +M V30 11 N -4.0573 0.4953 0.0 0 +M V30 12 N -4.0691 -1.9107 0.0 0 +M V30 13 C -4.9537 -0.7076 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 2 3 7 +M V30 7 2 4 8 +M V30 8 1 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 2 11 13 +M V30 13 1 7 8 +M V30 14 1 12 13 +M V30 END BOND +M V30 END CTAB +M END +> +110 + +> +Z1255446657 + +> +178.195 + +> +0.809 + +> +2 + +> +83.720 + +> +1 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL407391 + +> +0.87 + +$$$$ +Compound 111 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.3012 0.762 0.0 0 +M V30 3 C -2.6025 0.0234 0.0 0 +M V30 4 C -1.3129 2.2625 0.0 0 +M V30 5 C -3.9037 0.7854 0.0 0 +M V30 6 C -2.6142 3.0245 0.0 0 +M V30 7 C -5.205 0.0468 0.0 0 +M V30 8 C -3.9155 2.286 0.0 0 +M V30 9 O -5.2167 -1.4302 0.0 0 +M V30 10 N -6.5063 0.8088 0.0 0 +M V30 11 N -5.2167 3.048 0.0 0 +M V30 12 C -6.518 2.3094 0.0 0 +M V30 13 C -7.8075 0.0703 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 1 10 12 +M V30 12 1 10 13 +M V30 13 1 6 8 +M V30 14 2 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +111 + +> +Z335825800 + +> +194.618 + +> +1.373 + +> +0 + +> +32.670 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1949844 + +> +0.9 + +$$$$ +Compound 112 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.2886 0.7684 0.0 0 +M V30 3 C 1.2768 2.2817 0.0 0 +M V30 4 C 2.5773 0.0236 0.0 0 +M V30 5 C 2.5654 3.0383 0.0 0 +M V30 6 C -0.0354 3.0383 0.0 0 +M V30 7 C 3.8659 0.7921 0.0 0 +M V30 8 C 3.8541 2.3053 0.0 0 +M V30 9 N 5.1546 0.0472 0.0 0 +M V30 10 C 5.1428 3.062 0.0 0 +M V30 11 C 6.4432 0.8157 0.0 0 +M V30 12 O 5.1309 4.5753 0.0 0 +M V30 13 N 6.4314 2.329 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 2 10 12 +M V30 12 1 10 13 +M V30 13 1 7 8 +M V30 14 1 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +112 + +> +Z1255463756 + +> +178.163 + +> +1.005 + +> +1 + +> +41.460 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1949840 + +> +0.88 + +$$$$ +Compound 113 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2904 -0.7339 0.0 0 +M V30 3 N 2.5808 0.0236 0.0 0 +M V30 4 C 1.2785 -2.2256 0.0 0 +M V30 5 C -0.0355 -2.9596 0.0 0 +M V30 6 C 2.5689 -2.9596 0.0 0 +M V30 7 N -1.4798 -2.486 0.0 0 +M V30 8 C -0.0473 -4.4513 0.0 0 +M V30 9 C 2.5571 -4.4513 0.0 0 +M V30 10 C -2.3795 -3.6936 0.0 0 +M V30 11 N -1.4916 -4.9011 0.0 0 +M V30 12 C 1.243 -5.1971 0.0 0 +M V30 13 C -3.8948 -3.6817 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 8 12 +M V30 12 1 10 13 +M V30 13 1 9 12 +M V30 14 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +113 + +> +Z1255403177 + +> +175.187 + +> +0.841 + +> +2 + +> +71.770 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL133694 + +> +0.86 + +$$$$ +Compound 114 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5079 0.0 0 +M V30 3 N -1.3194 2.2737 0.0 0 +M V30 4 C 1.2723 2.2737 0.0 0 +M V30 5 C -1.3312 3.7817 0.0 0 +M V30 6 C 1.2605 3.7817 0.0 0 +M V30 7 C 2.5564 1.5315 0.0 0 +M V30 8 N -0.0471 4.5474 0.0 0 +M V30 9 C 2.5447 4.5474 0.0 0 +M V30 10 C 3.8406 2.2972 0.0 0 +M V30 11 C 3.8288 3.8052 0.0 0 +M V30 12 C 5.1129 4.571 0.0 0 +M V30 13 N 6.397 5.3368 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 2 9 11 +M V30 11 1 11 12 +M V30 12 3 12 13 +M V30 13 1 6 8 +M V30 14 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +114 + +> +Z1675167200 + +> +171.156 + +> +-0.127 + +> +1 + +> +65.250 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL266540 + +> +0.89 + +$$$$ +Compound 115 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 15 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 N 7.7567 0.5675 0.0 0 +M V30 3 C 6.2432 0.5793 0.0 0 +M V30 4 C 8.206 -0.8513 0.0 0 +M V30 5 C 8.7499 1.6908 0.0 0 +M V30 6 C 5.7702 -0.8395 0.0 0 +M V30 7 C 5.2263 1.7026 0.0 0 +M V30 8 C 6.9763 -1.7263 0.0 0 +M V30 9 C 9.6604 -1.1469 0.0 0 +M V30 10 C 10.2043 1.3952 0.0 0 +M V30 11 C 4.2922 -1.1351 0.0 0 +M V30 12 C 3.7482 1.407 0.0 0 +M V30 13 N 10.6536 -0.0236 0.0 0 +M V30 14 C 3.2753 -0.0118 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 2 3 +M V30 2 1 2 4 +M V30 3 1 2 5 +M V30 4 2 3 6 +M V30 5 1 3 7 +M V30 6 2 4 8 +M V30 7 1 4 9 +M V30 8 1 5 10 +M V30 9 1 6 11 +M V30 10 2 7 12 +M V30 11 1 9 13 +M V30 12 2 11 14 +M V30 13 1 6 8 +M V30 14 1 10 13 +M V30 15 1 12 14 +M V30 END BOND +M V30 END CTAB +M END +> +115 + +> +Z2188172213 + +> +172.226 + +> +1.831 + +> +1 + +> +16.960 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL417002 + +> +1.0 + +$$$$ +Compound 116 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5078 0.0 0 +M V30 3 N -1.3193 2.2735 0.0 0 +M V30 4 C 1.2722 2.2735 0.0 0 +M V30 5 C -1.3311 3.7813 0.0 0 +M V30 6 C 1.2604 3.7813 0.0 0 +M V30 7 C 2.5562 1.5313 0.0 0 +M V30 8 C -0.0471 4.5352 0.0 0 +M V30 9 C 2.5444 4.5352 0.0 0 +M V30 10 C 3.8402 2.297 0.0 0 +M V30 11 C -0.0588 6.043 0.0 0 +M V30 12 C 3.8284 3.8049 0.0 0 +M V30 13 N -0.0706 7.5509 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 3 11 13 +M V30 13 1 6 8 +M V30 14 1 10 12 +M V30 END BOND +M V30 END CTAB +M END +> +116 + +> +Z1255422871 + +> +170.167 + +> +0.700 + +> +1 + +> +52.890 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1173657 + +> +0.85 + +$$$$ +Compound 117 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3075 -0.7421 0.0 0 +M V30 3 N -1.3193 -2.2263 0.0 0 +M V30 4 N -2.6151 0.0235 0.0 0 +M V30 5 C -2.6269 -2.9685 0.0 0 +M V30 6 C -3.9226 -0.7185 0.0 0 +M V30 7 O -2.6386 -4.4527 0.0 0 +M V30 8 C -3.9344 -2.2028 0.0 0 +M V30 9 C -5.2302 0.0471 0.0 0 +M V30 10 C -5.242 -2.9449 0.0 0 +M V30 11 C -6.5378 -0.695 0.0 0 +M V30 12 N -5.2538 -4.4292 0.0 0 +M V30 13 C -6.5495 -2.1792 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 1 10 12 +M V30 12 2 10 13 +M V30 13 2 6 8 +M V30 14 1 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +117 + +> +Z1262634750 + +> +177.160 + +> +-0.314 + +> +3 + +> +84.220 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL421646 + +> +0.95 + +$$$$ +Compound 118 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5081 0.0 0 +M V30 3 N -1.3196 2.2739 0.0 0 +M V30 4 C 1.2724 2.2739 0.0 0 +M V30 5 C -1.3314 3.7821 0.0 0 +M V30 6 C -2.6274 1.5317 0.0 0 +M V30 7 C 1.2607 3.7821 0.0 0 +M V30 8 C 2.5567 1.5317 0.0 0 +M V30 9 C -0.0471 4.5362 0.0 0 +M V30 10 C 2.5449 4.5362 0.0 0 +M V30 11 C 3.841 2.2975 0.0 0 +M V30 12 N 2.5332 6.0443 0.0 0 +M V30 13 C 3.8292 3.8056 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 10 12 +M V30 12 2 10 13 +M V30 13 1 7 9 +M V30 14 1 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +118 + +> +Z1262625575 + +> +174.199 + +> +0.446 + +> +1 + +> +46.330 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL446240 + +> +0.9 + +$$$$ +Compound 119 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2902 -0.7457 0.0 0 +M V30 3 N 1.2784 -2.2372 0.0 0 +M V30 4 C 2.5805 0.0236 0.0 0 +M V30 5 C 3.8708 -0.722 0.0 0 +M V30 6 C 2.5687 1.5388 0.0 0 +M V30 7 C 5.161 0.0473 0.0 0 +M V30 8 C 3.8589 2.3082 0.0 0 +M V30 9 C 5.1492 1.5625 0.0 0 +M V30 10 C 6.4513 -0.6984 0.0 0 +M V30 11 C 6.4395 2.3319 0.0 0 +M V30 12 N 7.7416 0.071 0.0 0 +M V30 13 C 7.7297 1.5862 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 1 8 9 +M V30 14 1 12 13 +M V30 END BOND +M V30 END CTAB +M END +> +119 + +> +Z1262603067 + +> +176.215 + +> +0.108 + +> +2 + +> +55.120 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL125200 + +> +0.88 + +$$$$ +Compound 120 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 15 0 0 0 +M V30 BEGIN ATOM +M V30 1 N 0.0 0.0 0.0 0 +M V30 2 C -1.1467 0.9712 0.0 0 +M V30 3 C -0.5733 -1.3691 0.0 0 +M V30 4 C 1.451 0.3042 0.0 0 +M V30 5 C -1.1233 2.469 0.0 0 +M V30 6 C -2.4222 0.1989 0.0 0 +M V30 7 C -2.0712 -1.252 0.0 0 +M V30 8 C 2.118 1.6382 0.0 0 +M V30 9 C -2.5392 3.1828 0.0 0 +M V30 10 C 0.0468 3.3701 0.0 0 +M V30 11 C -3.7445 0.9127 0.0 0 +M V30 12 N 1.4978 3.0073 0.0 0 +M V30 13 C -3.8147 2.4105 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 1 4 +M V30 4 2 2 5 +M V30 5 1 2 6 +M V30 6 2 3 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 1 5 10 +M V30 10 2 6 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 6 7 +M V30 14 1 10 12 +M V30 15 1 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +120 + +> +Z1262563874 + +> +172.226 + +> +1.831 + +> +1 + +> +16.960 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL366295 + +> +0.85 + +$$$$ +Compound 121 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.3077 -0.7304 0.0 0 +M V30 3 C -2.6155 0.0235 0.0 0 +M V30 4 C -1.3195 -2.2149 0.0 0 +M V30 5 C -3.9233 -0.7069 0.0 0 +M V30 6 C -2.6273 -2.9454 0.0 0 +M V30 7 S -5.3606 -0.2356 0.0 0 +M V30 8 C -3.935 -2.1913 0.0 0 +M V30 9 O -6.6684 0.5301 0.0 0 +M V30 10 O -4.7598 1.1428 0.0 0 +M V30 11 N -6.256 -1.4373 0.0 0 +M V30 12 C -5.3724 -2.6391 0.0 0 +M V30 13 O -5.8437 -4.0529 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 2 7 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 2 12 13 +M V30 13 1 6 8 +M V30 14 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +121 + +> +Z1269206304 + +> +217.630 + +> +1.685 + +> +1 + +> +63.240 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1967612 + +> +0.94 + +$$$$ +Compound 122 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5082 0.0 0 +M V30 3 N -1.3196 2.2741 0.0 0 +M V30 4 C 1.2725 2.2741 0.0 0 +M V30 5 C -1.3314 3.7823 0.0 0 +M V30 6 C -2.6275 1.5317 0.0 0 +M V30 7 C 1.2607 3.7823 0.0 0 +M V30 8 C 2.5568 1.5317 0.0 0 +M V30 9 N -0.0471 4.5482 0.0 0 +M V30 10 C 2.5451 4.5482 0.0 0 +M V30 11 C 3.8412 2.2976 0.0 0 +M V30 12 C 3.8294 3.8058 0.0 0 +M V30 13 O 5.1255 1.5553 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 2 10 12 +M V30 12 1 11 13 +M V30 13 1 7 9 +M V30 14 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +122 + +> +Z1278927493 + +> +176.172 + +> +0.730 + +> +1 + +> +52.900 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1949846 + +> +0.9 + +$$$$ +Compound 123 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4946 0.0 0 +M V30 3 F 1.4828 -1.4828 0.0 0 +M V30 4 F -1.5302 -1.4828 0.0 0 +M V30 5 C -0.0237 -2.9893 0.0 0 +M V30 6 O 1.2692 -3.7248 0.0 0 +M V30 7 O -1.3404 -3.7248 0.0 0 +M V30 8 O -10.6288 0.2847 0.0 0 +M V30 9 C -10.1781 -1.1388 0.0 0 +M V30 10 N -11.0796 -2.3487 0.0 0 +M V30 11 C -8.7545 -1.5895 0.0 0 +M V30 12 C -10.2018 -3.5587 0.0 0 +M V30 13 C -8.7664 -3.0842 0.0 0 +M V30 14 C -7.4615 -0.8303 0.0 0 +M V30 15 O -10.6763 -4.9822 0.0 0 +M V30 16 C -7.4734 -3.8316 0.0 0 +M V30 17 C -6.1685 -1.5658 0.0 0 +M V30 18 C -6.1804 -3.0724 0.0 0 +M V30 19 C -4.8755 -0.8066 0.0 0 +M V30 20 N -4.8873 0.7117 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 2 5 6 +M V30 6 1 5 7 +M V30 7 2 8 9 +M V30 8 1 9 10 +M V30 9 1 9 11 +M V30 10 1 10 12 +M V30 11 2 11 13 +M V30 12 1 11 14 +M V30 13 2 12 15 +M V30 14 1 13 16 +M V30 15 2 14 17 +M V30 16 2 16 18 +M V30 17 1 17 19 +M V30 18 1 19 20 +M V30 19 1 12 13 +M V30 20 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +123 + +> +Z2854589464 + +> +176.172 + +> +0.100 + +> +2 + +> +72.190 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL611975 + +> +0.85 + +$$$$ +Compound 124 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5096 0.0 0 +M V30 3 N -1.3209 2.2763 0.0 0 +M V30 4 C 1.2737 2.2763 0.0 0 +M V30 5 C -1.3327 3.786 0.0 0 +M V30 6 C 1.262 3.786 0.0 0 +M V30 7 C 2.5593 1.5332 0.0 0 +M V30 8 C -0.0471 4.5526 0.0 0 +M V30 9 C 2.5475 4.5526 0.0 0 +M V30 10 C 3.8449 2.2999 0.0 0 +M V30 11 C 3.8331 3.8095 0.0 0 +M V30 12 C 5.1305 1.5568 0.0 0 +M V30 13 N 6.4161 0.8138 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 2 9 11 +M V30 11 1 10 12 +M V30 12 3 12 13 +M V30 13 1 6 8 +M V30 14 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +124 + +> +Z1486007497 + +> +172.183 + +> +0.897 + +> +1 + +> +52.890 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1688212 + +> +0.87 + +$$$$ +Compound 125 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4963 0.0 0 +M V30 3 C 1.2825 -2.2326 0.0 0 +M V30 4 C -1.33 -2.2326 0.0 0 +M V30 5 C 1.2706 -3.7289 0.0 0 +M V30 6 C 2.577 -1.4725 0.0 0 +M V30 7 C -1.3419 -3.7289 0.0 0 +M V30 8 C 2.5651 -4.4771 0.0 0 +M V30 9 C -0.0475 -4.4771 0.0 0 +M V30 10 C 3.8714 -2.2088 0.0 0 +M V30 11 C -2.6601 -4.4771 0.0 0 +M V30 12 O 2.5532 -5.9734 0.0 0 +M V30 13 N 3.8595 -3.7051 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 1 7 9 +M V30 14 1 10 13 +M V30 END BOND +M V30 END CTAB +M END +> +125 + +> +Z1513697130 + +> +240.097 + +> +2.560 + +> +1 + +> +29.100 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3629673 + +> +0.87 + +$$$$ +Compound 126 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4911 0.0 0 +M V30 3 C -1.3254 -2.2248 0.0 0 +M V30 4 C 1.2781 -2.2248 0.0 0 +M V30 5 C -2.7692 -1.7514 0.0 0 +M V30 6 C -1.3372 -3.7159 0.0 0 +M V30 7 C 1.2662 -3.7159 0.0 0 +M V30 8 O -3.2426 -0.3076 0.0 0 +M V30 9 N -3.6686 -2.9585 0.0 0 +M V30 10 S -2.781 -4.1656 0.0 0 +M V30 11 C -0.0473 -4.4497 0.0 0 +M V30 12 O -3.8935 -5.1716 0.0 0 +M V30 13 O -2.497 -5.6331 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 6 11 +M V30 11 2 10 12 +M V30 12 2 10 13 +M V30 13 1 7 11 +M V30 14 1 9 10 +M V30 END BOND +M V30 END CTAB +M END +> +126 + +> +Z1862012665 + +> +217.630 + +> +1.685 + +> +1 + +> +63.240 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1967612 + +> +0.89 + +$$$$ +Compound 127 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4904 0.0 0 +M V30 3 C -1.3248 -2.2238 0.0 0 +M V30 4 C 1.2775 -2.2238 0.0 0 +M V30 5 S -2.7679 -1.7506 0.0 0 +M V30 6 C -1.3366 -3.7142 0.0 0 +M V30 7 C 1.2656 -3.7142 0.0 0 +M V30 8 O -4.0809 -0.9936 0.0 0 +M V30 9 O -2.1646 -0.3666 0.0 0 +M V30 10 N -3.6669 -2.9572 0.0 0 +M V30 11 C -2.7797 -4.1637 0.0 0 +M V30 12 C -0.0473 -4.4476 0.0 0 +M V30 13 O -3.2529 -5.5832 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 2 5 9 +M V30 9 1 5 10 +M V30 10 1 6 11 +M V30 11 2 6 12 +M V30 12 2 11 13 +M V30 13 1 7 12 +M V30 14 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +127 + +> +Z1862012659 + +> +217.630 + +> +1.685 + +> +1 + +> +63.240 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1967612 + +> +0.93 + +$$$$ +Compound 128 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 O 6.7957 -2.8315 0.0 0 +M V30 3 C 5.4861 -2.0764 0.0 0 +M V30 4 N 4.1765 -2.8079 0.0 0 +M V30 5 C 5.4743 -0.5663 0.0 0 +M V30 6 C 6.7603 0.1887 0.0 0 +M V30 7 C 4.1647 0.1887 0.0 0 +M V30 8 C 6.7485 1.6989 0.0 0 +M V30 9 C 8.0463 -0.5427 0.0 0 +M V30 10 C 4.1529 1.6989 0.0 0 +M V30 11 C 5.4389 2.454 0.0 0 +M V30 12 C 8.0345 2.454 0.0 0 +M V30 13 C 9.3323 0.2123 0.0 0 +M V30 14 N 9.3205 1.7225 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 1 3 4 +M V30 3 1 3 5 +M V30 4 2 5 6 +M V30 5 1 5 7 +M V30 6 1 6 8 +M V30 7 1 6 9 +M V30 8 2 7 10 +M V30 9 2 8 11 +M V30 10 1 8 12 +M V30 11 1 9 13 +M V30 12 1 12 14 +M V30 13 1 10 11 +M V30 14 1 13 14 +M V30 END BOND +M V30 END CTAB +M END +> +128 + +> +Z2583130856 + +> +176.215 + +> +0.108 + +> +2 + +> +55.120 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1688212 + +> +0.88 + +$$$$ +Compound 129 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4946 0.0 0 +M V30 3 C 1.2811 -2.23 0.0 0 +M V30 4 C -1.3285 -2.23 0.0 0 +M V30 5 C 2.574 -1.4709 0.0 0 +M V30 6 C 1.2692 -3.7247 0.0 0 +M V30 7 C -1.3404 -3.7247 0.0 0 +M V30 8 O 2.5622 0.0474 0.0 0 +M V30 9 N 3.867 -2.2063 0.0 0 +M V30 10 N 2.5622 -4.472 0.0 0 +M V30 11 C -0.0474 -4.472 0.0 0 +M V30 12 Cl -2.6571 -4.472 0.0 0 +M V30 13 C 3.8552 -3.701 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 6 11 +M V30 11 1 7 12 +M V30 12 1 9 13 +M V30 13 1 7 11 +M V30 14 2 10 13 +M V30 END BOND +M V30 END CTAB +M END +> +129 + +> +Z1694627901 + +> +215.036 + +> +1.809 + +> +1 + +> +41.460 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1949841 + +> +0.96 + +$$$$ +Compound 130 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3122 -0.7329 0.0 0 +M V30 3 N -2.6244 0.0236 0.0 0 +M V30 4 C -1.324 -2.2225 0.0 0 +M V30 5 C -2.6363 1.5368 0.0 0 +M V30 6 C -2.6363 -2.9555 0.0 0 +M V30 7 C -0.0354 -2.9555 0.0 0 +M V30 8 N -4.0785 -2.4826 0.0 0 +M V30 9 C -2.6481 -4.445 0.0 0 +M V30 10 C -0.0472 -4.445 0.0 0 +M V30 11 N -4.977 -3.6884 0.0 0 +M V30 12 C -1.3595 -5.178 0.0 0 +M V30 13 C -4.0904 -4.8943 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 6 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 1 10 12 +M V30 14 2 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +130 + +> +Z332659752 + +> +175.187 + +> +0.995 + +> +2 + +> +57.780 + +> +1 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL451401 + +> +0.9 + +$$$$ +Compound 131 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.4147 0.4715 0.0 0 +M V30 3 C -0.896 1.2261 0.0 0 +M V30 4 N 2.8295 0.0235 0.0 0 +M V30 5 C 1.403 1.9807 0.0 0 +M V30 6 C -2.4051 1.2379 0.0 0 +M V30 7 C -0.0235 2.4523 0.0 0 +M V30 8 N 3.702 1.2497 0.0 0 +M V30 9 C 3.2775 -1.3912 0.0 0 +M V30 10 C 2.8177 2.4523 0.0 0 +M V30 11 O -3.1596 2.5466 0.0 0 +M V30 12 C -3.1596 -0.0471 0.0 0 +M V30 13 C 3.2658 3.8906 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 1 3 6 +M V30 6 2 3 7 +M V30 7 1 4 8 +M V30 8 1 4 9 +M V30 9 1 5 10 +M V30 10 2 6 11 +M V30 11 1 6 12 +M V30 12 1 10 13 +M V30 13 1 5 7 +M V30 14 2 8 10 +M V30 END BOND +M V30 END CTAB +M END +> +131 + +> +Z2010010294 + +> +194.254 + +> +1.464 + +> +0 + +> +34.890 + +> +1 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1986588 + +> +0.86 + +$$$$ +Compound 132 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 13 14 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5022 0.0 0 +M V30 3 C -1.3145 2.2651 0.0 0 +M V30 4 C 1.2675 2.2651 0.0 0 +M V30 5 C -2.6172 1.5257 0.0 0 +M V30 6 C -1.3262 3.7674 0.0 0 +M V30 7 C 1.2558 3.7674 0.0 0 +M V30 8 O -2.629 0.0469 0.0 0 +M V30 9 N -3.92 2.2886 0.0 0 +M V30 10 N -2.629 4.5303 0.0 0 +M V30 11 C -0.0469 4.5303 0.0 0 +M V30 12 C -3.9317 3.7909 0.0 0 +M V30 13 C -5.2228 1.5492 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 6 11 +M V30 11 1 9 12 +M V30 12 1 9 13 +M V30 13 1 7 11 +M V30 14 2 10 12 +M V30 END BOND +M V30 END CTAB +M END +> +132 + +> +Z1328990525 + +> +178.163 + +> +0.803 + +> +0 + +> +32.670 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1949840 + +> +0.92 + +$$$$ +Compound 133 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 15 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2943 -0.7481 0.0 0 +M V30 3 N 1.2824 -2.2443 0.0 0 +M V30 4 N 2.5886 0.0237 0.0 0 +M V30 5 C 2.5768 -2.9924 0.0 0 +M V30 6 C -0.0356 -2.9924 0.0 0 +M V30 7 C 3.883 -0.7243 0.0 0 +M V30 8 O 2.5649 -4.4886 0.0 0 +M V30 9 C 3.8711 -2.2205 0.0 0 +M V30 10 C -0.0474 -4.4886 0.0 0 +M V30 11 C 5.1773 0.0474 0.0 0 +M V30 12 C 5.1655 -2.9686 0.0 0 +M V30 13 C 6.4717 -0.7006 0.0 0 +M V30 14 C 6.4598 -2.1968 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 9 12 +M V30 12 2 11 13 +M V30 13 2 12 14 +M V30 14 2 7 9 +M V30 15 1 13 14 +M V30 END BOND +M V30 END CTAB +M END +> +133 + +> +Z57440638 + +> +190.199 + +> +1.913 + +> +1 + +> +49.410 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL421646 + +> +0.86 + +$$$$ +Compound 134 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.2318 -0.8715 0.0 0 +M V30 3 C 1.2085 -0.8715 0.0 0 +M V30 4 N -2.7193 -0.5461 0.0 0 +M V30 5 C -0.7786 -2.2893 0.0 0 +M V30 6 C 0.7321 -2.2893 0.0 0 +M V30 7 C 2.6728 -0.5461 0.0 0 +M V30 8 C -3.7419 -1.6501 0.0 0 +M V30 9 C -1.8012 -3.3933 0.0 0 +M V30 10 C 1.7315 -3.3933 0.0 0 +M V30 11 C 3.6722 -1.6501 0.0 0 +M V30 12 N -3.2887 -3.0679 0.0 0 +M V30 13 O -1.348 -4.8111 0.0 0 +M V30 14 C 3.1958 -3.0679 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 10 14 +M V30 14 1 5 6 +M V30 15 1 9 12 +M V30 16 1 11 14 +M V30 END BOND +M V30 END CTAB +M END +> +134 + +> +Z56757191 + +> +206.264 + +> +1.696 + +> +1 + +> +41.460 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL539906 + +> +1.0 + +$$$$ +Compound 135 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 15 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3051 0.7642 0.0 0 +M V30 3 O -2.6102 0.0235 0.0 0 +M V30 4 C -1.3168 2.2692 0.0 0 +M V30 5 C -0.0352 3.0335 0.0 0 +M V30 6 C -2.622 3.0335 0.0 0 +M V30 7 C -0.047 4.5385 0.0 0 +M V30 8 C 1.2463 2.2927 0.0 0 +M V30 9 C -2.6337 4.5385 0.0 0 +M V30 10 N -1.3521 5.3028 0.0 0 +M V30 11 C 1.2345 5.3028 0.0 0 +M V30 12 C 2.5279 3.057 0.0 0 +M V30 13 O -3.9389 5.3028 0.0 0 +M V30 14 C 2.5161 4.562 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 4 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 7 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 1 11 14 +M V30 14 2 9 10 +M V30 15 2 12 14 +M V30 END BOND +M V30 END CTAB +M END +> +135 + +> +Z57070446 + +> +189.167 + +> +2.522 + +> +2 + +> +70.420 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1205058 + +> +0.85 + +$$$$ +Compound 136 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.2416 -0.8513 0.0 0 +M V30 3 C 1.1824 -0.8986 0.0 0 +M V30 4 N -2.7197 -0.4966 0.0 0 +M V30 5 C -0.8159 -2.2821 0.0 0 +M V30 6 C 0.674 -2.3058 0.0 0 +M V30 7 C 2.6724 -0.9223 0.0 0 +M V30 8 C -3.7602 -1.5845 0.0 0 +M V30 9 C -1.8564 -3.37 0.0 0 +M V30 10 C 1.8564 -3.2045 0.0 0 +M V30 11 C 3.098 -2.3531 0.0 0 +M V30 12 N -3.3345 -3.0153 0.0 0 +M V30 13 C -5.2383 -1.2297 0.0 0 +M V30 14 O -1.4307 -4.8008 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 5 6 +M V30 15 1 9 12 +M V30 16 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +136 + +> +Z55164934 + +> +206.264 + +> +1.636 + +> +1 + +> +41.460 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL539906 + +> +0.89 + +$$$$ +Compound 137 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 0.0939 1.5033 0.0 0 +M V30 3 C -1.4563 -0.3406 0.0 0 +M V30 4 N -1.3036 2.0788 0.0 0 +M V30 5 N 1.3506 2.302 0.0 0 +M V30 6 C -2.255 0.9395 0.0 0 +M V30 7 C -2.1728 -1.6442 0.0 0 +M V30 8 C -3.7583 0.9043 0.0 0 +M V30 9 C -3.6761 -1.6795 0.0 0 +M V30 10 C -4.4748 -0.3993 0.0 0 +M V30 11 C -4.557 2.1845 0.0 0 +M V30 12 C -5.9781 -0.4345 0.0 0 +M V30 13 C -6.0603 2.1493 0.0 0 +M V30 14 C -6.7768 0.8456 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 1 6 8 +M V30 8 1 7 9 +M V30 9 2 8 10 +M V30 10 1 8 11 +M V30 11 1 10 12 +M V30 12 2 11 13 +M V30 13 2 12 14 +M V30 14 1 4 6 +M V30 15 1 9 10 +M V30 16 1 13 14 +M V30 END BOND +M V30 END CTAB +M END +> +137 + +> +Z48847686 + +> +202.276 + +> +2.781 + +> +1 + +> +38.910 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1178727 + +> +0.87 + +$$$$ +Compound 138 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 15 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5067 0.0 0 +M V30 3 N -1.3183 2.2718 0.0 0 +M V30 4 C 1.2712 2.2718 0.0 0 +M V30 5 N -1.3301 3.7785 0.0 0 +M V30 6 C 1.2595 3.7785 0.0 0 +M V30 7 C 2.5543 1.5302 0.0 0 +M V30 8 C -0.047 4.5318 0.0 0 +M V30 9 C 2.5425 4.5318 0.0 0 +M V30 10 C 3.8373 2.2953 0.0 0 +M V30 11 C -0.0588 6.0385 0.0 0 +M V30 12 C 3.8256 3.802 0.0 0 +M V30 13 C -1.3654 6.7919 0.0 0 +M V30 14 N -2.672 7.557 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 11 13 +M V30 13 3 13 14 +M V30 14 1 6 8 +M V30 15 1 10 12 +M V30 END BOND +M V30 END CTAB +M END +> +138 + +> +Z56956071 + +> +185.182 + +> +-0.423 + +> +1 + +> +65.250 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL66761 + +> +0.89 + +$$$$ +Compound 139 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4492 1.4421 0.0 0 +M V30 3 N -0.5674 2.5652 0.0 0 +M V30 4 C 1.9032 1.7613 0.0 0 +M V30 5 C -0.1182 4.0074 0.0 0 +M V30 6 O 3.1089 0.8865 0.0 0 +M V30 7 C 2.3524 3.2035 0.0 0 +M V30 8 N 1.3358 4.3265 0.0 0 +M V30 9 C 4.3147 1.785 0.0 0 +M V30 10 C 3.8419 3.2153 0.0 0 +M V30 11 C 5.7687 1.4894 0.0 0 +M V30 12 C 4.8348 4.3383 0.0 0 +M V30 13 C 6.7617 2.6124 0.0 0 +M V30 14 C 6.2889 4.0428 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 2 11 13 +M V30 13 2 12 14 +M V30 14 1 7 8 +M V30 15 2 9 10 +M V30 16 1 13 14 +M V30 END BOND +M V30 END CTAB +M END +> +139 + +> +Z62452555 + +> +186.167 + +> +0.865 + +> +1 + +> +54.600 + +> +0 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1992323 + +> +0.94 + +$$$$ +Compound 140 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 15 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3085 -0.7427 0.0 0 +M V30 3 N -1.3203 -2.2281 0.0 0 +M V30 4 N -2.6171 0.0235 0.0 0 +M V30 5 C -2.6289 -2.9708 0.0 0 +M V30 6 C -0.0353 -2.9708 0.0 0 +M V30 7 C -3.9257 -0.7191 0.0 0 +M V30 8 C -2.6289 1.5325 0.0 0 +M V30 9 O -2.6407 -4.4562 0.0 0 +M V30 10 C -3.9375 -2.2045 0.0 0 +M V30 11 N -5.364 -0.2475 0.0 0 +M V30 12 N -5.3758 -2.6525 0.0 0 +M V30 13 C -6.26 -1.45 0.0 0 +M V30 14 C -5.8474 -4.0672 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 5 10 +M V30 10 1 7 11 +M V30 11 1 10 12 +M V30 12 2 11 13 +M V30 13 1 12 14 +M V30 14 2 7 10 +M V30 15 1 12 13 +M V30 END BOND +M V30 END CTAB +M END +> +140 + +> +Z104507896 + +> +194.191 + +> +-0.040 + +> +0 + +> +58.440 + +> +0 + +> +Serine-protein kinase ATM + +> +CHEMBL113 + +> +1.0 + +$$$$ +Compound 141 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 15 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.4148 0.4716 0.0 0 +M V30 3 C -0.896 1.2262 0.0 0 +M V30 4 N 2.7 -0.2711 0.0 0 +M V30 5 N 1.403 1.9807 0.0 0 +M V30 6 C -0.0235 2.4524 0.0 0 +M V30 7 C -2.4052 1.2379 0.0 0 +M V30 8 C 3.9851 0.4951 0.0 0 +M V30 9 C 2.6882 2.7471 0.0 0 +M V30 10 C 3.9733 2.0043 0.0 0 +M V30 11 O 2.6764 4.2563 0.0 0 +M V30 12 C 5.2585 2.7707 0.0 0 +M V30 13 O 5.2467 4.2799 0.0 0 +M V30 14 O 6.5437 2.0397 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 2 8 10 +M V30 10 2 9 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 5 6 +M V30 15 1 9 10 +M V30 END BOND +M V30 END CTAB +M END +> +141 + +> +Z57301586 + +> +210.210 + +> +0.635 + +> +1 + +> +69.970 + +> +1 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1971679 + +> +0.87 + +$$$$ +Compound 142 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 15 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 O 8.3526 2.9424 0.0 0 +M V30 3 C 8.8034 1.5186 0.0 0 +M V30 4 N 7.9017 0.3084 0.0 0 +M V30 5 C 10.2272 1.0678 0.0 0 +M V30 6 C 8.7797 -0.9017 0.0 0 +M V30 7 C 6.3831 0.3203 0.0 0 +M V30 8 C 10.2153 -0.4271 0.0 0 +M V30 9 C 11.5204 1.8271 0.0 0 +M V30 10 O 8.3051 -2.3254 0.0 0 +M V30 11 C 5.6237 -0.9728 0.0 0 +M V30 12 C 11.5086 -1.1627 0.0 0 +M V30 13 C 12.8137 1.0796 0.0 0 +M V30 14 N 4.1051 -0.961 0.0 0 +M V30 15 C 12.8018 -0.4033 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 1 3 4 +M V30 3 1 3 5 +M V30 4 1 4 6 +M V30 5 1 4 7 +M V30 6 2 5 8 +M V30 7 1 5 9 +M V30 8 2 6 10 +M V30 9 1 7 11 +M V30 10 1 8 12 +M V30 11 2 9 13 +M V30 12 1 11 14 +M V30 13 2 12 15 +M V30 14 1 6 8 +M V30 15 1 13 15 +M V30 END BOND +M V30 END CTAB +M END +> +142 + +> +Z812516920 + +> +190.199 + +> +0.752 + +> +1 + +> +63.400 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL611975 + +> +0.85 + +$$$$ +Compound 143 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 15 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.505 0.0 0 +M V30 3 N -1.3168 2.2692 0.0 0 +M V30 4 C 1.2698 2.2692 0.0 0 +M V30 5 C -1.3286 3.7742 0.0 0 +M V30 6 C 1.258 3.7742 0.0 0 +M V30 7 C 2.5514 1.5285 0.0 0 +M V30 8 N -0.047 4.5385 0.0 0 +M V30 9 C -2.6337 4.5385 0.0 0 +M V30 10 C 2.5397 4.5385 0.0 0 +M V30 11 C 3.833 2.2927 0.0 0 +M V30 12 N -3.9389 3.7978 0.0 0 +M V30 13 C 3.8213 3.7978 0.0 0 +M V30 14 C -5.244 4.562 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 1 6 8 +M V30 15 1 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +143 + +> +Z44507989 + +> +189.214 + +> +-0.327 + +> +2 + +> +53.490 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL395092 + +> +0.89 + +$$$$ +Compound 144 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 15 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2933 0.7712 0.0 0 +M V30 3 C -1.3171 0.7712 0.0 0 +M V30 4 C 2.5867 0.0237 0.0 0 +M V30 5 N 3.8801 0.795 0.0 0 +M V30 6 C 5.1735 0.0474 0.0 0 +M V30 7 N 6.4669 0.8187 0.0 0 +M V30 8 C 5.1617 -1.4476 0.0 0 +M V30 9 C 7.7603 0.0711 0.0 0 +M V30 10 N 4.0344 -2.4443 0.0 0 +M V30 11 C 6.4551 -2.1833 0.0 0 +M V30 12 N 7.7484 -1.4239 0.0 0 +M V30 13 C 4.6396 -3.8089 0.0 0 +M V30 14 N 6.1347 -3.6428 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 1 4 5 +M V30 5 1 5 6 +M V30 6 2 6 7 +M V30 7 1 6 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 2 8 11 +M V30 11 2 9 12 +M V30 12 2 10 13 +M V30 13 1 11 14 +M V30 14 1 11 12 +M V30 15 1 13 14 +M V30 END BOND +M V30 END CTAB +M END +> +144 + +> +Z57745220 + +> +193.206 + +> +0.681 + +> +2 + +> +75.720 + +> +4 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL271138 + +> +0.87 + +$$$$ +Compound 145 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 15 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.489 0.0 0 +M V30 3 N 1.2763 -2.2217 0.0 0 +M V30 4 C -1.3236 -2.2217 0.0 0 +M V30 5 C 1.2645 -3.7108 0.0 0 +M V30 6 C -1.3354 -3.7108 0.0 0 +M V30 7 C -2.6353 -1.4654 0.0 0 +M V30 8 N -0.0472 -4.4553 0.0 0 +M V30 9 C -2.6472 -4.4553 0.0 0 +M V30 10 C -3.9471 -2.1981 0.0 0 +M V30 11 C -3.959 -3.6871 0.0 0 +M V30 12 C -5.2707 -4.4317 0.0 0 +M V30 13 O -6.5825 -3.6635 0.0 0 +M V30 14 O -5.2826 -5.9207 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 2 9 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 6 8 +M V30 15 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +145 + +> +Z425007872 + +> +190.156 + +> +0.397 + +> +2 + +> +78.760 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1949851 + +> +0.85 + +$$$$ +Compound 146 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 N 0.0 0.0 0.0 0 +M V30 2 C -1.3054 0.7526 0.0 0 +M V30 3 C -0.0117 -1.4818 0.0 0 +M V30 4 N -1.3171 2.258 0.0 0 +M V30 5 C -2.6108 0.0235 0.0 0 +M V30 6 N -1.3171 -2.2109 0.0 0 +M V30 7 C -2.5402 3.1518 0.0 0 +M V30 8 C -0.1176 3.1518 0.0 0 +M V30 9 N -4.0456 0.4939 0.0 0 +M V30 10 C -2.6226 -1.4583 0.0 0 +M V30 11 C -2.0933 4.5866 0.0 0 +M V30 12 C -0.588 4.5866 0.0 0 +M V30 13 C -4.9394 -0.7056 0.0 0 +M V30 14 N -4.0574 -1.9052 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 4 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 2 5 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 10 14 +M V30 14 1 6 10 +M V30 15 1 11 12 +M V30 16 1 13 14 +M V30 END BOND +M V30 END CTAB +M END +> +146 + +> +Z57745260 + +> +189.217 + +> +0.852 + +> +1 + +> +57.700 + +> +1 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL2420911 + +> +0.87 + +$$$$ +Compound 147 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5072 0.0 0 +M V30 3 N -1.3188 2.2725 0.0 0 +M V30 4 C 1.2717 2.2725 0.0 0 +M V30 5 C -1.3305 3.7797 0.0 0 +M V30 6 C 1.2599 3.7797 0.0 0 +M V30 7 C 2.5551 1.5307 0.0 0 +M V30 8 N -0.0471 4.5333 0.0 0 +M V30 9 C -2.6376 4.5333 0.0 0 +M V30 10 C 2.5434 4.5333 0.0 0 +M V30 11 C 3.8386 2.2961 0.0 0 +M V30 12 C -4.1448 4.5451 0.0 0 +M V30 13 C -3.3912 5.8404 0.0 0 +M V30 14 C 3.8268 3.8033 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 6 8 +M V30 15 1 11 14 +M V30 16 1 12 13 +M V30 END BOND +M V30 END CTAB +M END +> +147 + +> +Z790346658 + +> +186.210 + +> +1.248 + +> +1 + +> +41.460 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL2377256 + +> +0.9 + +$$$$ +Compound 148 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.7841 -1.2712 0.0 0 +M V30 3 N 2.2811 -1.2118 0.0 0 +M V30 4 C 0.0594 -2.59 0.0 0 +M V30 5 C 3.0652 -2.483 0.0 0 +M V30 6 N 0.8435 -3.8612 0.0 0 +M V30 7 C -1.4138 -2.9345 0.0 0 +M V30 8 C 2.3405 -3.8018 0.0 0 +M V30 9 C 4.5622 -2.4236 0.0 0 +M V30 10 C -0.1425 -5.0018 0.0 0 +M V30 11 C -1.5326 -4.4315 0.0 0 +M V30 12 C 3.1246 -5.073 0.0 0 +M V30 13 C 5.3463 -3.6949 0.0 0 +M V30 14 C 4.6216 -5.0136 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 2 12 14 +M V30 14 1 6 8 +M V30 15 2 10 11 +M V30 16 1 13 14 +M V30 END BOND +M V30 END CTAB +M END +> +148 + +> +Z1198153145 + +> +184.194 + +> +1.812 + +> +1 + +> +34.030 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL555751 + +> +1.0 + +$$$$ +Compound 149 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 15 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.3196 -1.4559 0.0 0 +M V30 3 N 1.7755 -1.7755 0.0 0 +M V30 4 C -0.8641 -2.3792 0.0 0 +M V30 5 C 2.4147 -3.1131 0.0 0 +M V30 6 C -0.8759 -3.8707 0.0 0 +M V30 7 C -2.178 -1.6098 0.0 0 +M V30 8 C 1.7518 -4.4507 0.0 0 +M V30 9 C -2.1898 -4.6164 0.0 0 +M V30 10 C 0.284 -4.794 0.0 0 +M V30 11 C -3.4919 -2.3555 0.0 0 +M V30 12 C -3.5037 -3.847 0.0 0 +M V30 13 O -4.8176 -4.5927 0.0 0 +M V30 14 C -4.8295 -6.0842 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 9 12 +M V30 12 1 12 13 +M V30 13 1 13 14 +M V30 14 1 8 10 +M V30 15 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +149 + +> +Z1198154509 + +> +191.226 + +> +1.606 + +> +1 + +> +38.330 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3629677 + +> +0.88 + +$$$$ +Compound 150 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.4577 0.3644 0.0 0 +M V30 3 C 0.094 -1.4812 0.0 0 +M V30 4 N -2.1748 1.6928 0.0 0 +M V30 5 C -2.2571 -0.8934 0.0 0 +M V30 6 C -1.3048 -2.0337 0.0 0 +M V30 7 C -3.6795 1.7516 0.0 0 +M V30 8 C -3.7618 -0.8346 0.0 0 +M V30 9 O -4.3966 3.08 0.0 0 +M V30 10 C -4.4789 0.4937 0.0 0 +M V30 11 C -4.5612 -2.0925 0.0 0 +M V30 12 C -5.9836 0.5525 0.0 0 +M V30 13 C -6.0659 -2.0337 0.0 0 +M V30 14 C -6.783 -0.7053 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 1 10 12 +M V30 12 2 11 13 +M V30 13 2 12 14 +M V30 14 1 5 6 +M V30 15 2 8 10 +M V30 16 1 13 14 +M V30 END BOND +M V30 END CTAB +M END +> +150 + +> +Z1269205479 + +> +201.244 + +> +2.003 + +> +1 + +> +29.100 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3108871 + +> +1.0 + +$$$$ +Compound 151 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 15 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.4244 -0.451 0.0 0 +M V30 3 C -0.9021 -1.2107 0.0 0 +M V30 4 N 2.7183 0.3205 0.0 0 +M V30 5 N 1.4125 -1.9467 0.0 0 +M V30 6 C -0.0237 -2.4215 0.0 0 +M V30 7 C 4.0122 -0.4273 0.0 0 +M V30 8 C 2.7064 -2.6946 0.0 0 +M V30 9 C -0.4985 -3.846 0.0 0 +M V30 10 C 4.0003 -1.923 0.0 0 +M V30 11 O 2.6946 -4.1902 0.0 0 +M V30 12 C 5.2942 -2.6708 0.0 0 +M V30 13 O 6.5881 -1.8992 0.0 0 +M V30 14 O 5.2823 -4.1665 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 2 8 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 5 6 +M V30 15 1 8 10 +M V30 END BOND +M V30 END CTAB +M END +> +151 + +> +Z1269235931 + +> +210.210 + +> +0.635 + +> +1 + +> +69.970 + +> +1 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1971679 + +> +0.9 + +$$$$ +Compound 152 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 15 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4477 1.4375 0.0 0 +M V30 3 N -0.4477 2.663 0.0 0 +M V30 4 C 1.8617 1.9088 0.0 0 +M V30 5 C 0.4241 3.8884 0.0 0 +M V30 6 C -1.956 2.6748 0.0 0 +M V30 7 C 1.8499 3.4171 0.0 0 +M V30 8 C 3.1461 1.1665 0.0 0 +M V30 9 O -0.0471 5.326 0.0 0 +M V30 10 C 3.1343 4.183 0.0 0 +M V30 11 C 4.4305 1.9324 0.0 0 +M V30 12 C 4.4187 3.4525 0.0 0 +M V30 13 C 5.7148 1.1901 0.0 0 +M V30 14 N 6.9992 1.956 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 2 10 12 +M V30 12 1 11 13 +M V30 13 1 13 14 +M V30 14 1 5 7 +M V30 15 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +152 + +> +Z1416185658 + +> +190.199 + +> +0.466 + +> +1 + +> +63.400 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL611975 + +> +0.95 + +$$$$ +Compound 153 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 15 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.2944 -0.7481 0.0 0 +M V30 3 C 1.2825 -2.2444 0.0 0 +M V30 4 C 2.5888 0.0237 0.0 0 +M V30 5 C 2.5769 -2.9926 0.0 0 +M V30 6 C 3.8832 -0.7244 0.0 0 +M V30 7 C 3.8713 -2.2207 0.0 0 +M V30 8 C 2.565 -4.4889 0.0 0 +M V30 9 C 5.1776 0.0475 0.0 0 +M V30 10 N 5.1658 -2.9688 0.0 0 +M V30 11 O 3.8595 -5.237 0.0 0 +M V30 12 N 1.2469 -5.237 0.0 0 +M V30 13 C 6.4721 -0.7006 0.0 0 +M V30 14 C 6.4602 -2.1969 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 2 3 +M V30 3 2 2 4 +M V30 4 2 3 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 2 7 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 1 6 7 +M V30 15 2 13 14 +M V30 END BOND +M V30 END CTAB +M END +> +153 + +> +Z1416163215 + +> +190.174 + +> +1.227 + +> +1 + +> +55.980 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL502330 + +> +0.86 + +$$$$ +Compound 154 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 15 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3063 0.7649 0.0 0 +M V30 3 O -2.6126 0.0235 0.0 0 +M V30 4 C -1.318 2.2713 0.0 0 +M V30 5 C -0.0353 3.0362 0.0 0 +M V30 6 C -2.6243 3.0362 0.0 0 +M V30 7 C -0.047 4.5426 0.0 0 +M V30 8 C 1.2474 2.2948 0.0 0 +M V30 9 C -2.6361 4.5426 0.0 0 +M V30 10 C 1.2356 5.3075 0.0 0 +M V30 11 C -1.3533 5.3075 0.0 0 +M V30 12 N 2.5302 3.0598 0.0 0 +M V30 13 C 2.5184 4.5661 0.0 0 +M V30 14 O 3.8012 5.3311 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 4 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 10 13 +M V30 13 1 13 14 +M V30 14 2 9 11 +M V30 15 2 12 13 +M V30 END BOND +M V30 END CTAB +M END +> +154 + +> +Z1486032512 + +> +189.167 + +> +2.196 + +> +2 + +> +70.420 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL45072 + +> +0.85 + +$$$$ +Compound 155 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.7003 -1.3175 0.0 0 +M V30 3 N 2.1959 -1.3531 0.0 0 +M V30 4 C -0.1068 -2.5876 0.0 0 +M V30 5 C 2.8962 -2.6707 0.0 0 +M V30 6 C 0.5934 -3.9051 0.0 0 +M V30 7 C -1.6261 -2.5282 0.0 0 +M V30 8 N 2.089 -3.9407 0.0 0 +M V30 9 C 4.3443 -3.0149 0.0 0 +M V30 10 C -0.2136 -5.1752 0.0 0 +M V30 11 C -2.4333 -3.7983 0.0 0 +M V30 12 N 3.0505 -5.0802 0.0 0 +M V30 13 C 4.4393 -4.5105 0.0 0 +M V30 14 C -1.7329 -5.1159 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 6 8 +M V30 15 1 11 14 +M V30 16 2 12 13 +M V30 END BOND +M V30 END CTAB +M END +> +155 + +> +Z1509204240 + +> +185.182 + +> +0.870 + +> +1 + +> +46.920 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL551657 + +> +0.9 + +$$$$ +Compound 156 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 15 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.507 0.0 0 +M V30 3 N -1.3186 2.2723 0.0 0 +M V30 4 C 1.2716 2.2723 0.0 0 +M V30 5 C -1.3304 3.7794 0.0 0 +M V30 6 C -2.6256 1.5306 0.0 0 +M V30 7 C 1.2598 3.7794 0.0 0 +M V30 8 C 2.5549 1.5306 0.0 0 +M V30 9 N -0.047 4.5447 0.0 0 +M V30 10 C -2.6373 4.5447 0.0 0 +M V30 11 C -2.6373 0.047 0.0 0 +M V30 12 C 2.5432 4.5447 0.0 0 +M V30 13 C 3.8383 2.2959 0.0 0 +M V30 14 C 3.8265 3.803 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 5 10 +M V30 10 1 6 11 +M V30 11 1 7 12 +M V30 12 2 8 13 +M V30 13 2 12 14 +M V30 14 1 7 9 +M V30 15 1 13 14 +M V30 END BOND +M V30 END CTAB +M END +> +156 + +> +Z214617946 + +> +188.226 + +> +1.675 + +> +0 + +> +32.670 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL395092 + +> +0.88 + +$$$$ +Compound 157 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 14 15 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.312 -0.7328 0.0 0 +M V30 3 N -2.6241 0.0236 0.0 0 +M V30 4 C -1.3238 -2.2222 0.0 0 +M V30 5 C -2.6359 1.5366 0.0 0 +M V30 6 C -3.9361 -0.7092 0.0 0 +M V30 7 C -2.6359 -2.9551 0.0 0 +M V30 8 C -0.0354 -2.9551 0.0 0 +M V30 9 N -4.078 -2.4822 0.0 0 +M V30 10 C -2.6477 -4.4444 0.0 0 +M V30 11 C -0.0472 -4.4444 0.0 0 +M V30 12 N -4.9763 -3.6879 0.0 0 +M V30 13 C -1.3593 -5.1773 0.0 0 +M V30 14 C -4.0898 -4.8936 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 1 7 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 1 11 13 +M V30 15 2 12 14 +M V30 END BOND +M V30 END CTAB +M END +> +157 + +> +Z332751028 + +> +189.214 + +> +0.568 + +> +1 + +> +48.990 + +> +1 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL451401 + +> +0.85 + +$$$$ +Compound 158 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3159 -0.735 0.0 0 +M V30 3 C -1.3277 -2.2287 0.0 0 +M V30 4 C -2.6318 0.0237 0.0 0 +M V30 5 C -0.0355 -2.9756 0.0 0 +M V30 6 C -2.6437 -2.9756 0.0 0 +M V30 7 C -0.0474 -4.4694 0.0 0 +M V30 8 C -2.6555 -4.4694 0.0 0 +M V30 9 C -1.3633 -5.2163 0.0 0 +M V30 10 N -1.3752 -6.71 0.0 0 +M V30 11 C -0.0829 -7.445 0.0 0 +M V30 12 C -2.6911 -7.445 0.0 0 +M V30 13 C -0.0948 -8.9388 0.0 0 +M V30 14 C -2.7029 -8.9388 0.0 0 +M V30 15 O -1.4107 -9.6857 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 3 5 +M V30 5 1 3 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 2 7 9 +M V30 9 1 9 10 +M V30 10 1 10 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 1 8 9 +M V30 16 1 14 15 +M V30 END BOND +M V30 END CTAB +M END +> +158 + +> +Z56899083 + +> +205.253 + +> +1.348 + +> +0 + +> +29.540 + +> +2 + +> +DNA-dependent protein kinase + +> +CHEMBL1317546 + +> +0.85 + +$$$$ +Compound 159 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.4998 0.0 0 +M V30 3 N 1.2655 2.2615 0.0 0 +M V30 4 C -1.3124 2.2615 0.0 0 +M V30 5 C 1.2538 3.7614 0.0 0 +M V30 6 C -1.3241 3.7614 0.0 0 +M V30 7 C -2.613 1.5233 0.0 0 +M V30 8 C -0.0468 4.5231 0.0 0 +M V30 9 C -2.6248 4.5231 0.0 0 +M V30 10 C -3.9137 2.2849 0.0 0 +M V30 11 C -3.9254 3.7848 0.0 0 +M V30 12 O -5.2144 1.5467 0.0 0 +M V30 13 O -5.2261 4.5465 0.0 0 +M V30 14 C -6.5151 2.3084 0.0 0 +M V30 15 C -6.5268 3.8083 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 2 9 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 1 6 8 +M V30 16 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +159 + +> +Z94602405 + +> +207.226 + +> +0.875 + +> +1 + +> +47.560 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3629677 + +> +0.93 + +$$$$ +Compound 160 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.2193 -0.8996 0.0 0 +M V30 3 C 1.2193 -0.8523 0.0 0 +M V30 4 N -2.7109 -0.6037 0.0 0 +M V30 5 C -0.7457 -2.3084 0.0 0 +M V30 6 C 0.7694 -2.2847 0.0 0 +M V30 7 C 2.6043 -0.2841 0.0 0 +M V30 8 C -3.7171 -1.7283 0.0 0 +M V30 9 C -1.752 -3.433 0.0 0 +M V30 10 C 1.6099 -3.5159 0.0 0 +M V30 11 C 3.8947 -1.018 0.0 0 +M V30 12 N -3.2436 -3.137 0.0 0 +M V30 13 O -1.2785 -4.8417 0.0 0 +M V30 14 C 3.1015 -3.6106 0.0 0 +M V30 15 C 4.1078 -2.4978 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 5 6 +M V30 16 1 9 12 +M V30 17 1 14 15 +M V30 END BOND +M V30 END CTAB +M END +> +160 + +> +Z56758245 + +> +220.291 + +> +2.255 + +> +1 + +> +41.460 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL539906 + +> +0.99 + +$$$$ +Compound 161 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.223 0.8884 0.0 0 +M V30 3 C 1.2 0.8884 0.0 0 +M V30 4 N -2.7 0.5884 0.0 0 +M V30 5 C -0.773 2.3192 0.0 0 +M V30 6 C 0.7269 2.3192 0.0 0 +M V30 7 C 2.6538 0.5884 0.0 0 +M V30 8 C -3.7154 1.7077 0.0 0 +M V30 9 C -1.7884 3.4384 0.0 0 +M V30 10 C 1.7192 3.4384 0.0 0 +M V30 11 C 3.6461 1.7077 0.0 0 CFG=2 +M V30 12 N -3.2654 3.1384 0.0 0 +M V30 13 O -1.3384 4.8692 0.0 0 +M V30 14 C 3.173 3.1384 0.0 0 +M V30 15 C 5.1 1.4077 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 10 14 +M V30 14 1 11 15 CFG=3 +M V30 15 1 5 6 +M V30 16 1 9 12 +M V30 17 1 11 14 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 11) +M V30 END COLLECTION +M V30 END CTAB +M END +> +161 + +> +Z56876394 + +> +220.291 + +> +2.215 + +> +1 + +> +41.460 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL539906 + +> +0.97 + +$$$$ +Compound 162 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.7769 1.2831 0.0 0 +M V30 3 N 0.0588 2.6134 0.0 0 +M V30 4 N 2.2603 1.2478 0.0 0 +M V30 5 N -1.4009 2.9784 0.0 0 +M V30 6 C 0.8358 3.8966 0.0 0 +M V30 7 C 3.0372 2.531 0.0 0 +M V30 8 C -1.5186 4.4853 0.0 0 +M V30 9 N -0.1412 5.0503 0.0 0 +M V30 10 C 2.3191 3.8613 0.0 0 +M V30 11 C 4.5206 2.4957 0.0 0 +M V30 12 C -2.8018 5.2858 0.0 0 +M V30 13 C 3.0961 5.1445 0.0 0 +M V30 14 C 5.2976 3.7789 0.0 0 +M V30 15 C 4.5794 5.1092 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 10 13 +M V30 13 2 11 14 +M V30 14 2 13 15 +M V30 15 2 7 10 +M V30 16 1 8 9 +M V30 17 1 14 15 +M V30 END BOND +M V30 END CTAB +M END +> +162 + +> +Z90662863 + +> +200.197 + +> +-0.707 + +> +1 + +> +59.810 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1992673 + +> +0.9 + +$$$$ +Compound 163 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.4915 0.0118 0.0 0 +M V30 3 N 2.3675 1.2429 0.0 0 +M V30 4 N 2.3675 -1.1955 0.0 0 +M V30 5 C 3.788 0.7931 0.0 0 +M V30 6 C 3.788 -0.722 0.0 0 +M V30 7 C 1.894 -2.6161 0.0 0 +M V30 8 N 5.0783 1.5625 0.0 0 +M V30 9 C 5.0783 -1.4678 0.0 0 +M V30 10 C 6.3686 0.8286 0.0 0 +M V30 11 C 5.0664 3.0777 0.0 0 +M V30 12 O 5.0664 -2.9593 0.0 0 +M V30 13 N 6.3686 -0.6984 0.0 0 +M V30 14 O 7.6589 1.598 0.0 0 +M V30 15 C 7.6589 -1.4441 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 8 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 2 5 6 +M V30 16 1 10 13 +M V30 END BOND +M V30 END CTAB +M END +> +163 + +> +Z104495030 + +> +228.636 + +> +0.707 + +> +0 + +> +58.440 + +> +0 + +> +Serine-protein kinase ATM + +> +CHEMBL113 + +> +0.88 + +$$$$ +Compound 164 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.7227 -1.3151 0.0 0 +M V30 3 N -2.2392 -1.3506 0.0 0 +M V30 4 C 0.0592 -2.5828 0.0 0 +M V30 5 C -2.9619 -2.6657 0.0 0 +M V30 6 C -0.6634 -3.8979 0.0 0 +M V30 7 C 1.552 -2.5235 0.0 0 +M V30 8 N -2.1799 -3.9334 0.0 0 +M V30 9 C -4.431 -3.0093 0.0 0 +M V30 10 C 0.1184 -5.1656 0.0 0 +M V30 11 C 2.334 -3.7912 0.0 0 +M V30 12 N -3.1633 -5.0708 0.0 0 +M V30 13 C -4.5495 -4.5021 0.0 0 +M V30 14 C 1.6112 -5.1063 0.0 0 +M V30 15 C -5.8409 -5.2841 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 1 6 8 +M V30 16 1 11 14 +M V30 17 2 12 13 +M V30 END BOND +M V30 END CTAB +M END +> +164 + +> +Z56764726 + +> +199.209 + +> +1.139 + +> +1 + +> +46.920 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL551657 + +> +1.0 + +$$$$ +Compound 165 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.4998 0.0 0 +M V30 3 N 1.2655 2.2615 0.0 0 +M V30 4 C -1.3124 2.2615 0.0 0 +M V30 5 C 1.2538 3.7614 0.0 0 +M V30 6 C -1.3241 3.7614 0.0 0 +M V30 7 C -2.613 1.5233 0.0 0 +M V30 8 N -0.0468 4.5231 0.0 0 +M V30 9 C -2.6248 4.5231 0.0 0 +M V30 10 C -3.9137 2.2849 0.0 0 +M V30 11 C -3.9254 3.7848 0.0 0 +M V30 12 O -5.2144 1.5467 0.0 0 +M V30 13 O -5.2261 4.5465 0.0 0 +M V30 14 C -6.5151 2.3084 0.0 0 +M V30 15 C -6.5268 3.8083 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 2 9 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 1 6 8 +M V30 16 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +165 + +> +Z1741982032 + +> +206.198 + +> +0.452 + +> +1 + +> +59.920 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1949846 + +> +0.86 + +$$$$ +Compound 166 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.4123 0.4707 0.0 0 +M V30 3 C -0.8944 1.224 0.0 0 +M V30 4 N 2.6951 -0.2589 0.0 0 +M V30 5 C 1.4005 1.9772 0.0 0 +M V30 6 C -0.0235 2.448 0.0 0 +M V30 7 C 3.978 0.4943 0.0 0 +M V30 8 C 2.6833 2.7304 0.0 0 +M V30 9 C -0.4943 3.8838 0.0 0 +M V30 10 N 3.9662 2.0007 0.0 0 +M V30 11 O 2.6716 4.2369 0.0 0 +M V30 12 S 0.3766 5.1078 0.0 0 +M V30 13 C -1.9301 4.3546 0.0 0 +M V30 14 C -0.5178 6.3318 0.0 0 +M V30 15 C -1.9419 5.861 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 9 12 +M V30 12 2 9 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 1 5 6 +M V30 16 1 8 10 +M V30 17 2 14 15 +M V30 END BOND +M V30 END CTAB +M END +> +166 + +> +Z56884045 + +> +234.297 + +> +1.875 + +> +1 + +> +41.460 + +> +1 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL559696 + +> +1.0 + +$$$$ +Compound 167 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.513 0.0 0 +M V30 3 N -1.3238 2.2813 0.0 0 +M V30 4 C 1.2765 2.2813 0.0 0 +M V30 5 C -1.3357 3.7943 0.0 0 +M V30 6 C 1.2647 3.7943 0.0 0 +M V30 7 C 2.565 1.5366 0.0 0 +M V30 8 N -0.0472 4.5508 0.0 0 +M V30 9 C 2.5531 4.5508 0.0 0 +M V30 10 C 3.8534 2.3049 0.0 0 +M V30 11 C 3.8416 3.8061 0.0 0 +M V30 12 C 5.1418 1.5602 0.0 0 +M V30 13 C 5.13 4.5626 0.0 0 +M V30 14 C 6.4302 2.3286 0.0 0 +M V30 15 C 6.4184 3.8179 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 2 11 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 1 6 8 +M V30 16 1 10 11 +M V30 17 2 14 15 +M V30 END BOND +M V30 END CTAB +M END +> +167 + +> +Z56762044 + +> +196.205 + +> +1.479 + +> +1 + +> +41.460 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL266540 + +> +0.94 + +$$$$ +Compound 168 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.2307 -0.8708 0.0 0 +M V30 3 C 1.2075 -0.8708 0.0 0 +M V30 4 N -2.7169 -0.5457 0.0 0 +M V30 5 C -0.7779 -2.2873 0.0 0 +M V30 6 C 0.7314 -2.2873 0.0 0 +M V30 7 C 2.6704 -0.5457 0.0 0 +M V30 8 C -3.7386 -1.6487 0.0 0 +M V30 9 C -1.7996 -3.3903 0.0 0 +M V30 10 C 1.73 -3.3903 0.0 0 +M V30 11 C 3.669 -1.6487 0.0 0 +M V30 12 N -3.2858 -3.0652 0.0 0 +M V30 13 C -5.2248 -1.3236 0.0 0 +M V30 14 O -1.3468 -4.8068 0.0 0 +M V30 15 C 3.1929 -3.0652 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 10 15 +M V30 15 1 5 6 +M V30 16 1 9 12 +M V30 17 1 11 15 +M V30 END BOND +M V30 END CTAB +M END +> +168 + +> +Z55164926 + +> +220.291 + +> +2.195 + +> +1 + +> +41.460 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL539906 + +> +0.92 + +$$$$ +Compound 169 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4919 0.0 0 +M V30 3 N 1.2788 -2.2261 0.0 0 +M V30 4 C -1.3262 -2.2261 0.0 0 +M V30 5 C 1.267 -3.7181 0.0 0 +M V30 6 C -1.338 -3.7181 0.0 0 +M V30 7 C -2.6405 -1.4683 0.0 0 +M V30 8 N -0.0473 -4.4641 0.0 0 +M V30 9 C 2.5576 -4.4641 0.0 0 +M V30 10 C -2.6524 -4.4641 0.0 0 +M V30 11 C -3.9549 -2.2024 0.0 0 +M V30 12 C 3.8483 -3.6944 0.0 0 +M V30 13 C -3.9667 -3.6944 0.0 0 +M V30 14 C -2.6642 -5.956 0.0 0 +M V30 15 N 5.139 -2.9366 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 3 12 15 +M V30 15 1 6 8 +M V30 16 1 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +169 + +> +Z361877642 + +> +199.209 + +> +0.226 + +> +1 + +> +65.250 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1949864 + +> +0.87 + +$$$$ +Compound 170 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5049 0.0 0 +M V30 3 N -1.3168 2.2692 0.0 0 +M V30 4 C 1.2698 2.2692 0.0 0 +M V30 5 C -1.3286 3.7741 0.0 0 +M V30 6 C 1.258 3.7741 0.0 0 +M V30 7 C 2.5513 1.5284 0.0 0 +M V30 8 N -0.047 4.5384 0.0 0 +M V30 9 C -2.6336 4.5384 0.0 0 +M V30 10 C 2.5396 4.5384 0.0 0 +M V30 11 C 3.8329 2.2927 0.0 0 +M V30 12 N -3.9387 3.7976 0.0 0 +M V30 13 C 3.8212 3.7976 0.0 0 +M V30 14 C -5.2438 4.5619 0.0 0 +M V30 15 C -6.5489 3.8212 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 6 8 +M V30 16 1 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +170 + +> +Z44508631 + +> +203.240 + +> +0.202 + +> +2 + +> +53.490 + +> +3 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL395092 + +> +0.88 + +$$$$ +Compound 171 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 O 9.1008 3.1284 0.0 0 +M V30 3 C 9.5511 1.7064 0.0 0 +M V30 4 N 8.6505 0.4977 0.0 0 +M V30 5 C 10.9731 1.2561 0.0 0 +M V30 6 C 9.5274 -0.711 0.0 0 +M V30 7 C 7.1337 0.5095 0.0 0 +M V30 8 C 10.9612 -0.237 0.0 0 +M V30 9 C 12.2647 2.0145 0.0 0 +M V30 10 O 9.0534 -2.133 0.0 0 +M V30 11 C 6.3634 -0.7821 0.0 0 +M V30 12 C 12.2529 -0.9835 0.0 0 +M V30 13 C 13.5564 1.2798 0.0 0 +M V30 14 C 4.8466 -0.7702 0.0 0 +M V30 15 C 13.5445 -0.2251 0.0 0 +M V30 16 N 4.0882 -2.0619 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 1 3 4 +M V30 3 1 3 5 +M V30 4 1 4 6 +M V30 5 1 4 7 +M V30 6 2 5 8 +M V30 7 1 5 9 +M V30 8 2 6 10 +M V30 9 1 7 11 +M V30 10 1 8 12 +M V30 11 2 9 13 +M V30 12 1 11 14 +M V30 13 2 12 15 +M V30 14 1 14 16 +M V30 15 1 6 8 +M V30 16 1 13 15 +M V30 END BOND +M V30 END CTAB +M END +> +171 + +> +Z2065464347 + +> +204.225 + +> +1.073 + +> +1 + +> +63.400 + +> +3 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1329044 + +> +0.89 + +$$$$ +Compound 172 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.45 -1.4212 0.0 0 +M V30 3 N -0.45 -2.6292 0.0 0 +M V30 4 C 1.8712 -1.8712 0.0 0 +M V30 5 C 0.4263 -3.8373 0.0 0 +M V30 6 C -1.966 -2.6174 0.0 0 +M V30 7 C 1.8594 -3.3635 0.0 0 +M V30 8 C 3.1622 -1.1132 0.0 0 +M V30 9 O -0.0473 -5.2585 0.0 0 +M V30 10 C -2.724 -3.9083 0.0 0 +M V30 11 C 3.1503 -4.1097 0.0 0 +M V30 12 C 4.4531 -1.8476 0.0 0 +M V30 13 C -4.24 -3.8965 0.0 0 +M V30 14 C 4.4413 -3.3517 0.0 0 +M V30 15 N -5.7559 -3.8847 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 10 13 +M V30 13 2 11 14 +M V30 14 3 13 15 +M V30 15 1 5 7 +M V30 16 1 12 14 +M V30 END BOND +M V30 END CTAB +M END +> +172 + +> +Z54335998 + +> +200.193 + +> +0.960 + +> +0 + +> +61.170 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1329044 + +> +0.86 + +$$$$ +Compound 173 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.7486 -1.2952 0.0 0 +M V30 3 N 2.2458 -1.2833 0.0 0 +M V30 4 C -0.0118 -2.5904 0.0 0 +M V30 5 C 2.9826 -2.5786 0.0 0 +M V30 6 C 0.7367 -3.8857 0.0 0 +M V30 7 C -1.5329 -2.5786 0.0 0 +M V30 8 C 2.2221 -3.8738 0.0 0 +M V30 9 C 4.4798 -2.5667 0.0 0 +M V30 10 C -0.0237 -5.1809 0.0 0 +M V30 11 C -2.2934 -3.8738 0.0 0 +M V30 12 C 2.9588 -5.1691 0.0 0 +M V30 13 C 5.2285 -3.8619 0.0 0 +M V30 14 C -1.5447 -5.1691 0.0 0 +M V30 15 C 4.4561 -5.1572 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 2 10 14 +M V30 14 2 12 15 +M V30 15 1 6 8 +M V30 16 1 11 14 +M V30 17 1 13 15 +M V30 END BOND +M V30 END CTAB +M END +> +173 + +> +Z275368072 + +> +195.217 + +> +2.196 + +> +1 + +> +29.100 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL45245 + +> +1.0 + +$$$$ +Compound 174 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C 1.4248 -0.4511 0.0 0 +M V30 3 C 1.7216 -1.9116 0.0 0 +M V30 4 C 2.529 0.5699 0.0 0 +M V30 5 C 3.1465 -2.3628 0.0 0 +M V30 6 C 3.9539 0.1187 0.0 0 +M V30 7 C 4.2507 -1.3417 0.0 0 +M V30 8 C 5.6755 -1.7929 0.0 0 +M V30 9 N 6.1267 -3.2177 0.0 0 +M V30 10 C 6.8866 -0.8905 0.0 0 +M V30 11 N 7.6228 -3.2058 0.0 0 +M V30 12 C 8.0978 -1.7691 0.0 0 +M V30 13 C 6.8748 0.6293 0.0 0 +M V30 14 N 9.5226 -1.2942 0.0 0 +M V30 15 C 8.169 1.3892 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 2 8 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 1 10 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 1 6 7 +M V30 16 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +174 + +> +Z445224178 + +> +266.137 + +> +3.306 + +> +2 + +> +54.700 + +> +2 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 175 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 O 9.1008 3.1284 0.0 0 +M V30 3 C 9.5511 1.7064 0.0 0 +M V30 4 N 8.6505 0.4977 0.0 0 +M V30 5 C 10.9731 1.2561 0.0 0 +M V30 6 C 9.5274 -0.711 0.0 0 +M V30 7 C 7.1337 0.5095 0.0 0 +M V30 8 C 10.9612 -0.237 0.0 0 +M V30 9 C 12.2647 2.0145 0.0 0 +M V30 10 O 9.0534 -2.133 0.0 0 +M V30 11 C 6.3634 -0.7821 0.0 0 +M V30 12 C 12.2529 -0.9835 0.0 0 +M V30 13 C 13.5564 1.2798 0.0 0 +M V30 14 N 4.8466 -0.7702 0.0 0 +M V30 15 C 13.5445 -0.2251 0.0 0 +M V30 16 C 4.0882 -2.0619 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 1 3 4 +M V30 3 1 3 5 +M V30 4 1 4 6 +M V30 5 1 4 7 +M V30 6 2 5 8 +M V30 7 1 5 9 +M V30 8 2 6 10 +M V30 9 1 7 11 +M V30 10 1 8 12 +M V30 11 2 9 13 +M V30 12 1 11 14 +M V30 13 2 12 15 +M V30 14 1 14 16 +M V30 15 1 6 8 +M V30 16 1 13 15 +M V30 END BOND +M V30 END CTAB +M END +> +175 + +> +Z1507503093 + +> +204.225 + +> +1.038 + +> +1 + +> +49.410 + +> +3 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL611975 + +> +0.85 + +$$$$ +Compound 176 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.308 -0.7424 0.0 0 +M V30 3 N -2.6161 0.0235 0.0 0 +M V30 4 N -1.3198 -2.2272 0.0 0 +M V30 5 C -3.9241 -0.7188 0.0 0 +M V30 6 C -2.6278 1.5319 0.0 0 +M V30 7 C -2.6278 -2.9696 0.0 0 +M V30 8 N -5.3618 -0.2474 0.0 0 +M V30 9 C -3.9359 -2.2036 0.0 0 +M V30 10 O -2.6396 -4.4544 0.0 0 +M V30 11 C -6.2574 -1.4494 0.0 0 +M V30 12 N -5.3736 -2.6514 0.0 0 +M V30 13 C -5.845 -4.0655 0.0 0 +M V30 14 C -7.318 -4.3601 0.0 0 +M V30 15 C -7.7894 -5.7743 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 2 7 10 +M V30 10 2 8 11 +M V30 11 1 9 12 +M V30 12 1 12 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 1 7 9 +M V30 16 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +176 + +> +Z839027068 + +> +208.217 + +> +0.386 + +> +1 + +> +67.230 + +> +2 + +> +Serine-protein kinase ATM + +> +CHEMBL113 + +> +0.9 + +$$$$ +Compound 177 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -1.3021 0.7625 0.0 0 +M V30 3 C -2.6042 0.0234 0.0 0 +M V30 4 C -1.3138 2.264 0.0 0 +M V30 5 C -3.9064 0.7859 0.0 0 +M V30 6 C -2.616 3.0265 0.0 0 +M V30 7 C -3.9181 2.2875 0.0 0 +M V30 8 C -5.2085 0.0469 0.0 0 +M V30 9 N -5.2202 3.05 0.0 0 +M V30 10 C -5.2202 -1.4311 0.0 0 +M V30 11 C -6.5107 0.8094 0.0 0 +M V30 12 C -6.5224 2.311 0.0 0 +M V30 13 O -6.5224 -2.1702 0.0 0 +M V30 14 O -3.9416 -2.1702 0.0 0 +M V30 15 O -7.8245 3.0735 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 1 8 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 1 12 15 +M V30 15 1 6 7 +M V30 16 2 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +177 + +> +Z916963792 + +> +207.158 + +> +2.701 + +> +2 + +> +70.420 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL298053 + +> +0.86 + +$$$$ +Compound 178 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.292 -0.7468 0.0 0 +M V30 3 C -1.3158 -0.7468 0.0 0 +M V30 4 C 1.2802 -2.2404 0.0 0 +M V30 5 C -1.3276 -2.2404 0.0 0 +M V30 6 N -0.0355 -2.9753 0.0 0 +M V30 7 C -0.0474 -4.469 0.0 0 +M V30 8 N 1.2446 -5.2039 0.0 0 +M V30 9 C -1.3632 -5.2039 0.0 0 +M V30 10 C 1.2328 -6.6975 0.0 0 +M V30 11 N -2.8094 -4.7297 0.0 0 +M V30 12 C -1.375 -6.6975 0.0 0 +M V30 13 N -0.0829 -7.4325 0.0 0 +M V30 14 C -3.7103 -5.9389 0.0 0 +M V30 15 N -2.8212 -7.148 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 1 6 7 +M V30 7 2 7 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 2 9 12 +M V30 12 2 10 13 +M V30 13 2 11 14 +M V30 14 1 12 15 +M V30 15 1 5 6 +M V30 16 1 12 13 +M V30 17 1 14 15 +M V30 END BOND +M V30 END CTAB +M END +> +178 + +> +Z57921676 + +> +205.217 + +> +0.029 + +> +1 + +> +66.930 + +> +1 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL271138 + +> +1.0 + +$$$$ +Compound 179 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 N 0.0 0.0 0.0 0 +M V30 2 C 0.8683 1.2272 0.0 0 +M V30 3 C 0.8683 -1.204 0.0 0 +M V30 4 N 2.2807 0.7757 0.0 0 +M V30 5 C 0.3936 2.6628 0.0 0 +M V30 6 C 2.2807 -0.7293 0.0 0 +M V30 7 C 3.5659 1.5398 0.0 0 +M V30 8 C -1.0882 2.987 0.0 0 +M V30 9 C 1.3893 3.7858 0.0 0 +M V30 10 C 3.5659 -1.4703 0.0 0 +M V30 11 C 4.851 0.7988 0.0 0 +M V30 12 C -1.5629 4.4226 0.0 0 +M V30 13 C 0.9146 5.2215 0.0 0 +M V30 14 C 4.851 -0.7062 0.0 0 +M V30 15 C -0.5673 5.5456 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 2 10 14 +M V30 14 2 12 15 +M V30 15 1 4 6 +M V30 16 1 11 14 +M V30 17 1 13 15 +M V30 END BOND +M V30 END CTAB +M END +> +179 + +> +Z57214517 + +> +194.232 + +> +3.504 + +> +0 + +> +17.300 + +> +1 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1380050 + +> +1.0 + +$$$$ +Compound 180 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.509 0.0 0 +M V30 3 F 1.4737 1.5208 0.0 0 +M V30 4 F -1.5208 1.5208 0.0 0 +M V30 5 C -0.0235 3.0181 0.0 0 +M V30 6 C 1.2614 3.7726 0.0 0 +M V30 7 C -1.3322 3.7726 0.0 0 +M V30 8 N 2.5465 3.0299 0.0 0 +M V30 9 C 1.2497 5.2817 0.0 0 +M V30 10 C -1.344 5.2817 0.0 0 +M V30 11 C 3.8316 3.7844 0.0 0 +M V30 12 C 2.5347 6.0363 0.0 0 +M V30 13 C -0.0589 6.0363 0.0 0 +M V30 14 N 3.8198 5.2935 0.0 0 +M V30 15 O 2.5229 7.5453 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 2 5 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 2 8 11 +M V30 11 1 9 12 +M V30 12 2 9 13 +M V30 13 1 11 14 +M V30 14 2 12 15 +M V30 15 1 10 13 +M V30 16 1 12 14 +M V30 END BOND +M V30 END CTAB +M END +> +180 + +> +Z1262433900 + +> +214.144 + +> +1.290 + +> +1 + +> +41.460 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1949850 + +> +0.88 + +$$$$ +Compound 181 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2959 -0.7371 0.0 0 +M V30 3 N 2.5918 0.0237 0.0 0 +M V30 4 C 1.284 -2.2351 0.0 0 +M V30 5 C 3.8877 -0.7133 0.0 0 +M V30 6 C 2.5799 -2.9841 0.0 0 +M V30 7 C 3.8758 -2.2113 0.0 0 +M V30 8 C 5.1836 0.0475 0.0 0 +M V30 9 C 2.568 -4.4822 0.0 0 +M V30 10 C 5.1717 -2.9603 0.0 0 +M V30 11 C 6.4795 -0.6895 0.0 0 +M V30 12 O 3.8639 -5.2312 0.0 0 +M V30 13 O 1.2483 -5.2312 0.0 0 +M V30 14 C 6.4676 -2.1994 0.0 0 +M V30 15 C 1.2364 -6.7292 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 1 6 7 +M V30 16 1 11 14 +M V30 END BOND +M V30 END CTAB +M END +> +181 + +> +Z211347112 + +> +203.194 + +> +1.138 + +> +1 + +> +55.400 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL294995 + +> +0.87 + +$$$$ +Compound 182 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5031 0.0 0 +M V30 3 N 1.2683 2.2665 0.0 0 +M V30 4 C -1.3152 2.2665 0.0 0 +M V30 5 C 1.2565 3.7697 0.0 0 +M V30 6 C -1.327 3.7697 0.0 0 +M V30 7 C -2.6188 1.5266 0.0 0 +M V30 8 N -0.0469 4.5213 0.0 0 +M V30 9 C 2.5366 4.5213 0.0 0 +M V30 10 C -2.6305 4.5213 0.0 0 +M V30 11 C -3.9223 2.29 0.0 0 +M V30 12 C 2.5248 6.0244 0.0 0 +M V30 13 C -3.9341 3.7931 0.0 0 +M V30 14 C -5.2259 1.5501 0.0 0 +M V30 15 N 2.5131 7.5276 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 11 14 +M V30 14 3 12 15 +M V30 15 1 6 8 +M V30 16 1 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +182 + +> +Z104342646 + +> +199.209 + +> +0.226 + +> +1 + +> +65.250 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1949864 + +> +0.86 + +$$$$ +Compound 183 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4949 0.0 0 +M V30 3 N 1.2813 -2.2305 0.0 0 +M V30 4 C -1.3288 -2.2305 0.0 0 +M V30 5 C 1.2695 -3.7254 0.0 0 +M V30 6 C 2.5746 -1.4712 0.0 0 +M V30 7 C -1.3407 -3.7254 0.0 0 +M V30 8 C -2.6458 -1.4712 0.0 0 +M V30 9 N -0.0474 -4.4611 0.0 0 +M V30 10 C 2.5627 -4.4611 0.0 0 +M V30 11 C 3.8678 -2.2068 0.0 0 +M V30 12 C -2.6576 -4.4611 0.0 0 +M V30 13 C -3.9627 -2.2068 0.0 0 +M V30 14 C 3.856 -3.7017 0.0 0 +M V30 15 C -3.9746 -3.7017 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 5 10 +M V30 10 1 6 11 +M V30 11 1 7 12 +M V30 12 2 8 13 +M V30 13 1 10 14 +M V30 14 2 12 15 +M V30 15 1 7 9 +M V30 16 1 11 14 +M V30 17 1 13 15 +M V30 END BOND +M V30 END CTAB +M END +> +183 + +> +Z1509566963 + +> +200.236 + +> +1.720 + +> +0 + +> +32.670 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL2377256 + +> +0.87 + +$$$$ +Compound 184 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3075 -0.7303 0.0 0 +M V30 3 N -1.3193 -2.2146 0.0 0 +M V30 4 N -2.6151 0.0353 0.0 0 +M V30 5 C -2.6269 -2.9567 0.0 0 +M V30 6 C -0.0353 -2.9567 0.0 0 +M V30 7 C -3.9227 -0.695 0.0 0 +M V30 8 C -2.6269 1.5431 0.0 0 +M V30 9 O -2.6386 -4.441 0.0 0 +M V30 10 C -3.9344 -2.191 0.0 0 +M V30 11 N -5.3598 -0.2238 0.0 0 +M V30 12 N -5.3716 -2.6386 0.0 0 +M V30 13 C -6.2551 -1.4253 0.0 0 +M V30 14 C -5.8428 -4.0522 0.0 0 +M V30 15 N -7.7629 -1.4135 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 5 10 +M V30 10 1 7 11 +M V30 11 1 10 12 +M V30 12 2 11 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 2 7 10 +M V30 16 1 12 13 +M V30 END BOND +M V30 END CTAB +M END +> +184 + +> +Z1509384713 + +> +209.205 + +> +-0.309 + +> +1 + +> +84.460 + +> +0 + +> +Serine-protein kinase ATM + +> +CHEMBL113 + +> +0.91 + +$$$$ +Compound 185 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.3403 1.4551 0.0 0 +M V30 3 N -0.6336 2.6051 0.0 0 +M V30 4 N 1.7132 2.0301 0.0 0 +M V30 5 C -2.1357 2.6637 0.0 0 +M V30 6 C 0.1408 3.8841 0.0 0 +M V30 7 N 1.5959 3.5321 0.0 0 +M V30 8 C -2.8515 3.9898 0.0 0 +M V30 9 C -2.9336 1.4081 0.0 0 +M V30 10 C -0.575 5.2102 0.0 0 +M V30 11 C -2.077 5.2688 0.0 0 +M V30 12 C -4.3535 4.0484 0.0 0 +M V30 13 C -4.4357 1.4668 0.0 0 +M V30 14 C -2.7928 6.5949 0.0 0 +M V30 15 C -5.1515 2.7928 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 8 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 11 14 +M V30 14 2 12 15 +M V30 15 2 6 7 +M V30 16 2 10 11 +M V30 17 1 13 15 +M V30 END BOND +M V30 END CTAB +M END +> +185 + +> +Z1618120491 + +> +199.209 + +> +1.996 + +> +1 + +> +44.700 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL487274 + +> +1.0 + +$$$$ +Compound 186 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5023 0.0 0 +M V30 3 N 1.2676 2.2652 0.0 0 +M V30 4 C -1.3145 2.2652 0.0 0 +M V30 5 C 1.2558 3.7676 0.0 0 +M V30 6 C -1.3262 3.7676 0.0 0 +M V30 7 C -2.6173 1.5258 0.0 0 +M V30 8 N -0.0469 4.5305 0.0 0 +M V30 9 C 2.5352 4.5305 0.0 0 +M V30 10 C -2.6291 4.5305 0.0 0 +M V30 11 C -3.9201 2.2887 0.0 0 +M V30 12 C -3.9319 3.791 0.0 0 +M V30 13 C -5.223 1.5492 0.0 0 +M V30 14 C -5.2347 0.0704 0.0 0 +M V30 15 C -6.5258 2.3122 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 10 12 +M V30 12 1 11 13 +M V30 13 1 13 14 +M V30 14 1 13 15 +M V30 15 1 6 8 +M V30 16 1 11 12 +M V30 END BOND +M V30 END CTAB +M END +> +186 + +> +Z951211406 + +> +202.252 + +> +2.231 + +> +1 + +> +41.460 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL395092 + +> +0.87 + +$$$$ +Compound 187 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.2183 -0.899 0.0 0 +M V30 3 C 1.2183 -0.8516 0.0 0 +M V30 4 N -2.7088 -0.6032 0.0 0 +M V30 5 C -0.7452 -2.3066 0.0 0 +M V30 6 C 0.7688 -2.2829 0.0 0 +M V30 7 C 2.6023 -0.2838 0.0 0 +M V30 8 C -3.7143 -1.727 0.0 0 +M V30 9 C -1.7506 -3.4304 0.0 0 +M V30 10 C 1.6087 -3.5132 0.0 0 +M V30 11 C 3.8917 -1.0172 0.0 0 +M V30 12 N -3.2411 -3.1346 0.0 0 +M V30 13 C -5.2047 -1.4313 0.0 0 +M V30 14 O -1.2775 -4.838 0.0 0 +M V30 15 C 3.0991 -3.6078 0.0 0 +M V30 16 C 4.1046 -2.4959 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 10 15 +M V30 15 1 11 16 +M V30 16 1 5 6 +M V30 17 1 9 12 +M V30 18 1 15 16 +M V30 END BOND +M V30 END CTAB +M END +> +187 + +> +Z55996437 + +> +234.317 + +> +2.754 + +> +1 + +> +41.460 + +> +0 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL539906 + +> +0.91 + +$$$$ +Compound 188 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.4135 0.4711 0.0 0 +M V30 3 C -0.8952 1.225 0.0 0 +M V30 4 N 2.6975 -0.2591 0.0 0 +M V30 5 C 1.4017 1.979 0.0 0 +M V30 6 C -0.0235 2.4501 0.0 0 +M V30 7 C 3.9815 0.4947 0.0 0 +M V30 8 C 2.6857 2.7329 0.0 0 +M V30 9 C -0.4947 3.8873 0.0 0 +M V30 10 N 3.9697 2.0025 0.0 0 +M V30 11 C 5.2655 -0.2355 0.0 0 +M V30 12 O 2.674 4.2407 0.0 0 +M V30 13 S 0.3769 5.1124 0.0 0 +M V30 14 C -1.9318 4.3585 0.0 0 +M V30 15 C -0.5183 6.3375 0.0 0 +M V30 16 C -1.9436 5.8663 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 9 13 +M V30 13 2 9 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 1 5 6 +M V30 17 1 8 10 +M V30 18 2 15 16 +M V30 END BOND +M V30 END CTAB +M END +> +188 + +> +Z55164942 + +> +248.324 + +> +2.374 + +> +1 + +> +41.460 + +> +1 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL559696 + +> +0.92 + +$$$$ +Compound 189 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4872 0.0 0 +M V30 3 N -1.322 -2.2309 0.0 0 +M V30 4 C 1.2748 -2.2309 0.0 0 +M V30 5 C -2.6322 -1.4636 0.0 0 +M V30 6 C 2.5614 -1.4636 0.0 0 +M V30 7 C 1.263 -3.7181 0.0 0 +M V30 8 C -3.9424 -2.2073 0.0 0 +M V30 9 C -2.644 0.0472 0.0 0 +M V30 10 N 2.5496 0.0472 0.0 0 +M V30 11 C 3.848 -2.2073 0.0 0 +M V30 12 C 2.5496 -4.45 0.0 0 +M V30 13 C -5.2526 -1.44 0.0 0 +M V30 14 C -3.9542 0.8026 0.0 0 +M V30 15 C 3.8362 -3.6945 0.0 0 +M V30 16 C -5.2644 0.0708 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 6 11 +M V30 11 2 7 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 2 11 15 +M V30 15 2 13 16 +M V30 16 1 12 15 +M V30 17 1 14 16 +M V30 END BOND +M V30 END CTAB +M END +> +189 + +> +Z56040677 + +> +212.247 + +> +2.343 + +> +2 + +> +55.120 + +> +2 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1727312 + +> +0.85 + +$$$$ +Compound 190 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.8953 1.2252 0.0 0 +M V30 3 C -0.8953 -1.2016 0.0 0 +M V30 4 C -0.4476 2.6624 0.0 0 +M V30 5 C -2.3326 0.7775 0.0 0 +M V30 6 C -2.3326 -0.7304 0.0 0 +M V30 7 C -0.4476 -2.6153 0.0 0 +M V30 8 C 0.966 3.1337 0.0 0 +M V30 9 C -1.343 3.8877 0.0 0 +M V30 10 C 2.2501 2.3915 0.0 0 +M V30 11 C 0.9542 4.6416 0.0 0 +M V30 12 S -0.4712 5.1129 0.0 0 +M V30 13 O 2.2383 0.9071 0.0 0 +M V30 14 N 3.5342 3.1572 0.0 0 +M V30 15 N 2.2383 5.4074 0.0 0 +M V30 16 C 3.5224 4.6652 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 1 4 8 +M V30 8 2 4 9 +M V30 9 1 8 10 +M V30 10 2 8 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 14 16 +M V30 16 1 5 6 +M V30 17 1 11 12 +M V30 18 2 15 16 +M V30 END BOND +M V30 END CTAB +M END +> +190 + +> +Z94597996 + +> +248.324 + +> +2.374 + +> +1 + +> +41.460 + +> +1 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL559696 + +> +0.98 + +$$$$ +Compound 191 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3042 0.7519 0.0 0 +M V30 3 N -1.3159 2.2559 0.0 0 +M V30 4 N -2.6084 0.0234 0.0 0 +M V30 5 C -2.6201 3.0079 0.0 0 +M V30 6 C -0.0352 3.0079 0.0 0 +M V30 7 C -3.9126 0.7754 0.0 0 +M V30 8 C -2.6201 -1.4569 0.0 0 +M V30 9 O -2.6319 4.5118 0.0 0 +M V30 10 C -3.9244 2.2794 0.0 0 +M V30 11 N -5.3461 0.3289 0.0 0 +M V30 12 N -5.3578 2.7494 0.0 0 +M V30 13 C -6.2391 1.5509 0.0 0 +M V30 14 C -5.8278 4.1829 0.0 0 +M V30 15 C -4.8408 5.2991 0.0 0 +M V30 16 O -5.3108 6.7325 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 5 10 +M V30 10 1 7 11 +M V30 11 1 10 12 +M V30 12 2 11 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 2 7 10 +M V30 17 1 12 13 +M V30 END BOND +M V30 END CTAB +M END +> +191 + +> +Z104493598 + +> +224.217 + +> +-0.870 + +> +1 + +> +78.670 + +> +2 + +> +Serine-protein kinase ATM + +> +CHEMBL113 + +> +0.91 + +$$$$ +Compound 192 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5074 0.0 0 +M V30 3 N -1.319 2.2729 0.0 0 +M V30 4 C 1.2719 2.2729 0.0 0 +M V30 5 C -1.3308 3.7804 0.0 0 +M V30 6 C 1.2601 3.7804 0.0 0 +M V30 7 C 2.5556 1.531 0.0 0 +M V30 8 N -0.0471 4.5341 0.0 0 +M V30 9 C -2.638 4.5341 0.0 0 +M V30 10 C 2.5438 4.5341 0.0 0 +M V30 11 C 3.8393 2.2965 0.0 0 +M V30 12 C -3.9452 3.8039 0.0 0 +M V30 13 C 3.8275 3.8039 0.0 0 +M V30 14 C -5.2525 4.5577 0.0 0 +M V30 15 O -5.2643 6.0651 0.0 0 +M V30 16 O -6.5597 3.8275 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 6 8 +M V30 17 1 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +192 + +> +Z56869288 + +> +218.209 + +> +0.066 + +> +2 + +> +78.760 + +> +3 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL2377256 + +> +0.86 + +$$$$ +Compound 193 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 O 12.3761 2.7502 0.0 0 +M V30 3 C 12.0569 1.2891 0.0 0 +M V30 4 N 10.6695 0.6875 0.0 0 +M V30 5 C 13.0514 0.1841 0.0 0 +M V30 6 C 10.8168 -0.798 0.0 0 +M V30 7 C 9.3557 1.4487 0.0 0 +M V30 8 C 12.2901 -1.105 0.0 0 +M V30 9 C 14.537 0.1964 0.0 0 +M V30 10 O 9.6872 -1.7925 0.0 0 +M V30 11 C 8.042 0.7121 0.0 0 +M V30 12 C 13.0268 -2.3941 0.0 0 +M V30 13 C 15.2737 -1.0927 0.0 0 +M V30 14 C 6.7282 1.4733 0.0 0 +M V30 15 C 14.5124 -2.3819 0.0 0 +M V30 16 C 5.4145 0.7366 0.0 0 +M V30 17 N 4.1008 1.4979 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 1 3 4 +M V30 3 1 3 5 +M V30 4 1 4 6 +M V30 5 1 4 7 +M V30 6 2 5 8 +M V30 7 1 5 9 +M V30 8 2 6 10 +M V30 9 1 7 11 +M V30 10 1 8 12 +M V30 11 2 9 13 +M V30 12 1 11 14 +M V30 13 2 12 15 +M V30 14 1 14 16 +M V30 15 1 16 17 +M V30 16 1 6 8 +M V30 17 1 13 15 +M V30 END BOND +M V30 END CTAB +M END +> +193 + +> +Z1266933853 + +> +218.252 + +> +1.214 + +> +1 + +> +63.400 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1329044 + +> +0.88 + +$$$$ +Compound 194 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 Cl 3.5817 0.0942 0.0 0 +M V30 3 Cl -0.0942 -7.0691 0.0 0 +M V30 4 N 8.2002 -8.3887 0.0 0 +M V30 5 C 6.8924 -7.6346 0.0 0 +M V30 6 C 8.1884 -9.8732 0.0 0 +M V30 7 N 6.8806 -6.1266 0.0 0 +M V30 8 C 5.5846 -8.3651 0.0 0 +M V30 9 N 6.8806 -10.6037 0.0 0 +M V30 10 C 8.212 -5.4668 0.0 0 +M V30 11 C 5.5257 -5.4668 0.0 0 +M V30 12 N 4.1472 -7.8938 0.0 0 +M V30 13 C 5.5728 -9.8497 0.0 0 +M V30 14 C 8.5301 -3.994 0.0 0 +M V30 15 C 5.184 -3.994 0.0 0 +M V30 16 C 3.2518 -9.0956 0.0 0 +M V30 17 N 4.1354 -10.2974 0.0 0 +M V30 18 N 7.5875 -2.8158 0.0 0 +M V30 19 C 6.103 -2.8158 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 4 5 +M V30 2 1 4 6 +M V30 3 1 5 7 +M V30 4 1 5 8 +M V30 5 2 6 9 +M V30 6 1 7 10 +M V30 7 1 7 11 +M V30 8 1 8 12 +M V30 9 2 8 13 +M V30 10 1 10 14 +M V30 11 1 11 15 +M V30 12 2 12 16 +M V30 13 1 13 17 +M V30 14 1 14 18 +M V30 15 1 15 19 +M V30 16 1 9 13 +M V30 17 1 16 17 +M V30 18 1 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +194 + +> +Z2063791535 + +> +218.258 + +> +0.062 + +> +2 + +> +69.730 + +> +1 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL2420911 + +> +0.86 + +$$$$ +Compound 195 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.019 -1.1019 0.0 0 +M V30 3 C 1.3626 -0.6042 0.0 0 +M V30 4 N -2.5001 -1.3981 0.0 0 +M V30 5 C -0.2725 -2.3935 0.0 0 +M V30 6 C 2.6541 0.1658 0.0 0 +M V30 7 C 1.1967 -2.0854 0.0 0 +M V30 8 N -2.666 -2.8793 0.0 0 +M V30 9 C -3.6257 -0.3791 0.0 0 +M V30 10 C -1.2915 -3.4954 0.0 0 +M V30 11 O 2.6423 1.6825 0.0 0 +M V30 12 N 3.9457 -0.5806 0.0 0 +M V30 13 C -0.9953 -4.9528 0.0 0 +M V30 14 C 5.2372 0.1895 0.0 0 +M V30 15 C 6.5288 -0.5569 0.0 0 +M V30 16 O 7.8203 0.2132 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 1 3 6 +M V30 6 2 3 7 +M V30 7 1 4 8 +M V30 8 1 4 9 +M V30 9 1 5 10 +M V30 10 2 6 11 +M V30 11 1 6 12 +M V30 12 1 10 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 5 7 +M V30 17 2 8 10 +M V30 END BOND +M V30 END CTAB +M END +> +195 + +> +Z228706704 + +> +239.294 + +> +0.293 + +> +2 + +> +67.150 + +> +3 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1986588 + +> +0.87 + +$$$$ +Compound 196 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 O 12.3761 2.7502 0.0 0 +M V30 3 C 12.0569 1.2891 0.0 0 +M V30 4 N 10.6695 0.6875 0.0 0 +M V30 5 C 13.0514 0.1841 0.0 0 +M V30 6 C 10.8168 -0.798 0.0 0 +M V30 7 C 9.3557 1.4487 0.0 0 +M V30 8 C 12.2901 -1.105 0.0 0 +M V30 9 C 14.537 0.1964 0.0 0 +M V30 10 O 9.6872 -1.7925 0.0 0 +M V30 11 C 8.042 0.7121 0.0 0 +M V30 12 C 13.0268 -2.3941 0.0 0 +M V30 13 C 15.2737 -1.0927 0.0 0 +M V30 14 C 6.7282 1.4733 0.0 0 +M V30 15 C 14.5124 -2.3819 0.0 0 +M V30 16 N 5.4145 0.7366 0.0 0 +M V30 17 C 4.1008 1.4979 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 1 3 4 +M V30 3 1 3 5 +M V30 4 1 4 6 +M V30 5 1 4 7 +M V30 6 2 5 8 +M V30 7 1 5 9 +M V30 8 2 6 10 +M V30 9 1 7 11 +M V30 10 1 8 12 +M V30 11 2 9 13 +M V30 12 1 11 14 +M V30 13 2 12 15 +M V30 14 1 14 16 +M V30 15 1 16 17 +M V30 16 1 6 8 +M V30 17 1 13 15 +M V30 END BOND +M V30 END CTAB +M END +> +196 + +> +Z2372097351 + +> +218.252 + +> +1.311 + +> +1 + +> +49.410 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1329044 + +> +0.89 + +$$$$ +Compound 197 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C 1.4867 0.1665 0.0 0 +M V30 3 C 2.2241 1.4867 0.0 0 +M V30 4 C 2.4858 -0.9396 0.0 0 +M V30 5 N 3.6871 1.1893 0.0 0 +M V30 6 C 3.8536 -0.3092 0.0 0 +M V30 7 C 5.15 -1.0585 0.0 0 +M V30 8 O 5.1381 -2.5571 0.0 0 +M V30 9 N 6.4465 -0.2973 0.0 0 +M V30 10 C 7.7429 -1.0466 0.0 0 +M V30 11 C 9.0393 -0.2854 0.0 0 +M V30 12 N 10.3358 -1.0347 0.0 0 +M V30 13 C 10.4785 -2.5215 0.0 0 +M V30 14 C 11.7036 -0.4043 0.0 0 +M V30 15 N 11.9415 -2.8188 0.0 0 +M V30 16 C 12.7027 -1.5105 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 6 7 +M V30 7 2 7 8 +M V30 8 1 7 9 +M V30 9 1 9 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 1 12 14 +M V30 14 2 13 15 +M V30 15 2 14 16 +M V30 16 1 5 6 +M V30 17 1 15 16 +M V30 END BOND +M V30 END CTAB +M END +> +197 + +> +Z253447110 + +> +283.125 + +> +1.025 + +> +2 + +> +62.710 + +> +4 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 198 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4953 0.0 0 +M V30 3 N 1.2817 -2.2311 0.0 0 +M V30 4 C -1.3291 -2.2311 0.0 0 +M V30 5 C 1.2698 -3.7264 0.0 0 +M V30 6 C -1.341 -3.7264 0.0 0 +M V30 7 C -2.777 -1.7564 0.0 0 +M V30 8 N -0.0474 -4.4622 0.0 0 +M V30 9 C 2.5634 -4.4622 0.0 0 +M V30 10 C -2.7889 -4.1774 0.0 0 +M V30 11 C -3.6789 -2.9669 0.0 0 +M V30 12 C 3.8569 -3.7027 0.0 0 +M V30 13 C 2.5515 -5.9575 0.0 0 +M V30 14 C 5.1505 -4.4385 0.0 0 +M V30 15 C 3.8451 -6.7052 0.0 0 +M V30 16 C 5.1387 -5.9338 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 1 12 14 +M V30 14 2 13 15 +M V30 15 2 14 16 +M V30 16 1 6 8 +M V30 17 1 10 11 +M V30 18 1 15 16 +M V30 END BOND +M V30 END CTAB +M END +> +198 + +> +Z813019964 + +> +212.247 + +> +1.822 + +> +1 + +> +41.460 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL2419891 + +> +0.88 + +$$$$ +Compound 199 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C 1.2884 0.7565 0.0 0 +M V30 3 C 1.4303 2.2578 0.0 0 +M V30 4 C 2.6479 0.1536 0.0 0 +M V30 5 N 2.8843 2.5769 0.0 0 +M V30 6 C 3.6408 1.2766 0.0 0 +M V30 7 C 5.1184 1.1348 0.0 0 +M V30 8 O 5.7213 -0.2245 0.0 0 +M V30 9 N 5.9932 2.3642 0.0 0 +M V30 10 C 5.5204 3.8063 0.0 0 +M V30 11 C 7.4827 2.376 0.0 0 +M V30 12 C 6.7261 4.7047 0.0 0 CFG=2 +M V30 13 C 7.9318 3.8181 0.0 0 +M V30 14 C 6.7143 6.2178 0.0 0 +M V30 15 N 8.0028 6.9743 0.0 0 +M V30 16 C 7.991 8.4874 0.0 0 +M V30 17 Cl -3.499 2.9316 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 6 7 +M V30 7 2 7 8 +M V30 8 1 7 9 +M V30 9 1 9 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 1 12 14 CFG=1 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 5 6 +M V30 17 1 12 13 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 12) +M V30 END COLLECTION +M V30 END CTAB +M END +> +199 + +> +Z1457020715 + +> +286.168 + +> +-0.003 + +> +2 + +> +48.130 + +> +3 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 200 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3062 -0.7296 0.0 0 +M V30 3 N -1.318 -2.2124 0.0 0 +M V30 4 C -2.6125 0.0235 0.0 0 +M V30 5 C -0.0353 -2.9538 0.0 0 +M V30 6 C -3.9187 -0.706 0.0 0 +M V30 7 C -2.6242 1.5298 0.0 0 +M V30 8 C -5.225 0.047 0.0 0 +M V30 9 C -3.9305 2.283 0.0 0 +M V30 10 N -6.5313 -0.6825 0.0 0 +M V30 11 C -5.2368 1.5533 0.0 0 +M V30 12 C -7.8375 0.0706 0.0 0 +M V30 13 O -7.8493 1.5769 0.0 0 +M V30 14 C -9.1438 -0.659 0.0 0 +M V30 15 C -10.45 0.0941 0.0 0 +M V30 16 C -11.7563 -0.6354 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 2 8 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 9 11 +M V30 END BOND +M V30 END CTAB +M END +> +200 + +> +Z29131286 + +> +220.268 + +> +1.232 + +> +2 + +> +58.200 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3414777 + +> +0.88 + +$$$$ +Compound 201 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3144 -0.7342 0.0 0 +M V30 3 N -2.6289 0.0236 0.0 0 +M V30 4 C -1.3263 -2.2263 0.0 0 +M V30 5 C -2.6408 1.5394 0.0 0 +M V30 6 C -3.9434 -0.7105 0.0 0 +M V30 7 N -2.5579 -3.1026 0.0 0 +M V30 8 C -0.1184 -3.1026 0.0 0 +M V30 9 N -2.1079 -4.5237 0.0 0 +M V30 10 C -0.5921 -4.5237 0.0 0 +M V30 11 C 0.2842 -5.7316 0.0 0 +M V30 12 C 1.7644 -5.5658 0.0 0 +M V30 13 C -0.3434 -7.0935 0.0 0 +M V30 14 C 2.6408 -6.7737 0.0 0 +M V30 15 C 0.5329 -8.3014 0.0 0 +M V30 16 C 2.0131 -8.1356 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 1 7 9 +M V30 9 2 8 10 +M V30 10 1 10 11 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 2 13 15 +M V30 15 2 14 16 +M V30 16 1 9 10 +M V30 17 1 15 16 +M V30 END BOND +M V30 END CTAB +M END +> +201 + +> +Z116443582 + +> +215.251 + +> +1.441 + +> +1 + +> +48.990 + +> +2 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1995916 + +> +0.95 + +$$$$ +Compound 202 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.1142 1.0087 0.0 0 +M V30 3 C -0.6216 -1.3488 0.0 0 +M V30 4 N -1.126 2.51 0.0 0 +M V30 5 N -2.4162 0.2815 0.0 0 +M V30 6 C -2.1112 -1.1846 0.0 0 +M V30 7 C -2.4279 3.2724 0.0 0 +M V30 8 C -3.7181 1.0321 0.0 0 +M V30 9 C -3.7298 2.5335 0.0 0 +M V30 10 O -5.02 0.3049 0.0 0 +M V30 11 C -5.0318 3.2959 0.0 0 +M V30 12 O -5.0435 4.7972 0.0 0 +M V30 13 O -6.3337 2.5569 0.0 0 +M V30 14 C -7.6357 3.3193 0.0 0 +M V30 15 C -8.9376 2.5804 0.0 0 +M V30 16 C -10.2395 3.3428 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 7 9 +M V30 9 2 8 10 +M V30 10 1 9 11 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 5 6 +M V30 17 1 8 9 +M V30 END BOND +M V30 END CTAB +M END +> +202 + +> +Z448231276 + +> +238.263 + +> +1.060 + +> +0 + +> +58.970 + +> +4 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1971679 + +> +0.88 + +$$$$ +Compound 203 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 N 0.0 0.0 0.0 0 +M V30 2 C -1.3061 0.753 0.0 0 +M V30 3 C -0.0117 -1.4826 0.0 0 +M V30 4 N -1.3178 2.2592 0.0 0 +M V30 5 C -2.6122 0.0235 0.0 0 +M V30 6 N -1.3178 -2.2121 0.0 0 +M V30 7 C -0.0353 3.0122 0.0 0 +M V30 8 C -2.6239 3.0122 0.0 0 +M V30 9 N -4.0477 0.4942 0.0 0 +M V30 10 C -2.6239 -1.459 0.0 0 +M V30 11 C -0.047 4.5184 0.0 0 +M V30 12 C -2.6357 4.5184 0.0 0 +M V30 13 C -4.942 -0.706 0.0 0 +M V30 14 N -4.0595 -1.9062 0.0 0 +M V30 15 C -1.3531 5.2832 0.0 0 +M V30 16 C -1.3649 6.7893 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 4 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 2 5 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 15 16 +M V30 16 1 6 10 +M V30 17 1 12 15 +M V30 18 1 13 14 +M V30 END BOND +M V30 END CTAB +M END +> +203 + +> +Z57745257 + +> +217.270 + +> +1.930 + +> +1 + +> +57.700 + +> +1 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL2420911 + +> +0.92 + +$$$$ +Compound 204 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2933 -0.7356 0.0 0 +M V30 3 N 1.2814 -2.2306 0.0 0 +M V30 4 C 2.5866 0.0237 0.0 0 +M V30 5 C -0.0355 -2.9781 0.0 0 +M V30 6 C 2.5747 1.5424 0.0 0 +M V30 7 C 3.8799 -0.7119 0.0 0 +M V30 8 C -0.0474 -4.4732 0.0 0 +M V30 9 C 3.868 2.3018 0.0 0 +M V30 10 C 5.1732 0.0474 0.0 0 +M V30 11 C 1.2458 -5.2207 0.0 0 +M V30 12 C -1.3645 -5.2207 0.0 0 +M V30 13 N 5.1614 1.5662 0.0 0 +M V30 14 C 1.2339 -6.7157 0.0 0 +M V30 15 C -1.3763 -6.7157 0.0 0 +M V30 16 C -0.083 -7.4514 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 11 14 +M V30 14 2 12 15 +M V30 15 2 14 16 +M V30 16 1 10 13 +M V30 17 1 15 16 +M V30 END BOND +M V30 END CTAB +M END +> +204 + +> +Z27749507 + +> +212.247 + +> +2.076 + +> +1 + +> +41.990 + +> +3 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1977135 + +> +0.87 + +$$$$ +Compound 205 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.4885 0.0118 0.0 0 +M V30 3 N 2.3627 1.2404 0.0 0 +M V30 4 C 2.3627 -1.1931 0.0 0 +M V30 5 C 3.7804 0.7915 0.0 0 +M V30 6 C 3.7804 -0.7206 0.0 0 +M V30 7 C 1.8902 -2.6108 0.0 0 +M V30 8 C 5.0681 1.5594 0.0 0 +M V30 9 C 5.0681 -1.4649 0.0 0 +M V30 10 C 0.4134 -2.9061 0.0 0 +M V30 11 C 6.3558 0.8269 0.0 0 +M V30 12 C 6.3558 -0.697 0.0 0 +M V30 13 N -0.7088 -1.8902 0.0 0 +M V30 14 C -0.2126 -4.2647 0.0 0 +M V30 15 C -2.0201 -2.6226 0.0 0 +M V30 16 C -1.713 -4.0993 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 4 7 CFG=2 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 2 9 12 +M V30 12 1 10 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 5 6 +M V30 17 1 11 12 +M V30 18 2 15 16 +M V30 END BOND +M V30 END CTAB +M END +> +205 + +> +Z44305885 + +> +210.231 + +> +1.836 + +> +2 + +> +44.890 + +> +1 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1996176 + +> +1.0 + +$$$$ +Compound 206 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3141 0.7577 0.0 0 +M V30 3 N -1.3259 2.2731 0.0 0 +M V30 4 N -2.6283 0.0118 0.0 0 +M V30 5 C -0.0355 3.0308 0.0 0 +M V30 6 C -2.6401 -1.4799 0.0 0 +M V30 7 C 1.2549 2.2968 0.0 0 +M V30 8 C -0.0473 4.5462 0.0 0 +M V30 9 C -1.3496 -2.2257 0.0 0 +M V30 10 C -3.9543 -2.2257 0.0 0 +M V30 11 N 2.5454 3.0545 0.0 0 +M V30 12 C 1.2431 5.3039 0.0 0 +M V30 13 C -1.3615 -3.7175 0.0 0 +M V30 14 C -3.9661 -3.7175 0.0 0 +M V30 15 C 2.5335 4.5699 0.0 0 +M V30 16 C -2.6756 -4.4515 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 2 11 15 +M V30 15 1 13 16 +M V30 16 1 12 15 +M V30 17 1 14 16 +M V30 END BOND +M V30 END CTAB +M END +> +206 + +> +Z44592328 + +> +219.283 + +> +2.607 + +> +2 + +> +54.020 + +> +2 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1998193 + +> +0.86 + +$$$$ +Compound 207 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4872 0.0 0 +M V30 3 N -1.322 -2.2309 0.0 0 +M V30 4 C 1.2748 -2.2309 0.0 0 +M V30 5 C -2.6322 -1.4636 0.0 0 +M V30 6 N 1.263 -3.7181 0.0 0 +M V30 7 C 2.5614 -1.4636 0.0 0 +M V30 8 C -3.9424 -2.2073 0.0 0 +M V30 9 C -2.644 0.0472 0.0 0 +M V30 10 C 2.5496 -4.45 0.0 0 +M V30 11 N 3.848 -2.2073 0.0 0 +M V30 12 N 2.5496 0.0472 0.0 0 +M V30 13 C -5.2526 -1.44 0.0 0 +M V30 14 C -3.9542 0.8026 0.0 0 +M V30 15 C 3.8362 -3.6945 0.0 0 +M V30 16 C -5.2644 0.0708 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 7 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 2 10 15 +M V30 15 2 13 16 +M V30 16 1 11 15 +M V30 17 1 14 16 +M V30 END BOND +M V30 END CTAB +M END +> +207 + +> +Z224484568 + +> +214.223 + +> +1.711 + +> +2 + +> +80.900 + +> +2 + +> +Serine-protein kinase ATM + +> +CHEMBL1766760 + +> +1.0 + +$$$$ +Compound 208 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2886 0.7684 0.0 0 +M V30 3 N 1.2768 2.2817 0.0 0 +M V30 4 N 2.5773 0.0236 0.0 0 +M V30 5 C 2.5654 3.0502 0.0 0 +M V30 6 C 3.8659 0.7921 0.0 0 +M V30 7 O 2.5536 4.5635 0.0 0 +M V30 8 C 3.8541 2.3054 0.0 0 +M V30 9 C 5.1546 0.0472 0.0 0 +M V30 10 C 5.1428 3.0738 0.0 0 +M V30 11 C 6.4433 0.8157 0.0 0 +M V30 12 C 6.4314 2.329 0.0 0 +M V30 13 C 7.7319 0.0709 0.0 0 +M V30 14 C 7.7201 3.0975 0.0 0 +M V30 15 C 9.0206 0.8394 0.0 0 +M V30 16 C 9.0088 2.3526 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 2 8 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 2 11 13 +M V30 13 2 12 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 1 6 8 +M V30 17 1 11 12 +M V30 18 2 15 16 +M V30 END BOND +M V30 END CTAB +M END +> +208 + +> +Z1509143765 + +> +212.204 + +> +1.712 + +> +2 + +> +58.200 + +> +0 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL421646 + +> +0.94 + +$$$$ +Compound 209 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4897 0.0 0 +M V30 3 N 1.2768 -2.2227 0.0 0 +M V30 4 C -1.3241 -2.2227 0.0 0 +M V30 5 C 1.265 -3.7124 0.0 0 +M V30 6 C 2.5656 -1.466 0.0 0 +M V30 7 C -1.336 -3.7124 0.0 0 +M V30 8 C -2.6365 -1.466 0.0 0 +M V30 9 C -0.0472 -4.4573 0.0 0 +M V30 10 C -2.6483 -4.4573 0.0 0 +M V30 11 C -3.9489 -2.1991 0.0 0 +M V30 12 N -2.6602 -5.947 0.0 0 +M V30 13 C -3.9607 -3.7006 0.0 0 +M V30 14 C -3.9725 -6.6918 0.0 0 +M V30 15 O -5.2849 -5.9233 0.0 0 +M V30 16 C -3.9843 -8.1816 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 10 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 7 9 +M V30 17 1 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +209 + +> +Z1509182834 + +> +216.236 + +> +0.662 + +> +1 + +> +49.410 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1767054 + +> +0.9 + +$$$$ +Compound 210 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5084 0.0 0 +M V30 3 N -1.3199 2.2744 0.0 0 +M V30 4 C 1.2727 2.2744 0.0 0 +M V30 5 C -1.3316 3.7829 0.0 0 +M V30 6 C 1.2609 3.7829 0.0 0 +M V30 7 C 2.5573 1.532 0.0 0 +M V30 8 N -0.0471 4.5489 0.0 0 +M V30 9 C -2.6398 4.5489 0.0 0 +M V30 10 C 2.5455 4.5489 0.0 0 +M V30 11 C 3.8418 2.298 0.0 0 +M V30 12 C -3.9479 3.8065 0.0 0 +M V30 13 C 3.83 3.8065 0.0 0 +M V30 14 C 5.1264 1.5556 0.0 0 +M V30 15 C 6.4109 2.3216 0.0 0 +M V30 16 C 5.1146 0.0707 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 11 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 1 6 8 +M V30 17 1 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +210 + +> +Z951212366 + +> +216.279 + +> +2.760 + +> +1 + +> +41.460 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1949864 + +> +0.85 + +$$$$ +Compound 211 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.2305 -0.8706 0.0 0 +M V30 3 C 1.2073 -0.8706 0.0 0 +M V30 4 N -2.7165 -0.5456 0.0 0 +M V30 5 C -0.7778 -2.287 0.0 0 +M V30 6 C 0.7313 -2.287 0.0 0 +M V30 7 C 2.6701 -0.5456 0.0 0 +M V30 8 C -3.7381 -1.6485 0.0 0 +M V30 9 C -1.7994 -3.3899 0.0 0 +M V30 10 C 1.7297 -3.3899 0.0 0 +M V30 11 C 3.6685 -1.6485 0.0 0 +M V30 12 N -3.2854 -3.0648 0.0 0 +M V30 13 O -1.3466 -4.8062 0.0 0 +M V30 14 C 3.1925 -3.0648 0.0 0 +M V30 15 C -4.307 -4.1677 0.0 0 +M V30 16 C -5.793 -3.8426 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 10 14 +M V30 14 1 12 15 +M V30 15 1 15 16 +M V30 16 1 5 6 +M V30 17 1 9 12 +M V30 18 1 11 14 +M V30 END BOND +M V30 END CTAB +M END +> +211 + +> +Z152822648 + +> +234.317 + +> +2.527 + +> +0 + +> +32.670 + +> +1 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL539906 + +> +0.92 + +$$$$ +Compound 212 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.2305 -0.8706 0.0 0 +M V30 3 C 1.2073 -0.8706 0.0 0 +M V30 4 N -2.7165 -0.5456 0.0 0 +M V30 5 C -0.7778 -2.287 0.0 0 +M V30 6 C 0.7313 -2.287 0.0 0 +M V30 7 C 2.6701 -0.5456 0.0 0 +M V30 8 C -3.7381 -1.6485 0.0 0 +M V30 9 C -1.7994 -3.3899 0.0 0 +M V30 10 C 1.7297 -3.3899 0.0 0 +M V30 11 C 3.6685 -1.6485 0.0 0 +M V30 12 N -3.2854 -3.0648 0.0 0 +M V30 13 C -5.2241 -1.3234 0.0 0 +M V30 14 O -1.3466 -4.8062 0.0 0 +M V30 15 C 3.1925 -3.0648 0.0 0 +M V30 16 C -6.2457 -2.4263 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 10 15 +M V30 15 1 13 16 +M V30 16 1 5 6 +M V30 17 1 9 12 +M V30 18 1 11 15 +M V30 END BOND +M V30 END CTAB +M END +> +212 + +> +Z56757214 + +> +234.317 + +> +2.724 + +> +1 + +> +41.460 + +> +1 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL539906 + +> +0.89 + +$$$$ +Compound 213 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3038 0.7517 0.0 0 +M V30 3 N -1.3156 2.2553 0.0 0 +M V30 4 N -2.6077 0.0234 0.0 0 +M V30 5 C -2.6195 3.0071 0.0 0 +M V30 6 C -0.0352 3.0071 0.0 0 +M V30 7 C -3.9116 0.7752 0.0 0 +M V30 8 C -2.6195 -1.4565 0.0 0 +M V30 9 O -2.6312 4.5107 0.0 0 +M V30 10 C -3.9233 2.2788 0.0 0 +M V30 11 N -5.3447 0.3289 0.0 0 +M V30 12 N -5.3564 2.7487 0.0 0 +M V30 13 C -6.2374 1.5505 0.0 0 +M V30 14 C -5.8263 4.1818 0.0 0 +M V30 15 C -7.2946 4.4989 0.0 0 +M V30 16 N -8.763 4.8161 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 5 10 +M V30 10 1 7 11 +M V30 11 1 10 12 +M V30 12 2 11 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 3 15 16 +M V30 16 2 7 10 +M V30 17 1 12 13 +M V30 END BOND +M V30 END CTAB +M END +> +213 + +> +Z73835882 + +> +219.200 + +> +-0.902 + +> +0 + +> +82.230 + +> +1 + +> +Serine-protein kinase ATM + +> +CHEMBL113 + +> +0.91 + +$$$$ +Compound 214 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3042 0.7519 0.0 0 +M V30 3 N -1.3159 2.2559 0.0 0 +M V30 4 N -2.6084 0.0234 0.0 0 +M V30 5 C -2.6201 3.0079 0.0 0 +M V30 6 C -0.0352 3.0079 0.0 0 +M V30 7 C -3.9126 0.7754 0.0 0 +M V30 8 C -2.6201 -1.4569 0.0 0 +M V30 9 O -2.6319 4.5118 0.0 0 +M V30 10 C -3.9244 2.2794 0.0 0 +M V30 11 N -5.3461 0.3289 0.0 0 +M V30 12 N -5.3578 2.7494 0.0 0 +M V30 13 C -6.2391 1.5509 0.0 0 +M V30 14 C -5.8278 4.1829 0.0 0 +M V30 15 C -4.8408 5.2991 0.0 0 +M V30 16 C -5.3108 6.7325 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 5 10 +M V30 10 1 7 11 +M V30 11 1 10 12 +M V30 12 2 11 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 2 7 10 +M V30 17 1 12 13 +M V30 END BOND +M V30 END CTAB +M END +> +214 + +> +Z112207588 + +> +220.228 + +> +0.734 + +> +0 + +> +58.440 + +> +2 + +> +Serine-protein kinase ATM + +> +CHEMBL113 + +> +0.91 + +$$$$ +Compound 215 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 N 0.0 0.0 0.0 0 +M V30 2 C -1.3077 0.754 0.0 0 +M V30 3 C -0.0117 -1.4845 0.0 0 +M V30 4 N -1.3195 2.2621 0.0 0 +M V30 5 C -2.6155 0.0235 0.0 0 +M V30 6 N -1.3195 -2.215 0.0 0 +M V30 7 C -2.6745 2.9219 0.0 0 +M V30 8 C 0.0117 2.9219 0.0 0 +M V30 9 N -4.0529 0.4948 0.0 0 +M V30 10 C -2.6273 -1.4609 0.0 0 +M V30 11 C -3.0161 4.3946 0.0 0 +M V30 12 C 0.3298 4.3946 0.0 0 +M V30 13 C -4.9484 -0.7069 0.0 0 +M V30 14 N -4.0647 -1.9086 0.0 0 +M V30 15 C -2.0971 5.5728 0.0 0 +M V30 16 C -0.6126 5.5728 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 4 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 2 5 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 1 6 10 +M V30 17 1 13 14 +M V30 18 1 15 16 +M V30 END BOND +M V30 END CTAB +M END +> +215 + +> +Z57745291 + +> +217.270 + +> +1.970 + +> +1 + +> +57.700 + +> +1 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL2420911 + +> +0.9 + +$$$$ +Compound 216 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3096 -0.7432 0.0 0 +M V30 3 N -1.3214 -2.2298 0.0 0 +M V30 4 N -2.6192 0.0235 0.0 0 +M V30 5 C -2.631 -2.9731 0.0 0 +M V30 6 C -0.0353 -2.9731 0.0 0 +M V30 7 C -3.9288 -0.7196 0.0 0 +M V30 8 C -2.631 1.5337 0.0 0 +M V30 9 O -2.6428 -4.4597 0.0 0 +M V30 10 C -3.9406 -2.2062 0.0 0 +M V30 11 N -5.3682 -0.2477 0.0 0 +M V30 12 N -5.38 -2.6546 0.0 0 +M V30 13 C -6.2648 -1.4511 0.0 0 +M V30 14 C -5.8519 -4.0703 0.0 0 +M V30 15 C -7.3267 -4.3653 0.0 0 +M V30 16 C -4.8608 -5.1676 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 5 10 +M V30 10 1 7 11 +M V30 11 1 10 12 +M V30 12 2 11 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 2 7 10 +M V30 17 1 12 13 +M V30 END BOND +M V30 END CTAB +M END +> +216 + +> +Z114730422 + +> +222.244 + +> +0.798 + +> +0 + +> +58.440 + +> +1 + +> +Serine-protein kinase ATM + +> +CHEMBL113 + +> +0.92 + +$$$$ +Compound 217 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3038 0.7517 0.0 0 +M V30 3 N -1.3156 2.2553 0.0 0 +M V30 4 N -2.6077 0.0234 0.0 0 +M V30 5 C -2.6195 3.0071 0.0 0 +M V30 6 C -0.0352 3.0071 0.0 0 +M V30 7 C -3.9116 0.7752 0.0 0 +M V30 8 C -2.6195 -1.4565 0.0 0 +M V30 9 O -2.6312 4.5107 0.0 0 +M V30 10 C -3.9233 2.2788 0.0 0 +M V30 11 N -5.3447 0.3289 0.0 0 +M V30 12 N -5.3564 2.7487 0.0 0 +M V30 13 C -6.2374 1.5505 0.0 0 +M V30 14 C -5.8263 4.1818 0.0 0 +M V30 15 C -7.2946 4.4989 0.0 0 +M V30 16 C -8.763 4.8161 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 5 10 +M V30 10 1 7 11 +M V30 11 1 10 12 +M V30 12 2 11 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 3 15 16 +M V30 16 2 7 10 +M V30 17 1 12 13 +M V30 END BOND +M V30 END CTAB +M END +> +217 + +> +Z112209998 + +> +218.212 + +> +0.060 + +> +0 + +> +58.440 + +> +1 + +> +Serine-protein kinase ATM + +> +CHEMBL113 + +> +0.93 + +$$$$ +Compound 218 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 16 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4872 0.0 0 +M V30 3 N -1.322 -2.2309 0.0 0 +M V30 4 C 1.2748 -2.2309 0.0 0 +M V30 5 C -2.6322 -1.4636 0.0 0 +M V30 6 N 1.263 -3.7181 0.0 0 +M V30 7 C 2.5614 -1.4636 0.0 0 +M V30 8 C -3.9424 -2.2073 0.0 0 +M V30 9 C -2.644 0.0472 0.0 0 +M V30 10 C 2.5496 -4.45 0.0 0 +M V30 11 N 3.848 -2.2073 0.0 0 +M V30 12 N 2.5496 0.0472 0.0 0 +M V30 13 C -5.2526 -1.44 0.0 0 +M V30 14 C -3.9542 0.8026 0.0 0 +M V30 15 C 3.8362 -3.6945 0.0 0 +M V30 16 N -5.2644 0.0708 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 7 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 2 10 15 +M V30 15 2 13 16 +M V30 16 1 11 15 +M V30 17 1 14 16 +M V30 END BOND +M V30 END CTAB +M END +> +218 + +> +Z225339688 + +> +215.211 + +> +0.473 + +> +2 + +> +93.790 + +> +2 + +> +Serine-protein kinase ATM + +> +CHEMBL1766760 + +> +0.88 + +$$$$ +Compound 219 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.8986 -1.2061 0.0 0 +M V30 3 C 1.4189 -0.4493 0.0 0 +M V30 4 N -0.0236 -2.4122 0.0 0 +M V30 5 N -2.4122 -1.1943 0.0 0 +M V30 6 C 1.4071 -1.9392 0.0 0 +M V30 7 C 2.7078 0.3074 0.0 0 +M V30 8 C -3.169 -2.4832 0.0 0 +M V30 9 C 2.696 -2.6724 0.0 0 +M V30 10 C 3.9968 -0.4256 0.0 0 +M V30 11 C -4.6826 -2.4714 0.0 0 +M V30 12 C 3.9849 -1.9156 0.0 0 +M V30 13 C 5.2857 0.331 0.0 0 +M V30 14 C -5.4394 -3.7603 0.0 0 +M V30 15 C -5.4394 -1.1588 0.0 0 +M V30 16 C -6.953 -3.7484 0.0 0 +M V30 17 C -6.953 -1.147 0.0 0 +M V30 18 C -7.7216 -2.4359 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 10 13 +M V30 13 2 11 14 +M V30 14 1 11 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 2 16 18 +M V30 18 1 4 6 +M V30 19 1 10 12 +M V30 20 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +219 + +> +Z55176420 + +> +254.350 + +> +4.574 + +> +1 + +> +24.920 + +> +3 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1983534 + +> +0.9 + +$$$$ +Compound 220 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5061 0.0 0 +M V30 3 N -1.3178 2.2592 0.0 0 +M V30 4 C 1.2708 2.2592 0.0 0 +M V30 5 C -1.3296 3.7653 0.0 0 +M V30 6 C 1.259 3.7653 0.0 0 +M V30 7 C 2.5533 1.5179 0.0 0 +M V30 8 N -0.047 4.5184 0.0 0 +M V30 9 C -2.6357 4.5184 0.0 0 +M V30 10 C 2.5416 4.5184 0.0 0 +M V30 11 C 3.8359 2.2709 0.0 0 +M V30 12 N -2.6475 6.0245 0.0 0 +M V30 13 C 3.8241 3.7888 0.0 0 +M V30 14 C -3.9536 6.7776 0.0 0 +M V30 15 C -3.9653 8.2837 0.0 0 +M V30 16 C -5.2715 9.0368 0.0 0 +M V30 17 C -2.6828 9.0368 0.0 0 +M V30 18 C -5.2832 10.543 0.0 0 +M V30 19 C -2.6945 10.543 0.0 0 +M V30 20 C -4.0006 11.3078 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 17 19 +M V30 19 2 18 20 +M V30 20 1 6 8 +M V30 21 1 11 13 +M V30 22 1 19 20 +M V30 END BOND +M V30 END CTAB +M END +> +220 + +> +Z44493009 + +> +265.310 + +> +0.831 + +> +2 + +> +53.490 + +> +4 + +> +parp2 + +> + + +> + + +$$$$ +Compound 221 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.8943 1.2237 0.0 0 +M V30 3 C 1.412 0.4706 0.0 0 +M V30 4 N -0.0235 2.4475 0.0 0 +M V30 5 N -2.4004 1.2355 0.0 0 +M V30 6 C 1.4002 1.9768 0.0 0 +M V30 7 C 2.6946 -0.2706 0.0 0 +M V30 8 C -3.1535 -0.047 0.0 0 +M V30 9 C 2.6829 2.7417 0.0 0 +M V30 10 C 3.9772 0.4942 0.0 0 +M V30 11 C -4.6597 -0.0353 0.0 0 +M V30 12 C 3.9655 2.0121 0.0 0 +M V30 13 C -5.4246 -1.3179 0.0 0 +M V30 14 C -5.4246 1.2708 0.0 0 +M V30 15 C -6.9308 -1.3061 0.0 0 +M V30 16 C -6.9308 1.2826 0.0 0 +M V30 17 C -7.6957 0.0 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 13 15 +M V30 15 2 14 16 +M V30 16 2 15 17 +M V30 17 1 4 6 +M V30 18 1 10 12 +M V30 19 1 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +221 + +> +Z55176258 + +> +240.323 + +> +4.075 + +> +1 + +> +24.920 + +> +3 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1983534 + +> +0.88 + +$$$$ +Compound 222 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3086 0.7663 0.0 0 +M V30 3 N -2.6172 0.0235 0.0 0 +M V30 4 C -1.3204 2.2753 0.0 0 +M V30 5 C -2.629 -1.4618 0.0 0 +M V30 6 C -0.0353 3.0298 0.0 0 +M V30 7 C -2.629 3.0298 0.0 0 +M V30 8 C -3.9376 -2.1928 0.0 0 +M V30 9 C -0.0471 4.5389 0.0 0 +M V30 10 C 1.2496 2.2871 0.0 0 +M V30 11 C -2.6408 4.5389 0.0 0 +M V30 12 C -3.9494 -3.6782 0.0 0 +M V30 13 C -5.2462 -1.4383 0.0 0 +M V30 14 C -1.3557 5.2934 0.0 0 +M V30 15 C 1.2378 5.2934 0.0 0 +M V30 16 C 2.5347 3.0416 0.0 0 +M V30 17 C -5.258 -4.421 0.0 0 +M V30 18 C -6.5549 -2.1692 0.0 0 +M V30 19 C 2.5229 4.5625 0.0 0 +M V30 20 C -6.5667 -3.6547 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 1 9 14 +M V30 14 2 9 15 +M V30 15 1 10 16 +M V30 16 1 12 17 +M V30 17 2 13 18 +M V30 18 1 15 19 +M V30 19 2 17 20 +M V30 20 2 11 14 +M V30 21 2 16 19 +M V30 22 1 18 20 +M V30 END BOND +M V30 END CTAB +M END +> +222 + +> +Z27749745 + +> +261.318 + +> +4.003 + +> +1 + +> +29.100 + +> +3 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL594596 + +> +0.92 + +$$$$ +Compound 223 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2879 -0.7443 0.0 0 +M V30 3 N 1.276 -2.2331 0.0 0 +M V30 4 N 2.5758 0.0236 0.0 0 +M V30 5 C 2.564 -2.9775 0.0 0 +M V30 6 C -0.0354 -2.9775 0.0 0 +M V30 7 C 3.8637 -0.7207 0.0 0 +M V30 8 O 2.5521 -4.4663 0.0 0 +M V30 9 C 3.8519 -2.2095 0.0 0 +M V30 10 C -1.3469 -2.2095 0.0 0 +M V30 11 C 5.1516 0.0472 0.0 0 +M V30 12 C 5.1398 -2.9539 0.0 0 +M V30 13 C -2.6585 -2.9539 0.0 0 +M V30 14 C -1.3588 -0.6971 0.0 0 +M V30 15 C 6.4395 -0.6971 0.0 0 +M V30 16 C 6.4277 -2.1859 0.0 0 +M V30 17 C -3.97 -2.1859 0.0 0 +M V30 18 C -2.6703 0.0708 0.0 0 +M V30 19 C -3.9819 -0.6734 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 2 11 15 +M V30 15 2 12 16 +M V30 16 1 13 17 +M V30 17 2 14 18 +M V30 18 2 17 19 +M V30 19 2 7 9 +M V30 20 1 15 16 +M V30 21 1 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +223 + +> +Z278334116 + +> +252.268 + +> +3.152 + +> +1 + +> +49.410 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL421646 + +> +0.85 + +$$$$ +Compound 224 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2858 -0.7431 0.0 0 +M V30 3 N 2.5716 0.0117 0.0 0 +M V30 4 C 1.274 -2.2295 0.0 0 +M V30 5 C -0.0353 -2.9727 0.0 0 +M V30 6 C 2.5598 -2.9727 0.0 0 +M V30 7 O -1.3448 -2.2059 0.0 0 +M V30 8 C -0.0471 -4.459 0.0 0 +M V30 9 C 2.548 -4.459 0.0 0 +M V30 10 C -2.6542 -2.9491 0.0 0 +M V30 11 C 1.2386 -5.1904 0.0 0 +M V30 12 C -3.9636 -2.1823 0.0 0 +M V30 13 C -5.273 -2.9255 0.0 0 +M V30 14 C -3.9754 -0.6724 0.0 0 +M V30 15 C -6.5824 -2.1587 0.0 0 +M V30 16 C -5.2848 0.0825 0.0 0 +M V30 17 C -6.5942 -0.6488 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 2 14 16 +M V30 16 2 15 17 +M V30 17 1 9 11 +M V30 18 1 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +224 + +> +Z25777178 + +> +227.259 + +> +2.534 + +> +1 + +> +52.320 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3105429 + +> +0.96 + +$$$$ +Compound 225 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2921 -0.7468 0.0 0 +M V30 3 N 1.2803 -2.2405 0.0 0 +M V30 4 C 2.5843 0.0118 0.0 0 +M V30 5 C -0.0355 -2.9873 0.0 0 +M V30 6 C 3.8764 -0.7349 0.0 0 +M V30 7 C 2.5724 1.5292 0.0 0 +M V30 8 C -0.0474 -4.481 0.0 0 +M V30 9 C 5.1686 0.0237 0.0 0 +M V30 10 C 3.8646 2.2879 0.0 0 +M V30 11 C 1.2447 -5.216 0.0 0 +M V30 12 C -1.3632 -5.216 0.0 0 +M V30 13 C 5.1567 1.5411 0.0 0 +M V30 14 C 6.4607 -0.7231 0.0 0 +M V30 15 C 1.2328 -6.7097 0.0 0 +M V30 16 C -1.3751 -6.7097 0.0 0 +M V30 17 C 6.4489 2.2998 0.0 0 +M V30 18 C 7.7529 0.0355 0.0 0 +M V30 19 C -0.0829 -7.4565 0.0 0 +M V30 20 C 7.741 1.5648 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 2 9 14 +M V30 14 1 11 15 +M V30 15 2 12 16 +M V30 16 2 13 17 +M V30 17 1 14 18 +M V30 18 2 15 19 +M V30 19 1 17 20 +M V30 20 1 10 13 +M V30 21 1 16 19 +M V30 22 2 18 20 +M V30 END BOND +M V30 END CTAB +M END +> +225 + +> +Z27749593 + +> +261.318 + +> +4.003 + +> +1 + +> +29.100 + +> +3 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL594596 + +> +0.87 + +$$$$ +Compound 226 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.311 -0.7323 0.0 0 +M V30 3 N -2.6221 0.0236 0.0 0 +M V30 4 C -1.3229 -2.2205 0.0 0 +M V30 5 C -2.634 1.5355 0.0 0 +M V30 6 N -2.5513 -3.0946 0.0 0 +M V30 7 C -0.1181 -3.0946 0.0 0 +M V30 8 C -3.9451 2.2914 0.0 0 +M V30 9 N -2.1024 -4.512 0.0 0 +M V30 10 C -0.5905 -4.512 0.0 0 +M V30 11 C -5.2561 1.5591 0.0 0 +M V30 12 C -3.9569 3.8033 0.0 0 +M V30 13 C 0.2834 -5.7168 0.0 0 +M V30 14 C -6.5672 2.315 0.0 0 +M V30 15 C -5.268 4.5593 0.0 0 +M V30 16 C 1.7599 -5.5514 0.0 0 +M V30 17 C -0.3425 -7.0751 0.0 0 +M V30 18 C -6.5791 3.8269 0.0 0 +M V30 19 C 2.634 -6.7562 0.0 0 +M V30 20 C 0.5315 -8.2799 0.0 0 +M V30 21 C 2.0079 -8.1146 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 10 13 +M V30 13 1 11 14 +M V30 14 2 12 15 +M V30 15 2 13 16 +M V30 16 1 13 17 +M V30 17 2 14 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 2 19 21 +M V30 21 2 9 10 +M V30 22 1 15 18 +M V30 23 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +226 + +> +Z62434158 + +> +277.321 + +> +4.234 + +> +2 + +> +57.780 + +> +4 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1995916 + +> +0.92 + +$$$$ +Compound 227 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.4456 -0.4502 0.0 0 +M V30 3 C 0.8768 -1.2086 0.0 0 +M V30 4 N -2.8913 0.0236 0.0 0 +M V30 5 C -1.4575 -1.9433 0.0 0 +M V30 6 C 2.3699 -1.1968 0.0 0 +M V30 7 C -0.0236 -2.4173 0.0 0 +M V30 8 N -3.7919 -1.1849 0.0 0 +M V30 9 C -3.3653 1.4693 0.0 0 +M V30 10 C -2.9032 -2.3936 0.0 0 +M V30 11 O 3.1046 0.1184 0.0 0 +M V30 12 N 3.1046 -2.4884 0.0 0 +M V30 13 C -3.3771 -3.8156 0.0 0 +M V30 14 C 4.5977 -2.4766 0.0 0 +M V30 15 C 5.3324 -3.7682 0.0 0 +M V30 16 C 6.8254 -3.7563 0.0 0 +M V30 17 C 4.574 -5.0598 0.0 0 +M V30 18 C 7.5601 -5.048 0.0 0 +M V30 19 C 5.3087 -6.3514 0.0 0 +M V30 20 C 6.8017 -6.3396 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 1 3 6 +M V30 6 2 3 7 +M V30 7 1 4 8 +M V30 8 1 4 9 +M V30 9 1 5 10 +M V30 10 2 6 11 +M V30 11 1 6 12 +M V30 12 1 10 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 17 19 +M V30 19 2 18 20 +M V30 20 1 5 7 +M V30 21 2 8 10 +M V30 22 1 19 20 +M V30 END BOND +M V30 END CTAB +M END +> +227 + +> +Z220581054 + +> +285.364 + +> +2.822 + +> +1 + +> +46.920 + +> +3 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1986588 + +> +0.87 + +$$$$ +Compound 228 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5077 0.0 0 +M V30 3 N -1.3193 2.2734 0.0 0 +M V30 4 C 1.2722 2.2734 0.0 0 +M V30 5 C -1.3311 3.7812 0.0 0 +M V30 6 C 1.2604 3.7812 0.0 0 +M V30 7 C 2.5561 1.5313 0.0 0 +M V30 8 C -2.6386 4.5351 0.0 0 +M V30 9 C -0.0471 4.5351 0.0 0 +M V30 10 C 2.5444 4.5351 0.0 0 +M V30 11 C 3.8401 2.297 0.0 0 +M V30 12 O -2.6504 6.0429 0.0 0 +M V30 13 N -3.9461 3.8048 0.0 0 +M V30 14 C 3.8283 3.8048 0.0 0 +M V30 15 C -5.2537 4.5587 0.0 0 +M V30 16 C -6.5612 3.8283 0.0 0 +M V30 17 C -6.573 2.3441 0.0 0 +M V30 18 C -7.8688 4.5822 0.0 0 +M V30 19 C -7.8805 1.602 0.0 0 +M V30 20 C -9.1763 3.8519 0.0 0 +M V30 21 C -9.1881 2.3677 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 2 19 21 +M V30 21 1 6 9 +M V30 22 1 11 14 +M V30 23 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +228 + +> +Z79407714 + +> +278.305 + +> +2.083 + +> +2 + +> +58.200 + +> +3 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 229 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 O 8.2565 3.7961 0.0 0 +M V30 3 C 8.7073 2.3725 0.0 0 +M V30 4 N 7.8057 1.1625 0.0 0 +M V30 5 C 10.1308 1.9217 0.0 0 +M V30 6 C 8.6836 -0.0474 0.0 0 CFG=2 +M V30 7 C 6.2873 1.1744 0.0 0 +M V30 8 C 10.119 0.427 0.0 0 +M V30 9 C 11.4239 2.681 0.0 0 +M V30 10 C 8.209 -1.4709 0.0 0 +M V30 11 C 5.5162 -0.1186 0.0 0 +M V30 12 C 11.412 -0.3084 0.0 0 +M V30 13 C 12.7169 1.9455 0.0 0 +M V30 14 N 9.2055 -2.5742 0.0 0 +M V30 15 C 6.2635 -1.4116 0.0 0 +M V30 16 C 3.9977 -0.1067 0.0 0 +M V30 17 C 12.7051 0.4507 0.0 0 +M V30 18 C 5.4925 -2.7047 0.0 0 +M V30 19 C 3.2385 -1.3998 0.0 0 +M V30 20 C 3.974 -2.6928 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 1 3 4 +M V30 3 1 3 5 +M V30 4 1 4 6 +M V30 5 1 4 7 +M V30 6 2 5 8 +M V30 7 1 5 9 +M V30 8 1 6 10 CFG=3 +M V30 9 1 7 11 +M V30 10 1 8 12 +M V30 11 2 9 13 +M V30 12 1 10 14 +M V30 13 2 11 15 +M V30 14 1 11 16 +M V30 15 2 12 17 +M V30 16 1 15 18 +M V30 17 2 16 19 +M V30 18 2 18 20 +M V30 19 1 6 8 +M V30 20 1 13 17 +M V30 21 1 19 20 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 6) +M V30 END COLLECTION +M V30 END CTAB +M END +> +229 + +> +Z2598479077 + +> +252.311 + +> +2.137 + +> +1 + +> +46.330 + +> +3 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL596168 + +> +0.86 + +$$$$ +Compound 230 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 N 0.0 0.0 0.0 0 +M V30 2 N -1.3102 0.7672 0.0 0 +M V30 3 C 1.2866 0.7672 0.0 0 +M V30 4 C -1.322 2.2781 0.0 0 +M V30 5 C -2.7503 0.3187 0.0 0 +M V30 6 N 2.5732 0.0236 0.0 0 +M V30 7 C 1.2748 2.2781 0.0 0 +M V30 8 N -2.7621 2.7503 0.0 0 +M V30 9 C -0.0354 3.0454 0.0 0 +M V30 10 N -3.6474 1.5463 0.0 0 +M V30 11 C -3.2225 -1.0977 0.0 0 +M V30 12 C 3.8599 0.7908 0.0 0 +M V30 13 C 5.1465 0.0472 0.0 0 +M V30 14 C 6.4332 0.8144 0.0 0 +M V30 15 C 5.1347 -1.44 0.0 0 +M V30 16 C 7.7198 0.0708 0.0 0 +M V30 17 C 6.4214 -2.1837 0.0 0 +M V30 18 C 7.708 -1.4164 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 1 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 4 9 +M V30 9 2 5 10 +M V30 10 1 5 11 +M V30 11 1 6 12 +M V30 12 1 12 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 2 16 18 +M V30 18 2 7 9 +M V30 19 1 8 10 +M V30 20 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +230 + +> +Z57085294 + +> +239.276 + +> +1.579 + +> +1 + +> +55.110 + +> +3 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1375418 + +> +0.89 + +$$$$ +Compound 231 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3125 -0.7449 0.0 0 +M V30 3 C 1.2888 -0.7449 0.0 0 +M V30 4 N -1.3243 -2.2348 0.0 0 +M V30 5 N -2.625 0.0236 0.0 0 +M V30 6 C 2.5777 0.0236 0.0 0 +M V30 7 C -2.6368 -2.9679 0.0 0 +M V30 8 C -3.9376 -0.7213 0.0 0 +M V30 9 O 2.5659 1.5372 0.0 0 +M V30 10 N 3.8666 -0.7213 0.0 0 +M V30 11 C -3.9494 -2.2112 0.0 0 +M V30 12 C -2.6487 -4.4578 0.0 0 +M V30 13 O -5.2501 0.0472 0.0 0 +M V30 14 C 5.1555 0.0472 0.0 0 +M V30 15 C -5.2619 -2.9443 0.0 0 +M V30 16 C -3.9612 -5.2028 0.0 0 +M V30 17 C 6.4444 -0.6976 0.0 0 +M V30 18 C -5.2737 -4.4342 0.0 0 +M V30 19 C 7.7333 0.0709 0.0 0 +M V30 20 C 6.4326 -2.1875 0.0 0 +M V30 21 C 9.0221 -0.674 0.0 0 +M V30 22 C 7.7214 -2.9206 0.0 0 +M V30 23 C 9.0103 -2.1639 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 7 12 +M V30 12 2 8 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 2 12 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 2 17 19 +M V30 19 1 17 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 2 21 23 +M V30 23 1 8 11 +M V30 24 1 16 18 +M V30 25 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +231 + +> +Z16079432 + +> +325.385 + +> +1.916 + +> +2 + +> +70.560 + +> +5 + +> +parp3 + +> + + +> + + +$$$$ +Compound 232 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2866 0.7554 0.0 0 +M V30 3 O 2.5733 0.0118 0.0 0 +M V30 4 C 1.2748 2.2664 0.0 0 +M V30 5 C 2.5615 3.0218 0.0 0 +M V30 6 C -0.0354 3.0218 0.0 0 +M V30 7 N 3.8481 2.29 0.0 0 +M V30 8 C 2.5497 4.5328 0.0 0 +M V30 9 C -0.0472 4.5328 0.0 0 +M V30 10 C 5.1348 3.0454 0.0 0 +M V30 11 C 1.2394 5.2882 0.0 0 +M V30 12 O -1.3574 5.2882 0.0 0 +M V30 13 O 5.123 4.5564 0.0 0 +M V30 14 C 6.4214 2.3136 0.0 0 +M V30 15 O 1.2276 6.7992 0.0 0 +M V30 16 C -2.6677 4.5564 0.0 0 +M V30 17 C 2.5142 7.5547 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 1 15 17 +M V30 17 1 9 11 +M V30 END BOND +M V30 END CTAB +M END +> +232 + +> +Z56989659 + +> +239.225 + +> +1.478 + +> +2 + +> +84.860 + +> +4 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1997495 + +> +0.89 + +$$$$ +Compound 233 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.2304 -0.8705 0.0 0 +M V30 3 C 1.2072 -0.8705 0.0 0 +M V30 4 N -2.7162 -0.5455 0.0 0 +M V30 5 C -0.7777 -2.2867 0.0 0 +M V30 6 C 0.7312 -2.2867 0.0 0 +M V30 7 C 2.6698 -0.5455 0.0 0 +M V30 8 C -3.7377 -1.6483 0.0 0 +M V30 9 C -1.7992 -3.3895 0.0 0 +M V30 10 C 1.7295 -3.3895 0.0 0 +M V30 11 C 3.668 -1.6483 0.0 0 +M V30 12 N -3.285 -3.0644 0.0 0 +M V30 13 C -5.2235 -1.3233 0.0 0 +M V30 14 O -1.3465 -4.8056 0.0 0 +M V30 15 C 3.1921 -3.0644 0.0 0 +M V30 16 C -6.245 -2.426 0.0 0 +M V30 17 N -7.2665 -3.5288 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 10 15 +M V30 15 1 13 16 +M V30 16 3 16 17 +M V30 17 1 5 6 +M V30 18 1 9 12 +M V30 19 1 11 15 +M V30 END BOND +M V30 END CTAB +M END +> +233 + +> +Z56886503 + +> +245.300 + +> +1.118 + +> +1 + +> +65.250 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3616846 + +> +0.9 + +$$$$ +Compound 234 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4944 0.0 0 +M V30 3 C 1.2809 -2.2417 0.0 0 +M V30 4 C -1.3284 -2.2417 0.0 0 +M V30 5 C 1.2691 -3.7361 0.0 0 +M V30 6 C -1.3402 -3.7361 0.0 0 +M V30 7 N 2.5619 -4.4834 0.0 0 +M V30 8 C -0.0474 -4.4834 0.0 0 +M V30 9 C 3.8547 -3.7124 0.0 0 +M V30 10 O 3.8429 -2.1942 0.0 0 +M V30 11 C 5.1476 -4.4596 0.0 0 +M V30 12 C 6.4404 -3.6887 0.0 0 +M V30 13 C 5.1357 -5.9541 0.0 0 +M V30 14 N 6.4285 -2.1705 0.0 0 +M V30 15 C 7.7332 -4.4359 0.0 0 +M V30 16 C 6.4285 -6.6895 0.0 0 +M V30 17 C 7.7214 -5.9304 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 7 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 1 12 15 +M V30 15 2 13 16 +M V30 16 2 15 17 +M V30 17 1 6 8 +M V30 18 1 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +234 + +> +Z56040697 + +> +246.692 + +> +3.314 + +> +2 + +> +55.120 + +> +2 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL396034 + +> +0.86 + +$$$$ +Compound 235 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0117 -1.4866 0.0 0 +M V30 3 C -1.3214 -2.23 0.0 0 +M V30 4 C 1.2742 -2.23 0.0 0 +M V30 5 N -2.6311 -1.463 0.0 0 +M V30 6 C -1.3332 -3.7167 0.0 0 +M V30 7 C 1.2625 -3.7167 0.0 0 +M V30 8 C -2.6429 0.0471 0.0 0 +M V30 9 C -0.0471 -4.4482 0.0 0 +M V30 10 O -1.3568 0.8023 0.0 0 +M V30 11 C -3.9526 0.8023 0.0 0 +M V30 12 N -3.9644 2.3126 0.0 0 +M V30 13 C -5.2741 3.0677 0.0 0 +M V30 14 C -2.6783 3.0677 0.0 0 +M V30 15 C -5.2859 4.578 0.0 0 +M V30 16 C -2.6901 4.578 0.0 0 +M V30 17 N -3.9998 5.3331 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 2 8 10 +M V30 10 1 8 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 7 9 +M V30 18 1 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +235 + +> +Z85920180 + +> +253.728 + +> +1.008 + +> +2 + +> +44.370 + +> +3 + +> +ATM + +> + + +> + + +$$$$ +Compound 236 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3098 -0.7434 0.0 0 +M V30 3 N -1.3216 -2.2302 0.0 0 +M V30 4 N -2.6196 0.0236 0.0 0 +M V30 5 C -2.6314 -2.9736 0.0 0 +M V30 6 C -0.0354 -2.9736 0.0 0 +M V30 7 C -3.9295 -0.7198 0.0 0 +M V30 8 C -2.6314 1.534 0.0 0 +M V30 9 O -2.6432 -4.4605 0.0 0 +M V30 10 C -3.9413 -2.2066 0.0 0 +M V30 11 C -0.0472 -4.4605 0.0 0 +M V30 12 N -5.3691 -0.2478 0.0 0 +M V30 13 N -5.3809 -2.655 0.0 0 +M V30 14 O 1.239 -5.2039 0.0 0 +M V30 15 O -1.357 -5.2039 0.0 0 +M V30 16 C -6.2659 -1.4514 0.0 0 +M V30 17 C -5.8529 -4.0711 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 5 10 +M V30 10 1 6 11 +M V30 11 1 7 12 +M V30 12 1 10 13 +M V30 13 2 11 14 +M V30 14 1 11 15 +M V30 15 2 12 16 +M V30 16 1 13 17 +M V30 17 2 7 10 +M V30 18 1 13 16 +M V30 END BOND +M V30 END CTAB +M END +> +236 + +> +Z90662175 + +> +238.200 + +> +0.052 + +> +1 + +> +95.740 + +> +2 + +> +Serine-protein kinase ATM + +> +CHEMBL113 + +> +0.89 + +$$$$ +Compound 237 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5079 0.0 0 +M V30 3 N -1.3194 2.2737 0.0 0 +M V30 4 C 1.2723 2.2737 0.0 0 +M V30 5 C -1.3312 3.7816 0.0 0 +M V30 6 C 1.2605 3.7816 0.0 0 +M V30 7 C 2.5564 1.5315 0.0 0 +M V30 8 N -0.0471 4.5356 0.0 0 +M V30 9 C -2.6389 4.5356 0.0 0 +M V30 10 C 2.5446 4.5356 0.0 0 +M V30 11 C 3.8405 2.2972 0.0 0 +M V30 12 C -3.9466 3.7934 0.0 0 +M V30 13 C 3.8287 3.7934 0.0 0 +M V30 14 C -5.2542 4.5474 0.0 0 +M V30 15 C -6.5619 3.8052 0.0 0 +M V30 16 O -6.5737 2.3208 0.0 0 +M V30 17 O -7.8696 4.5592 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 6 8 +M V30 18 1 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +237 + +> +Z56983044 + +> +232.235 + +> +0.445 + +> +2 + +> +78.760 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL2377256 + +> +0.88 + +$$$$ +Compound 238 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5065 0.0 0 +M V30 3 N -1.3182 2.2716 0.0 0 +M V30 4 C 1.2711 2.2716 0.0 0 +M V30 5 C -1.33 3.7782 0.0 0 +M V30 6 C 1.2594 3.7782 0.0 0 +M V30 7 C 2.5541 1.5301 0.0 0 +M V30 8 N -0.047 4.5315 0.0 0 +M V30 9 C -2.6365 4.5315 0.0 0 +M V30 10 C 2.5423 4.5315 0.0 0 +M V30 11 C 3.837 2.2951 0.0 0 +M V30 12 C -3.943 3.8017 0.0 0 +M V30 13 C -2.6482 6.0381 0.0 0 +M V30 14 C 3.8253 3.8017 0.0 0 +M V30 15 C -5.2495 4.555 0.0 0 +M V30 16 C -3.9547 6.7914 0.0 0 +M V30 17 C -5.2612 6.0616 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 12 15 +M V30 15 2 13 16 +M V30 16 2 15 17 +M V30 17 1 6 8 +M V30 18 1 11 14 +M V30 19 1 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +238 + +> +Z57042021 + +> +222.242 + +> +2.403 + +> +1 + +> +41.460 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL395092 + +> +0.88 + +$$$$ +Compound 239 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3054 0.7526 0.0 0 +M V30 3 N -1.3172 2.258 0.0 0 +M V30 4 N -2.6108 0.0235 0.0 0 +M V30 5 C -2.6226 3.0107 0.0 0 +M V30 6 C -0.0352 3.0107 0.0 0 +M V30 7 C -3.9163 0.7762 0.0 0 +M V30 8 C -2.6226 -1.4583 0.0 0 +M V30 9 O -2.6344 4.5161 0.0 0 +M V30 10 C -3.928 2.2815 0.0 0 +M V30 11 N -5.3511 0.3293 0.0 0 +M V30 12 N -5.3628 2.752 0.0 0 +M V30 13 C -6.2449 1.5524 0.0 0 +M V30 14 C -5.8333 4.1868 0.0 0 +M V30 15 C -4.8454 5.304 0.0 0 +M V30 16 O -5.3158 6.7388 0.0 0 +M V30 17 O -3.3988 5.01 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 5 10 +M V30 10 1 7 11 +M V30 11 1 10 12 +M V30 12 2 11 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 2 7 10 +M V30 18 1 12 13 +M V30 END BOND +M V30 END CTAB +M END +> +239 + +> +Z104477882 + +> +238.200 + +> +-0.658 + +> +1 + +> +95.740 + +> +2 + +> +Serine-protein kinase ATM + +> +CHEMBL113 + +> +0.87 + +$$$$ +Compound 240 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2916 0.7583 0.0 0 +M V30 3 N 2.5832 0.0118 0.0 0 +M V30 4 N 1.2797 2.2751 0.0 0 +M V30 5 C 3.8748 0.7702 0.0 0 +M V30 6 C 2.5713 -1.4812 0.0 0 +M V30 7 C 2.5713 3.0335 0.0 0 +M V30 8 C 3.8629 2.2869 0.0 0 +M V30 9 C 5.1664 0.0236 0.0 0 +M V30 10 C 3.8629 -2.2277 0.0 0 +M V30 11 O 2.5595 4.5502 0.0 0 +M V30 12 C 5.1545 3.0453 0.0 0 +M V30 13 C 6.458 0.782 0.0 0 +M V30 14 C 3.8511 -3.7207 0.0 0 +M V30 15 C 6.4461 2.3106 0.0 0 +M V30 16 O 2.5358 -4.4554 0.0 0 +M V30 17 O 5.1427 -4.4554 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 10 14 +M V30 14 2 12 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 1 7 8 +M V30 18 1 13 15 +M V30 END BOND +M V30 END CTAB +M END +> +240 + +> +Z166653136 + +> +234.208 + +> +0.394 + +> +2 + +> +86.710 + +> +3 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3105416 + +> +0.88 + +$$$$ +Compound 241 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2853 -0.7429 0.0 0 +M V30 3 N 2.5707 0.0235 0.0 0 +M V30 4 N 1.2735 -2.2287 0.0 0 +M V30 5 C 2.5589 1.533 0.0 0 +M V30 6 C -0.0353 -2.9599 0.0 0 +M V30 7 C 3.8443 2.2877 0.0 0 +M V30 8 C 1.25 2.2877 0.0 0 +M V30 9 C -0.0471 -4.4458 0.0 0 +M V30 10 C -1.3443 -2.2052 0.0 0 +M V30 11 C 3.8325 3.7972 0.0 0 +M V30 12 C 1.2382 3.7972 0.0 0 +M V30 13 C -1.3561 -5.1769 0.0 0 +M V30 14 C -2.6533 -2.9363 0.0 0 +M V30 15 C 2.5236 4.5519 0.0 0 +M V30 16 C -2.6651 -4.4222 0.0 0 +M V30 17 N 2.5118 6.0613 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 2 11 15 +M V30 15 2 13 16 +M V30 16 1 15 17 +M V30 17 1 12 15 +M V30 18 1 14 16 +M V30 END BOND +M V30 END CTAB +M END +> +241 + +> +Z234854052 + +> +227.262 + +> +1.783 + +> +3 + +> +67.150 + +> +2 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL354676 + +> +0.94 + +$$$$ +Compound 242 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 -1.486 0.0 0 +M V30 3 N 1.2737 -2.2173 0.0 0 +M V30 4 N -1.3209 -2.2173 0.0 0 +M V30 5 C 2.5593 -1.4624 0.0 0 +M V30 6 C -2.6301 -1.4624 0.0 0 +M V30 7 C 2.5475 0.0471 0.0 0 +M V30 8 C 3.8449 -2.1937 0.0 0 +M V30 9 C -3.9393 -2.1937 0.0 0 +M V30 10 C -2.6419 0.0471 0.0 0 +M V30 11 C 3.8331 0.8138 0.0 0 +M V30 12 C 5.1305 -1.4389 0.0 0 +M V30 13 C -5.2484 -1.4389 0.0 0 +M V30 14 C -3.9511 0.8138 0.0 0 +M V30 15 N 3.8213 2.3234 0.0 0 +M V30 16 C 5.1187 0.0707 0.0 0 +M V30 17 C -5.2602 0.0707 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 11 15 +M V30 15 2 11 16 +M V30 16 2 13 17 +M V30 17 1 12 16 +M V30 18 1 14 17 +M V30 END BOND +M V30 END CTAB +M END +> +242 + +> +Z234815906 + +> +227.262 + +> +1.783 + +> +3 + +> +67.150 + +> +2 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL354676 + +> +0.91 + +$$$$ +Compound 243 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 O 6.6191 0.9455 0.0 0 +M V30 3 C 7.9075 0.2127 0.0 0 +M V30 4 N 7.8957 -1.2765 0.0 0 +M V30 5 C 9.1959 0.9692 0.0 0 +M V30 6 C 6.6664 -2.1512 0.0 0 CFG=2 +M V30 7 C 9.1013 -2.1512 0.0 0 +M V30 8 C 9.1841 2.4821 0.0 0 +M V30 9 C 10.4842 0.2363 0.0 0 +M V30 10 C 7.1156 -3.5696 0.0 0 +M V30 11 C 5.2244 -1.6784 0.0 0 +M V30 12 C 8.6285 -3.5696 0.0 0 +M V30 13 C 10.4724 3.2504 0.0 0 +M V30 14 C 7.872 3.2504 0.0 0 +M V30 15 C 11.7726 0.9928 0.0 0 +M V30 16 N 4.1015 -2.6713 0.0 0 +M V30 17 C 11.7608 2.5058 0.0 0 +M V30 18 C 10.4606 4.7634 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 1 3 4 +M V30 3 1 3 5 +M V30 4 1 4 6 +M V30 5 1 4 7 +M V30 6 2 5 8 +M V30 7 1 5 9 +M V30 8 1 6 10 +M V30 9 1 6 11 CFG=3 +M V30 10 1 7 12 +M V30 11 1 8 13 +M V30 12 1 8 14 +M V30 13 2 9 15 +M V30 14 1 11 16 +M V30 15 2 13 17 +M V30 16 1 13 18 +M V30 17 1 10 12 +M V30 18 1 15 17 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 6) +M V30 END COLLECTION +M V30 END CTAB +M END +> +243 + +> +Z1457711561 + +> +232.321 + +> +1.824 + +> +1 + +> +46.330 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL589755 + +> +0.87 + +$$$$ +Compound 244 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.8943 1.2237 0.0 0 +M V30 3 C 1.412 0.4706 0.0 0 +M V30 4 N -0.0235 2.4475 0.0 0 +M V30 5 N -2.4004 1.2355 0.0 0 +M V30 6 C 1.4002 1.9768 0.0 0 +M V30 7 C 2.6946 -0.2706 0.0 0 +M V30 8 C -3.1535 -0.047 0.0 0 +M V30 9 C 2.6829 2.7417 0.0 0 +M V30 10 C 3.9772 0.4942 0.0 0 +M V30 11 C -4.6597 -0.0353 0.0 0 +M V30 12 C 3.9655 2.0121 0.0 0 +M V30 13 C -5.4246 1.2708 0.0 0 +M V30 14 C -5.4246 -1.3179 0.0 0 +M V30 15 N -6.9308 1.2826 0.0 0 +M V30 16 C -6.9308 -1.3061 0.0 0 +M V30 17 C -7.6957 0.0 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 13 15 +M V30 15 2 14 16 +M V30 16 2 15 17 +M V30 17 1 4 6 +M V30 18 1 10 12 +M V30 19 1 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +244 + +> +Z86239022 + +> +241.312 + +> +2.578 + +> +1 + +> +37.810 + +> +3 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1983534 + +> +0.86 + +$$$$ +Compound 245 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.8968 1.2272 0.0 0 +M V30 3 C 1.416 0.472 0.0 0 +M V30 4 N -0.0236 2.4544 0.0 0 +M V30 5 N -2.4072 1.239 0.0 0 +M V30 6 C 1.4042 1.9824 0.0 0 +M V30 7 C 2.7022 -0.2714 0.0 0 +M V30 8 C -3.1624 -0.0472 0.0 0 +M V30 9 C 2.6904 2.7494 0.0 0 +M V30 10 C 3.9885 0.4956 0.0 0 +M V30 11 C -4.6729 -0.0354 0.0 0 +M V30 12 C 3.9767 2.0178 0.0 0 +M V30 13 C -5.4399 -1.3216 0.0 0 +M V30 14 S -6.9385 -1.4632 0.0 0 +M V30 15 C -4.8381 -2.6786 0.0 0 +M V30 16 C -7.2571 -2.9146 0.0 0 +M V30 17 C -5.9591 -3.6698 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 11 13 +M V30 13 1 13 14 +M V30 14 2 13 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 4 6 +M V30 18 1 10 12 +M V30 19 2 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +245 + +> +Z86362958 + +> +260.378 + +> +4.370 + +> +1 + +> +24.920 + +> +4 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1421720 + +> +0.86 + +$$$$ +Compound 246 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.493 0.0118 0.0 0 +M V30 3 C -0.7702 -1.2916 0.0 0 +M V30 4 C 2.2395 1.3271 0.0 0 +M V30 5 C 2.2395 -1.2797 0.0 0 +M V30 6 C 3.7326 1.339 0.0 0 +M V30 7 C 3.7326 -1.2679 0.0 0 +M V30 8 C 4.4673 0.0473 0.0 0 +M V30 9 C 5.9603 0.0592 0.0 0 +M V30 10 N 6.8372 1.2916 0.0 0 +M V30 11 N 6.8372 -1.1494 0.0 0 +M V30 12 C 8.2592 0.8413 0.0 0 +M V30 13 C 8.2592 -0.6754 0.0 0 +M V30 14 C 9.5508 1.6115 0.0 0 +M V30 15 C 9.5508 -1.4219 0.0 0 +M V30 16 C 10.8424 0.8768 0.0 0 +M V30 17 C 10.8424 -0.6517 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 2 6 8 +M V30 8 1 8 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 2 14 16 +M V30 16 2 15 17 +M V30 17 1 7 8 +M V30 18 2 12 13 +M V30 19 1 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +246 + +> +Z50129948 + +> +224.258 + +> +3.685 + +> +1 + +> +37.910 + +> +2 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL176554 + +> +0.86 + +$$$$ +Compound 247 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3074 -0.7302 0.0 0 +M V30 3 C 1.2838 -0.7302 0.0 0 +M V30 4 C -2.6148 0.0235 0.0 0 +M V30 5 C -1.3192 -2.2143 0.0 0 +M V30 6 C -3.9223 -0.7067 0.0 0 +M V30 7 C -2.6266 -2.9446 0.0 0 +M V30 8 C -3.934 -2.1908 0.0 0 +M V30 9 C -5.3593 -0.2355 0.0 0 +M V30 10 N -5.371 -2.6384 0.0 0 +M V30 11 C -5.8304 1.2014 0.0 0 +M V30 12 C -6.2544 -1.4369 0.0 0 +M V30 13 C -7.3027 1.5194 0.0 0 +M V30 14 C -4.841 2.3204 0.0 0 +M V30 15 C -7.7739 2.9564 0.0 0 +M V30 16 C -5.3121 3.7574 0.0 0 +M V30 17 N -6.7845 4.0754 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 2 6 8 +M V30 8 1 6 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 7 8 +M V30 18 1 10 12 +M V30 19 1 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +247 + +> +Z385446106 + +> +228.290 + +> +2.250 + +> +2 + +> +37.050 + +> +2 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1987982 + +> +0.85 + +$$$$ +Compound 248 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.5127 0.0 0 +M V30 3 N -1.3236 2.281 0.0 0 +M V30 4 C 1.2764 2.281 0.0 0 +M V30 5 C 2.5646 1.5364 0.0 0 +M V30 6 C 1.2646 3.7938 0.0 0 +M V30 7 C 3.8529 2.3046 0.0 0 +M V30 8 C 2.5528 4.562 0.0 0 +M V30 9 N 5.1411 1.56 0.0 0 +M V30 10 C 3.841 3.8174 0.0 0 +M V30 11 C 6.4293 2.3282 0.0 0 +M V30 12 O 6.4175 3.841 0.0 0 +M V30 13 C 7.7176 1.5837 0.0 0 +M V30 14 C 9.0058 2.3519 0.0 0 +M V30 15 C 10.2941 1.6073 0.0 0 +M V30 16 O 11.5823 2.3755 0.0 0 +M V30 17 O 10.2822 0.1181 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 2 7 10 +M V30 10 1 9 11 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 8 10 +M V30 END BOND +M V30 END CTAB +M END +> +248 + +> +Z57198217 + +> +236.224 + +> +-0.249 + +> +3 + +> +109.490 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3414766 + +> +0.88 + +$$$$ +Compound 249 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 Cl 3.5921 0.0945 0.0 0 +M V30 3 S -0.5671 -7.3734 0.0 0 +M V30 4 O 0.9216 -7.3616 0.0 0 +M V30 5 O -2.0796 -7.3616 0.0 0 +M V30 6 N -0.579 -8.8623 0.0 0 +M V30 7 C -0.579 -5.8609 0.0 0 +M V30 8 C -1.8906 -9.6067 0.0 0 +M V30 9 C 0.7089 -5.1046 0.0 0 +M V30 10 C -1.8906 -5.1046 0.0 0 +M V30 11 C -1.9024 -11.0955 0.0 0 +M V30 12 C 0.6971 -3.5921 0.0 0 +M V30 13 C 1.9969 -5.8373 0.0 0 +M V30 14 C -1.9024 -3.5921 0.0 0 +M V30 15 N -3.214 -11.8282 0.0 0 +M V30 16 C 1.9851 -2.8359 0.0 0 +M V30 17 C -0.6144 -2.8359 0.0 0 +M V30 18 C 3.2849 -5.081 0.0 0 +M V30 19 N 3.2731 -3.5803 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 3 4 +M V30 2 2 3 5 +M V30 3 1 3 6 +M V30 4 1 3 7 +M V30 5 1 6 8 +M V30 6 1 7 9 +M V30 7 2 7 10 +M V30 8 1 8 11 +M V30 9 1 9 12 +M V30 10 2 9 13 +M V30 11 1 10 14 +M V30 12 1 11 15 +M V30 13 2 12 16 +M V30 14 1 12 17 +M V30 15 1 13 18 +M V30 16 1 16 19 +M V30 17 2 14 17 +M V30 18 2 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +249 + +> +Z3235015426 + +> +251.305 + +> +0.903 + +> +2 + +> +85.080 + +> +3 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 250 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.8922 1.2209 0.0 0 +M V30 3 C -2.3948 1.2326 0.0 0 +M V30 4 C -0.4461 2.6531 0.0 0 +M V30 5 N -2.8644 2.6649 0.0 0 +M V30 6 C -1.667 3.5453 0.0 0 +M V30 7 C -1.6787 5.048 0.0 0 +M V30 8 O -0.3991 5.7993 0.0 0 +M V30 9 N -2.9818 5.7993 0.0 0 +M V30 10 C -4.2849 5.0715 0.0 0 +M V30 11 C -2.9936 7.302 0.0 0 +M V30 12 C -5.588 5.8228 0.0 0 CFG=2 +M V30 13 C -4.2967 8.0534 0.0 0 +M V30 14 C -5.5998 7.3255 0.0 0 +M V30 15 C -6.8911 5.095 0.0 0 +M V30 16 N -8.1942 5.8463 0.0 0 +M V30 17 C -9.4973 5.1184 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 6 7 +M V30 7 2 7 8 +M V30 8 1 7 9 +M V30 9 1 9 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 1 12 15 CFG=1 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 1 5 6 +M V30 18 1 13 14 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 12) +M V30 END COLLECTION +M V30 END CTAB +M END +> +250 + +> +Z763761462 + +> +255.744 + +> +0.406 + +> +2 + +> +48.130 + +> +3 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 251 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2911 -0.7462 0.0 0 CFG=2 +M V30 3 C -1.3148 -0.7462 0.0 0 +M V30 4 C 1.2792 -2.2387 0.0 0 +M V30 5 C 2.5822 0.0236 0.0 0 +M V30 6 C -1.3266 -2.2387 0.0 0 +M V30 7 N -0.0355 -2.9731 0.0 0 +M V30 8 N 2.5703 1.5398 0.0 0 +M V30 9 C -0.0473 -4.4655 0.0 0 +M V30 10 N 1.2437 -5.1999 0.0 0 +M V30 11 C -1.3621 -5.1999 0.0 0 +M V30 12 C 1.2318 -6.6924 0.0 0 +M V30 13 N -2.8072 -4.7261 0.0 0 +M V30 14 C -1.374 -6.6924 0.0 0 +M V30 15 N -0.0829 -7.4268 0.0 0 +M V30 16 C -3.7075 -5.9343 0.0 0 +M V30 17 N -2.8191 -7.1425 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 1 2 5 CFG=1 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 7 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 2 11 14 +M V30 14 2 12 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 1 6 7 +M V30 18 1 14 15 +M V30 19 2 16 17 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 2) +M V30 END COLLECTION +M V30 END CTAB +M END +> +251 + +> +Z803081352 + +> +234.258 + +> +-0.582 + +> +2 + +> +92.950 + +> +2 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL271138 + +> +0.87 + +$$$$ +Compound 252 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.1653 1.5003 0.0 0 +M V30 3 C 1.453 -0.2953 0.0 0 +M V30 4 N 1.1931 2.1264 0.0 0 +M V30 5 N -1.4766 2.2563 0.0 0 +M V30 6 C 2.1973 1.0159 0.0 0 +M V30 7 C 2.1973 -1.583 0.0 0 +M V30 8 C -2.7879 1.5239 0.0 0 +M V30 9 C 3.6858 1.0277 0.0 0 +M V30 10 C 3.6858 -1.5711 0.0 0 +M V30 11 O -2.7997 0.0354 0.0 0 +M V30 12 C -4.0992 2.2799 0.0 0 +M V30 13 C 4.43 -0.2598 0.0 0 +M V30 14 C -5.4105 1.5475 0.0 0 +M V30 15 C -6.7218 2.3036 0.0 0 +M V30 16 O -6.7336 3.8157 0.0 0 +M V30 17 O -8.0331 1.5711 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 4 6 +M V30 18 1 10 13 +M V30 END BOND +M V30 END CTAB +M END +> +252 + +> +Z56803696 + +> +254.305 + +> +1.752 + +> +2 + +> +79.290 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL448447 + +> +0.95 + +$$$$ +Compound 253 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4491 -1.4184 0.0 0 +M V30 3 N -0.4491 -2.6241 0.0 0 +M V30 4 C 1.8676 -1.8676 0.0 0 +M V30 5 C -1.9621 -2.6122 0.0 0 +M V30 6 C 0.4255 -3.8297 0.0 0 +M V30 7 C 1.8557 -3.3569 0.0 0 +M V30 8 C 3.156 -1.0992 0.0 0 +M V30 9 C -2.7186 -3.9007 0.0 0 +M V30 10 C -2.7186 -1.3002 0.0 0 +M V30 11 O -0.0472 -5.2482 0.0 0 +M V30 12 C 3.1442 -4.1016 0.0 0 +M V30 13 C 4.4444 -1.8321 0.0 0 +M V30 14 C -4.2316 -3.8888 0.0 0 +M V30 15 C -4.2316 -1.2884 0.0 0 +M V30 16 C 4.4326 -3.3333 0.0 0 +M V30 17 C -5.0 -2.5768 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 5 10 +M V30 10 2 6 11 +M V30 11 1 7 12 +M V30 12 2 8 13 +M V30 13 1 9 14 +M V30 14 2 10 15 +M V30 15 2 12 16 +M V30 16 2 14 17 +M V30 17 1 6 7 +M V30 18 1 13 16 +M V30 19 1 15 17 +M V30 END BOND +M V30 END CTAB +M END +> +253 + +> +Z57398053 + +> +223.227 + +> +2.393 + +> +0 + +> +37.380 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL611980 + +> +0.86 + +$$$$ +Compound 254 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4941 0.0 0 +M V30 3 N -1.3281 -2.2293 0.0 0 +M V30 4 C 1.2806 -2.2293 0.0 0 +M V30 5 C -2.6444 -1.4704 0.0 0 +M V30 6 N 1.4229 -3.7116 0.0 0 +M V30 7 C 2.6444 -1.6008 0.0 0 +M V30 8 C -3.9606 -2.2056 0.0 0 +M V30 9 N 2.8815 -4.0081 0.0 0 +M V30 10 C 3.6405 -2.7036 0.0 0 +M V30 11 C -5.2769 -1.4467 0.0 0 +M V30 12 C 5.1227 -2.5376 0.0 0 +M V30 13 C 5.7275 -1.1502 0.0 0 +M V30 14 C 6.0003 -3.7472 0.0 0 +M V30 15 C 7.2098 -0.9842 0.0 0 +M V30 16 C 7.4825 -3.5812 0.0 0 +M V30 17 C 8.0873 -2.1937 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 2 14 16 +M V30 16 2 15 17 +M V30 17 1 9 10 +M V30 18 1 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +254 + +> +Z183047826 + +> +229.278 + +> +2.984 + +> +2 + +> +57.780 + +> +4 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1995916 + +> +0.91 + +$$$$ +Compound 255 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.5122 0.0 0 +M V30 3 N -1.3232 2.2802 0.0 0 +M V30 4 C 1.2759 2.2802 0.0 0 +M V30 5 C -1.335 3.7925 0.0 0 +M V30 6 C 1.2641 3.7925 0.0 0 +M V30 7 C 2.5638 1.5359 0.0 0 +M V30 8 C -0.0472 4.5605 0.0 0 +M V30 9 C 2.5519 4.5605 0.0 0 +M V30 10 C 3.8516 2.3038 0.0 0 +M V30 11 C 3.8398 3.8161 0.0 0 +M V30 12 C 5.1394 1.5595 0.0 0 +M V30 13 C 6.4272 2.3275 0.0 0 +M V30 14 C 5.1276 0.0708 0.0 0 +M V30 15 C 7.715 1.5831 0.0 0 +M V30 16 C 6.4154 -0.6734 0.0 0 +M V30 17 C 7.7032 0.0945 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 2 9 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 2 14 16 +M V30 16 2 15 17 +M V30 17 1 6 8 +M V30 18 1 10 11 +M V30 19 1 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +255 + +> +Z1218937011 + +> +223.270 + +> +2.884 + +> +1 + +> +29.100 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1688212 + +> +0.87 + +$$$$ +Compound 256 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.4536 -0.2954 0.0 0 +M V30 3 C -0.1654 1.5008 0.0 0 +M V30 4 N 2.4463 -1.3945 0.0 0 +M V30 5 C 2.1981 1.0163 0.0 0 +M V30 6 C -1.4772 2.2572 0.0 0 +M V30 7 C 1.1936 2.1272 0.0 0 +M V30 8 N 3.8053 -0.7681 0.0 0 +M V30 9 C 2.1272 -2.8481 0.0 0 +M V30 10 C 3.6517 0.7208 0.0 0 +M V30 11 O -1.489 3.7699 0.0 0 +M V30 12 N -2.789 1.5126 0.0 0 +M V30 13 C 4.7508 1.7372 0.0 0 +M V30 14 C -4.1008 2.269 0.0 0 +M V30 15 C -5.4126 1.5245 0.0 0 +M V30 16 C -6.7244 2.2808 0.0 0 +M V30 17 C -5.4244 0.0354 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 1 3 6 +M V30 6 2 3 7 +M V30 7 1 4 8 +M V30 8 1 4 9 +M V30 9 1 5 10 +M V30 10 2 6 11 +M V30 11 1 6 12 +M V30 12 1 10 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 1 5 7 +M V30 18 2 8 10 +M V30 END BOND +M V30 END CTAB +M END +> +256 + +> +Z220832556 + +> +251.348 + +> +2.311 + +> +1 + +> +46.920 + +> +3 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1986588 + +> +0.9 + +$$$$ +Compound 257 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.314 -0.7339 0.0 0 +M V30 3 N -2.6281 0.0236 0.0 0 +M V30 4 C -1.3259 -2.2256 0.0 0 +M V30 5 C -2.6399 1.539 0.0 0 +M V30 6 N -2.5571 -3.1016 0.0 0 +M V30 7 C -0.1183 -3.1016 0.0 0 +M V30 8 C -3.954 2.2966 0.0 0 +M V30 9 C -1.3495 2.2966 0.0 0 +M V30 10 N -2.1072 -4.5223 0.0 0 +M V30 11 C -0.5919 -4.5223 0.0 0 +M V30 12 C 0.2841 -5.7298 0.0 0 +M V30 13 C 1.7639 -5.5641 0.0 0 +M V30 14 C -0.3433 -7.0912 0.0 0 +M V30 15 C 2.6399 -6.7716 0.0 0 +M V30 16 C 0.5327 -8.2988 0.0 0 +M V30 17 C 2.0125 -8.133 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 2 14 16 +M V30 16 2 15 17 +M V30 17 1 10 11 +M V30 18 1 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +257 + +> +Z116305602 + +> +229.278 + +> +2.764 + +> +2 + +> +57.780 + +> +3 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1995916 + +> +0.91 + +$$$$ +Compound 258 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.2003 0.8943 0.0 0 +M V30 3 C 0.4471 -1.4122 0.0 0 +M V30 4 C 1.1886 2.4007 0.0 0 +M V30 5 C 2.4007 0.0235 0.0 0 +M V30 6 C 1.93 -1.4004 0.0 0 +M V30 7 N -0.1176 3.1656 0.0 0 +M V30 8 C 2.4713 3.1656 0.0 0 +M V30 9 N -1.5534 2.7184 0.0 0 +M V30 10 C -0.1294 4.672 0.0 0 +M V30 11 C 2.4595 4.672 0.0 0 +M V30 12 C -2.4478 3.9424 0.0 0 +M V30 13 N 1.1533 5.4369 0.0 0 +M V30 14 C -1.5651 5.1427 0.0 0 +M V30 15 C -3.9541 3.9541 0.0 0 +M V30 16 O -4.7073 2.6714 0.0 0 +M V30 17 O -4.7073 5.2604 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 4 7 +M V30 7 2 4 8 +M V30 8 1 7 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 10 13 +M V30 13 2 10 14 +M V30 14 1 12 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 5 6 +M V30 18 2 11 13 +M V30 19 1 12 14 +M V30 END BOND +M V30 END CTAB +M END +> +258 + +> +Z1263738754 + +> +245.257 + +> +2.382 + +> +1 + +> +67.490 + +> +2 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1977619 + +> +0.93 + +$$$$ +Compound 259 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5065 0.0 0 +M V30 3 N -1.3182 2.2716 0.0 0 +M V30 4 C 1.2711 2.2716 0.0 0 +M V30 5 C -1.33 3.7782 0.0 0 +M V30 6 C 1.2594 3.7782 0.0 0 +M V30 7 C 2.5541 1.5301 0.0 0 +M V30 8 N -0.047 4.5315 0.0 0 +M V30 9 C -2.6365 4.5315 0.0 0 +M V30 10 C 2.5423 4.5315 0.0 0 +M V30 11 C 3.837 2.2951 0.0 0 +M V30 12 C -3.943 3.8017 0.0 0 +M V30 13 C -2.6482 6.0381 0.0 0 +M V30 14 C 3.8253 3.8017 0.0 0 +M V30 15 C -5.2495 4.555 0.0 0 +M V30 16 C -3.9547 6.7914 0.0 0 +M V30 17 N -5.2612 6.0616 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 7 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 1 12 15 +M V30 15 2 13 16 +M V30 16 2 15 17 +M V30 17 1 6 8 +M V30 18 2 11 14 +M V30 19 1 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +259 + +> +Z275254054 + +> +223.230 + +> +2.428 + +> +1 + +> +58.900 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL127066 + +> +0.89 + +$$$$ +Compound 260 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3085 -0.7308 0.0 0 +M V30 3 N -1.3203 -2.2162 0.0 0 +M V30 4 C -2.617 0.0235 0.0 0 +M V30 5 C -0.0353 -2.9589 0.0 0 +M V30 6 C -3.9255 -0.7073 0.0 0 +M V30 7 C -2.6288 1.5325 0.0 0 +M V30 8 C 1.2495 -2.1926 0.0 0 +M V30 9 C -5.2341 0.0471 0.0 0 +M V30 10 C -3.9373 2.2869 0.0 0 +M V30 11 C 1.2377 -0.6837 0.0 0 +M V30 12 C 2.5345 -2.9353 0.0 0 +M V30 13 N -6.5426 -0.6837 0.0 0 +M V30 14 C -5.2458 1.556 0.0 0 +M V30 15 C 2.5227 0.0707 0.0 0 +M V30 16 C 3.8194 -2.169 0.0 0 +M V30 17 C -7.8511 0.0707 0.0 0 +M V30 18 C 3.8076 -0.6601 0.0 0 +M V30 19 O -7.8629 1.5796 0.0 0 +M V30 20 C -9.1596 -0.6601 0.0 0 +M V30 21 C -10.4682 0.0943 0.0 0 +M V30 22 C -11.7767 -0.6365 0.0 0 +M V30 23 O -11.7885 -2.1219 0.0 0 +M V30 24 O -13.0852 0.1178 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 2 9 14 +M V30 14 1 11 15 +M V30 15 2 12 16 +M V30 16 1 13 17 +M V30 17 2 15 18 +M V30 18 2 17 19 +M V30 19 1 17 20 +M V30 20 1 20 21 +M V30 21 1 21 22 +M V30 22 2 22 23 +M V30 23 1 22 24 +M V30 24 1 10 14 +M V30 25 1 16 18 +M V30 END BOND +M V30 END CTAB +M END +> +260 + +> +Z1508928337 + +> +326.347 + +> +1.925 + +> +3 + +> +95.500 + +> +7 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3414756 + +> +0.9 + +$$$$ +Compound 261 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.4267 0.4518 0.0 0 +M V30 3 C 2.5205 -0.5588 0.0 0 +M V30 4 C 1.7477 1.9379 0.0 0 +M V30 5 C 3.9473 -0.107 0.0 0 +M V30 6 C 3.1744 2.3897 0.0 0 +M V30 7 N 4.8746 -1.2721 0.0 0 +M V30 8 C 4.2683 1.3791 0.0 0 +M V30 9 C 6.3727 -1.2602 0.0 0 +M V30 10 N 5.6118 2.0449 0.0 0 +M V30 11 O 7.0148 -2.6038 0.0 0 +M V30 12 C 7.3001 -0.0713 0.0 0 +M V30 13 C 6.9553 1.4029 0.0 0 +M V30 14 C 8.7268 -0.4993 0.0 0 +M V30 15 C 8.0491 2.4373 0.0 0 +M V30 16 C 9.8207 0.535 0.0 0 +M V30 17 C 9.4759 2.0093 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 1 9 12 +M V30 12 1 10 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 2 14 16 +M V30 16 2 15 17 +M V30 17 1 6 8 +M V30 18 2 12 13 +M V30 19 1 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +261 + +> +Z1650974438 + +> +244.676 + +> +3.468 + +> +2 + +> +41.130 + +> +0 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1727312 + +> +0.88 + +$$$$ +Compound 262 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.5122 0.0 0 +M V30 3 N -1.3232 2.2802 0.0 0 +M V30 4 C 1.2759 2.2802 0.0 0 +M V30 5 C -1.335 3.7925 0.0 0 +M V30 6 C 1.2641 3.7925 0.0 0 +M V30 7 C 2.5638 1.5359 0.0 0 +M V30 8 N -0.0472 4.5605 0.0 0 +M V30 9 C 2.5519 4.5605 0.0 0 +M V30 10 C 3.8516 2.3038 0.0 0 +M V30 11 C 3.8398 3.8161 0.0 0 +M V30 12 C 5.1394 1.5595 0.0 0 +M V30 13 C 6.4272 2.3275 0.0 0 +M V30 14 C 5.1276 0.0708 0.0 0 +M V30 15 C 7.715 1.5831 0.0 0 +M V30 16 C 6.4154 -0.6734 0.0 0 +M V30 17 C 7.7032 0.0945 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 2 9 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 2 14 16 +M V30 16 2 15 17 +M V30 17 1 6 8 +M V30 18 1 10 11 +M V30 19 1 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +262 + +> +Z1694627946 + +> +222.242 + +> +2.193 + +> +1 + +> +41.460 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL266540 + +> +0.89 + +$$$$ +Compound 263 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2907 -0.746 0.0 0 +M V30 3 C -1.3144 -0.746 0.0 0 +M V30 4 C 1.2789 -2.2381 0.0 0 +M V30 5 C 2.5815 0.0236 0.0 0 +M V30 6 C -1.3263 -2.2381 0.0 0 +M V30 7 C -2.6289 0.0236 0.0 0 +M V30 8 N -0.0355 -2.9723 0.0 0 +M V30 9 C -0.0473 -4.4644 0.0 0 +M V30 10 N 1.2434 -5.1986 0.0 0 +M V30 11 C -1.3618 -5.1986 0.0 0 +M V30 12 C 1.2315 -6.6907 0.0 0 +M V30 13 N -2.8065 -4.7249 0.0 0 +M V30 14 C -1.3736 -6.6907 0.0 0 +M V30 15 N -0.0828 -7.4249 0.0 0 +M V30 16 C -3.7065 -5.9328 0.0 0 +M V30 17 N -2.8183 -7.1407 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 3 7 +M V30 7 1 4 8 +M V30 8 1 8 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 2 11 14 +M V30 14 2 12 15 +M V30 15 2 13 16 +M V30 16 1 14 17 +M V30 17 1 6 8 +M V30 18 1 14 15 +M V30 19 1 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +263 + +> +Z57745246 + +> +233.270 + +> +1.067 + +> +1 + +> +66.930 + +> +1 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL271138 + +> +0.89 + +$$$$ +Compound 264 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5065 0.0 0 +M V30 3 N -1.3182 2.2716 0.0 0 +M V30 4 C 1.2711 2.2716 0.0 0 +M V30 5 C -1.33 3.7782 0.0 0 CFG=2 +M V30 6 C 1.2594 3.7782 0.0 0 +M V30 7 C 2.5541 1.5301 0.0 0 +M V30 8 N -0.047 4.5315 0.0 0 +M V30 9 C -2.6365 4.5315 0.0 0 +M V30 10 C 2.5423 4.5315 0.0 0 +M V30 11 C 3.837 2.2951 0.0 0 +M V30 12 C -3.943 3.8017 0.0 0 +M V30 13 C -2.6482 6.0381 0.0 0 +M V30 14 C 3.8253 3.8017 0.0 0 +M V30 15 C -5.2495 4.555 0.0 0 +M V30 16 C -3.9547 6.7914 0.0 0 +M V30 17 C -5.2612 6.0616 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 CFG=1 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 12 15 +M V30 15 2 13 16 +M V30 16 2 15 17 +M V30 17 1 6 8 +M V30 18 1 11 14 +M V30 19 1 16 17 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 5) +M V30 END COLLECTION +M V30 END CTAB +M END +> +264 + +> +Z48978308 + +> +224.258 + +> +2.320 + +> +2 + +> +41.130 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3594136 + +> +0.89 + +$$$$ +Compound 265 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 17 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4932 0.0 0 +M V30 3 C 1.2799 -2.2398 0.0 0 +M V30 4 C -1.3273 -2.2398 0.0 0 +M V30 5 C 2.5716 -1.4813 0.0 0 +M V30 6 C 1.268 -3.733 0.0 0 +M V30 7 C -1.3391 -3.733 0.0 0 +M V30 8 O 2.5598 0.0355 0.0 0 +M V30 9 N 3.8634 -2.2279 0.0 0 +M V30 10 C -0.0474 -4.4796 0.0 0 +M V30 11 F -2.6546 -4.4796 0.0 0 +M V30 12 C 5.1551 -1.4695 0.0 0 CFG=2 +M V30 13 F -0.0592 -5.9729 0.0 0 +M V30 14 C 6.4469 -2.2161 0.0 0 +M V30 15 C 5.1433 0.0474 0.0 0 +M V30 16 O 7.7387 -1.4576 0.0 0 +M V30 17 N 6.4351 -3.7093 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 7 11 +M V30 11 1 12 9 CFG=1 +M V30 12 1 10 13 +M V30 13 1 12 14 +M V30 14 1 12 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 1 7 10 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STEABS ATOMS=(1 12) +M V30 END COLLECTION +M V30 END CTAB +M END +> +265 + +> +Z1629028792 + +> +262.640 + +> +0.445 + +> +2 + +> +72.190 + +> +3 + +> +ATM + +> + + +> + + +$$$$ +Compound 266 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O 0.4484 1.4397 0.0 0 +M V30 3 O -0.472 -1.4161 0.0 0 +M V30 4 N -1.4397 0.472 0.0 0 +M V30 5 C 1.4161 -0.4484 0.0 0 +M V30 6 C -1.7583 1.9472 0.0 0 CFG=2 +M V30 7 C -2.5608 -0.5192 0.0 0 +M V30 8 C 2.6198 0.4484 0.0 0 +M V30 9 C 1.8646 -1.8646 0.0 0 +M V30 10 C -3.1981 2.4192 0.0 0 +M V30 11 C -0.6608 2.9621 0.0 0 +M V30 12 C -4.0006 -0.0472 0.0 0 +M V30 13 N 3.8236 -0.4248 0.0 0 +M V30 14 C 2.608 1.959 0.0 0 +M V30 15 N 3.3515 -1.8528 0.0 0 +M V30 16 C 0.9677 -3.0683 0.0 0 +M V30 17 C -4.3192 1.4279 0.0 0 +M V30 18 N 0.7552 2.5136 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 1 6 11 CFG=3 +M V30 11 1 7 12 +M V30 12 2 8 13 +M V30 13 1 8 14 +M V30 14 1 9 15 +M V30 15 1 9 16 +M V30 16 1 10 17 +M V30 17 1 11 18 +M V30 18 1 12 17 +M V30 19 1 13 15 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 6) +M V30 END COLLECTION +M V30 END CTAB +M END +> +266 + +> +Z871859290 + +> +272.367 + +> +1.034 + +> +2 + +> +92.080 + +> +2 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 267 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.224 0.8891 0.0 0 +M V30 3 C 1.2009 0.8891 0.0 0 +M V30 4 N -2.702 0.5889 0.0 0 +M V30 5 C -0.7736 2.321 0.0 0 +M V30 6 C 0.7274 2.321 0.0 0 +M V30 7 C 2.6558 0.5889 0.0 0 +M V30 8 C -3.7182 1.7089 0.0 0 +M V30 9 C -1.7898 3.441 0.0 0 +M V30 10 C 1.7205 3.441 0.0 0 +M V30 11 C 3.6489 1.7089 0.0 0 CFG=2 +M V30 12 N -3.2678 3.1408 0.0 0 +M V30 13 C -5.1962 1.4087 0.0 0 +M V30 14 O -1.3394 4.8729 0.0 0 +M V30 15 C 3.1755 3.1408 0.0 0 +M V30 16 C 5.1039 1.4087 0.0 0 +M V30 17 C -5.6697 0.0 0.0 0 +M V30 18 N -6.1431 -1.4087 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 10 15 +M V30 15 1 11 16 CFG=3 +M V30 16 1 13 17 +M V30 17 3 17 18 +M V30 18 1 5 6 +M V30 19 1 9 12 +M V30 20 1 11 15 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 11) +M V30 END COLLECTION +M V30 END CTAB +M END +> +267 + +> +Z56891276 + +> +259.327 + +> +1.637 + +> +1 + +> +65.250 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3616846 + +> +0.88 + +$$$$ +Compound 268 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C 1.2978 -0.7382 0.0 0 +M V30 3 C 2.5956 0.0238 0.0 0 +M V30 4 C 1.2859 -2.2384 0.0 0 +M V30 5 C 3.8934 -0.7143 0.0 0 +M V30 6 C 2.5837 -2.9766 0.0 0 +M V30 7 C 3.8815 -2.2146 0.0 0 +M V30 8 C 5.1793 -2.9528 0.0 0 +M V30 9 N 6.4771 -2.1907 0.0 0 +M V30 10 N 5.1674 -4.453 0.0 0 +M V30 11 C 7.7749 -2.9289 0.0 0 +M V30 12 C 6.4652 -5.2031 0.0 0 +M V30 13 C 7.763 -4.4292 0.0 0 +M V30 14 C 9.0727 -2.1669 0.0 0 +M V30 15 O 6.4533 -6.7033 0.0 0 +M V30 16 C 9.0608 -5.1793 0.0 0 +M V30 17 C 10.3705 -2.9051 0.0 0 +M V30 18 C 10.3586 -4.4054 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 2 8 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 2 12 15 +M V30 15 1 13 16 +M V30 16 2 14 17 +M V30 17 2 16 18 +M V30 18 1 6 7 +M V30 19 1 12 13 +M V30 20 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +268 + +> +Z56974849 + +> +301.138 + +> +3.281 + +> +1 + +> +41.460 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3289109 + +> +0.89 + +$$$$ +Compound 269 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3072 -0.7301 0.0 0 +M V30 3 N -2.6145 0.0235 0.0 0 +M V30 4 N -1.319 -2.2141 0.0 0 +M V30 5 C -3.9218 -0.7066 0.0 0 +M V30 6 C -2.6263 1.531 0.0 0 +M V30 7 C -2.6263 -2.9561 0.0 0 +M V30 8 N -5.2291 0.0471 0.0 0 +M V30 9 C -3.9336 -2.1905 0.0 0 +M V30 10 C -3.3801 2.8383 0.0 0 +M V30 11 C -1.8843 2.8383 0.0 0 +M V30 12 O -2.6381 -4.44 0.0 0 +M V30 13 C -6.5364 -0.683 0.0 0 +M V30 14 C -5.2409 -2.9325 0.0 0 +M V30 15 C -6.5482 -2.167 0.0 0 +M V30 16 C -7.8555 -2.909 0.0 0 +M V30 17 O -9.1627 -2.1434 0.0 0 +M V30 18 O -7.8672 -4.3929 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 6 11 +M V30 11 2 7 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 2 13 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 7 9 +M V30 19 1 10 11 +M V30 20 1 14 15 +M V30 END BOND +M V30 END CTAB +M END +> +269 + +> +Z235361937 + +> +247.207 + +> +0.635 + +> +2 + +> +99.600 + +> +2 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1977874 + +> +0.86 + +$$$$ +Compound 270 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3039 0.7518 0.0 0 +M V30 3 N -1.3157 2.2555 0.0 0 +M V30 4 N -2.6079 0.0234 0.0 0 +M V30 5 C -2.6197 3.0073 0.0 0 +M V30 6 C -0.0352 3.0073 0.0 0 +M V30 7 C -3.9119 0.7753 0.0 0 +M V30 8 C -2.6197 -1.4567 0.0 0 +M V30 9 O -2.6314 4.511 0.0 0 +M V30 10 C -3.9236 2.279 0.0 0 +M V30 11 N -5.3451 0.3289 0.0 0 +M V30 12 N -5.3569 2.7489 0.0 0 +M V30 13 C -6.2379 1.5506 0.0 0 +M V30 14 C -5.8268 4.1821 0.0 0 +M V30 15 C -7.2952 4.4993 0.0 0 +M V30 16 C -7.7651 5.9325 0.0 0 +M V30 17 O -9.2336 6.2497 0.0 0 +M V30 18 O -6.7783 7.0485 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 5 10 +M V30 10 1 7 11 +M V30 11 1 10 12 +M V30 12 2 11 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 2 7 10 +M V30 19 1 12 13 +M V30 END BOND +M V30 END CTAB +M END +> +270 + +> +Z164670588 + +> +252.227 + +> +-0.432 + +> +1 + +> +95.740 + +> +3 + +> +Serine-protein kinase ATM + +> +CHEMBL113 + +> +0.85 + +$$$$ +Compound 271 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4491 -1.4184 0.0 0 +M V30 3 N -0.4491 -2.6241 0.0 0 +M V30 4 C 1.8676 -1.8676 0.0 0 +M V30 5 C -1.9621 -2.6122 0.0 0 +M V30 6 C 0.4255 -3.8297 0.0 0 +M V30 7 C 1.8557 -3.3569 0.0 0 +M V30 8 C 3.156 -1.0992 0.0 0 +M V30 9 C -2.7186 -3.9006 0.0 0 +M V30 10 C -2.7186 -1.3002 0.0 0 +M V30 11 O -0.0472 -5.2482 0.0 0 +M V30 12 C 3.1441 -4.1016 0.0 0 +M V30 13 C 4.4444 -1.8321 0.0 0 +M V30 14 C -4.2316 -3.8888 0.0 0 +M V30 15 C -4.2316 -1.2884 0.0 0 +M V30 16 C 4.4326 -3.3333 0.0 0 +M V30 17 N -4.9999 -5.1772 0.0 0 +M V30 18 C -4.9999 -2.5768 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 5 10 +M V30 10 2 6 11 +M V30 11 1 7 12 +M V30 12 2 8 13 +M V30 13 1 9 14 +M V30 14 2 10 15 +M V30 15 2 12 16 +M V30 16 1 14 17 +M V30 17 2 14 18 +M V30 18 1 6 7 +M V30 19 1 13 16 +M V30 20 1 15 18 +M V30 END BOND +M V30 END CTAB +M END +> +271 + +> +Z57448306 + +> +238.241 + +> +1.166 + +> +1 + +> +63.400 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL611980 + +> +0.86 + +$$$$ +Compound 272 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4489 -1.4178 0.0 0 +M V30 3 N -0.4489 -2.6229 0.0 0 +M V30 4 C 1.8668 -1.8668 0.0 0 +M V30 5 C -1.9613 -2.6111 0.0 0 +M V30 6 C 0.4253 -3.8281 0.0 0 +M V30 7 C 1.8549 -3.3555 0.0 0 +M V30 8 C 3.1546 -1.0988 0.0 0 +M V30 9 C -2.7174 -1.2996 0.0 0 +M V30 10 C -2.7174 -3.899 0.0 0 +M V30 11 O -0.0472 -5.2459 0.0 0 +M V30 12 C 3.1428 -4.0998 0.0 0 +M V30 13 C 4.4425 -1.8313 0.0 0 +M V30 14 C -4.2298 -1.2878 0.0 0 +M V30 15 C -4.2298 -3.8872 0.0 0 +M V30 16 C 4.4307 -3.3318 0.0 0 +M V30 17 C -4.9978 -2.5757 0.0 0 +M V30 18 N -6.5101 -2.5638 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 5 10 +M V30 10 2 6 11 +M V30 11 1 7 12 +M V30 12 2 8 13 +M V30 13 1 9 14 +M V30 14 2 10 15 +M V30 15 2 12 16 +M V30 16 2 14 17 +M V30 17 1 17 18 +M V30 18 1 6 7 +M V30 19 1 13 16 +M V30 20 1 15 17 +M V30 END BOND +M V30 END CTAB +M END +> +272 + +> +Z57212890 + +> +238.241 + +> +1.166 + +> +1 + +> +63.400 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL611980 + +> +0.88 + +$$$$ +Compound 273 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 O 9.1084 3.6054 0.0 0 +M V30 3 C 9.5591 2.1822 0.0 0 +M V30 4 N 8.6577 0.9725 0.0 0 +M V30 5 C 10.9822 1.7315 0.0 0 +M V30 6 C 9.5353 -0.2371 0.0 0 +M V30 7 C 7.1396 0.9843 0.0 0 +M V30 8 C 10.9704 0.2371 0.0 0 +M V30 9 C 12.275 2.4905 0.0 0 +M V30 10 O 9.0609 -1.6603 0.0 0 +M V30 11 C 6.3687 -0.3083 0.0 0 +M V30 12 C 12.2631 -0.4981 0.0 0 +M V30 13 C 13.5677 1.7434 0.0 0 +M V30 14 C 7.1159 -1.601 0.0 0 +M V30 15 C 4.8507 -0.2964 0.0 0 +M V30 16 C 13.5559 0.2609 0.0 0 +M V30 17 C 6.345 -2.8938 0.0 0 +M V30 18 C 4.0916 -1.5892 0.0 0 +M V30 19 N 4.8269 -2.8819 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 1 3 4 +M V30 3 1 3 5 +M V30 4 1 4 6 +M V30 5 1 4 7 +M V30 6 2 5 8 +M V30 7 1 5 9 +M V30 8 2 6 10 +M V30 9 1 7 11 +M V30 10 1 8 12 +M V30 11 2 9 13 +M V30 12 1 11 14 +M V30 13 1 11 15 +M V30 14 2 12 16 +M V30 15 1 14 17 +M V30 16 1 15 18 +M V30 17 1 17 19 +M V30 18 1 6 8 +M V30 19 1 13 16 +M V30 20 1 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +273 + +> +Z594038388 + +> +244.289 + +> +1.745 + +> +1 + +> +49.410 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL598088 + +> +0.9 + +$$$$ +Compound 274 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3101 -0.7435 0.0 0 +M V30 3 N -2.6202 0.0236 0.0 0 +M V30 4 C -1.3219 -2.2307 0.0 0 +M V30 5 C -3.9303 -0.7199 0.0 0 +M V30 6 C -2.632 -2.9743 0.0 0 +M V30 7 C -0.0354 -2.9743 0.0 0 +M V30 8 N -5.3703 -0.2478 0.0 0 +M V30 9 C -3.9421 -2.2071 0.0 0 +M V30 10 C -2.6438 -4.4615 0.0 0 +M V30 11 C 1.2511 -2.2071 0.0 0 +M V30 12 N -6.2673 -1.4517 0.0 0 +M V30 13 C -5.8424 1.1921 0.0 0 +M V30 14 C -5.3821 -2.6556 0.0 0 +M V30 15 C 2.5376 -2.9507 0.0 0 +M V30 16 C -5.8542 -4.072 0.0 0 +M V30 17 O 3.8241 -2.1835 0.0 0 +M V30 18 O 2.5258 -4.4379 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 8 13 +M V30 13 1 9 14 +M V30 14 1 11 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 1 15 18 +M V30 18 1 6 9 +M V30 19 2 12 14 +M V30 END BOND +M V30 END CTAB +M END +> +274 + +> +Z359302070 + +> +249.266 + +> +-0.210 + +> +2 + +> +84.220 + +> +3 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL508796 + +> +0.89 + +$$$$ +Compound 275 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.2849 0.7662 0.0 0 CFG=2 +M V30 3 C -1.3085 0.7662 0.0 0 +M V30 4 C 2.5699 0.0235 0.0 0 +M V30 5 C 1.2731 2.2752 0.0 0 +M V30 6 C -2.617 0.0235 0.0 0 +M V30 7 O 3.8548 0.7898 0.0 0 +M V30 8 O 2.5581 -1.4617 0.0 0 +M V30 9 N -2.6288 -1.4617 0.0 0 +M V30 10 N -3.9256 0.7898 0.0 0 +M V30 11 C -3.9374 -2.1926 0.0 0 +M V30 12 C -5.2341 0.0471 0.0 0 +M V30 13 C -5.2459 -1.4382 0.0 0 +M V30 14 C -3.9492 -3.678 0.0 0 +M V30 15 O -6.5427 0.8134 0.0 0 +M V30 16 C -6.5545 -2.1691 0.0 0 +M V30 17 C -5.2577 -4.4207 0.0 0 +M V30 18 C -6.5662 -3.6544 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 2 1 CFG=1 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 2 12 15 +M V30 15 1 13 16 +M V30 16 2 14 17 +M V30 17 2 16 18 +M V30 18 1 12 13 +M V30 19 1 17 18 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 2) +M V30 END COLLECTION +M V30 END CTAB +M END +> +275 + +> +Z277167244 + +> +264.300 + +> +0.545 + +> +2 + +> +78.760 + +> +4 + +> +parp15, parp1 + +> + + +> + + +$$$$ +Compound 276 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.2908 0.7697 0.0 0 +M V30 3 C -1.3145 0.7697 0.0 0 +M V30 4 C 2.5816 0.0236 0.0 0 +M V30 5 C -2.629 0.0236 0.0 0 +M V30 6 N 3.8725 0.7934 0.0 0 +M V30 7 N 2.5698 -1.4684 0.0 0 +M V30 8 C -3.9435 0.7934 0.0 0 +M V30 9 C 5.1633 0.0473 0.0 0 +M V30 10 C 3.8606 -2.2027 0.0 0 +M V30 11 O -3.9554 2.3093 0.0 0 +M V30 12 O -5.2581 0.0473 0.0 0 +M V30 13 C 5.1515 -1.4447 0.0 0 +M V30 14 C 6.4542 0.8171 0.0 0 +M V30 15 O 3.8488 -3.6948 0.0 0 +M V30 16 C 6.4423 -2.179 0.0 0 +M V30 17 C 7.745 0.071 0.0 0 +M V30 18 C 7.7332 -1.4211 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 9 14 +M V30 14 2 10 15 +M V30 15 1 13 16 +M V30 16 2 14 17 +M V30 17 2 16 18 +M V30 18 1 10 13 +M V30 19 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +276 + +> +Z54837692 + +> +264.300 + +> +0.565 + +> +2 + +> +78.760 + +> +5 + +> +parp1 + +> + + +> + + +$$$$ +Compound 277 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 N 0.0 0.0 0.0 0 +M V30 2 N -0.9009 -1.2091 0.0 0 +M V30 3 C 1.4225 -0.4504 0.0 0 +M V30 4 C -0.0237 -2.4183 0.0 0 +M V30 5 C 1.4107 -1.9441 0.0 0 +M V30 6 C 2.7147 0.32 0.0 0 +M V30 7 C -0.4978 -3.8409 0.0 0 +M V30 8 C 2.7028 -2.691 0.0 0 +M V30 9 C 4.0068 -0.4267 0.0 0 +M V30 10 N 0.3793 -5.0501 0.0 0 +M V30 11 N -1.9441 -4.2914 0.0 0 +M V30 12 C 3.995 -1.9204 0.0 0 +M V30 13 C -0.5216 -6.2592 0.0 0 +M V30 14 C -1.956 -5.785 0.0 0 +M V30 15 C -0.2252 -7.7174 0.0 0 +M V30 16 C -3.0822 -6.7808 0.0 0 +M V30 17 C -1.3514 -8.7132 0.0 0 +M V30 18 C -2.7858 -8.239 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 1 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 2 7 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 10 13 +M V30 13 1 11 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 2 16 18 +M V30 18 2 4 5 +M V30 19 1 9 12 +M V30 20 2 13 14 +M V30 21 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +277 + +> +Z234897249 + +> +234.256 + +> +3.563 + +> +2 + +> +57.360 + +> +1 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1970104 + +> +0.88 + +$$$$ +Compound 278 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4959 0.0 0 +M V30 3 N -1.3297 -2.2438 0.0 0 +M V30 4 C 1.2822 -2.2438 0.0 0 +M V30 5 C -2.6475 -1.4721 0.0 0 +M V30 6 C 2.5763 -1.4721 0.0 0 +M V30 7 C 1.2703 -3.7398 0.0 0 +M V30 8 C 3.8703 -2.2201 0.0 0 +M V30 9 C 2.5644 -4.4758 0.0 0 +M V30 10 C 3.8585 -3.716 0.0 0 +M V30 11 N 5.1526 -4.4521 0.0 0 +M V30 12 C 6.4467 -3.6923 0.0 0 +M V30 13 O 6.4348 -2.1726 0.0 0 +M V30 14 C 7.7407 -4.4284 0.0 0 +M V30 15 C 9.0348 -3.6685 0.0 0 +M V30 16 C 10.3289 -4.4046 0.0 0 +M V30 17 O 11.623 -3.6448 0.0 0 +M V30 18 O 10.3171 -5.9005 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 2 8 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 9 10 +M V30 END BOND +M V30 END CTAB +M END +> +278 + +> +Z90427257 + +> +250.251 + +> +-0.043 + +> +3 + +> +95.500 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3414756 + +> +0.89 + +$$$$ +Compound 279 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.4796 -0.142 0.0 0 +M V30 3 N 2.0833 -1.5033 0.0 0 +M V30 4 C 2.3555 1.089 0.0 0 +M V30 5 C 1.1837 -2.7106 0.0 0 +M V30 6 C 3.5629 -1.6453 0.0 0 +M V30 7 N 3.847 1.1008 0.0 0 +M V30 8 C 1.882 2.5331 0.0 0 +M V30 9 C 1.7874 -4.0719 0.0 0 CFG=2 +M V30 10 C 4.1666 -3.0066 0.0 0 +M V30 11 C 4.2968 2.5449 0.0 0 +M V30 12 C 3.0894 3.4327 0.0 0 +M V30 13 C 3.267 -4.214 0.0 0 +M V30 14 C 0.8877 -5.2793 0.0 0 +M V30 15 C 3.0776 4.9479 0.0 0 +M V30 16 O 1.4914 -6.6406 0.0 0 +M V30 17 O 1.7637 5.7054 0.0 0 +M V30 18 C 4.3678 5.7054 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 1 9 14 CFG=3 +M V30 14 1 12 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 1 15 18 +M V30 18 1 10 13 +M V30 19 2 11 12 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 9) +M V30 END COLLECTION +M V30 END CTAB +M END +> +279 + +> +Z234897573 + +> +250.294 + +> +-0.990 + +> +2 + +> +73.400 + +> +3 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 280 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 O 3.3137 -0.6627 0.0 0 +M V30 3 C 4.6037 0.0946 0.0 0 +M V30 4 N 5.8937 -0.639 0.0 0 +M V30 5 C 4.5919 1.6095 0.0 0 +M V30 6 C 5.8819 -2.1302 0.0 0 +M V30 7 C 7.1837 0.1183 0.0 0 +M V30 8 C 5.8819 2.3669 0.0 0 +M V30 9 C 3.2782 2.3669 0.0 0 +M V30 10 C 7.1719 -2.8758 0.0 0 +M V30 11 C 4.5682 -2.8758 0.0 0 +M V30 12 C 5.87 3.8818 0.0 0 +M V30 13 C 7.1719 1.6332 0.0 0 +M V30 14 C 3.2664 3.8818 0.0 0 +M V30 15 C 7.16 -4.367 0.0 0 +M V30 16 C 4.5564 -4.367 0.0 0 +M V30 17 C 4.5564 4.6392 0.0 0 +M V30 18 C 7.16 4.6392 0.0 0 +M V30 19 N 5.8464 -5.1126 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 1 3 4 +M V30 3 1 3 5 +M V30 4 1 4 6 +M V30 5 1 4 7 +M V30 6 2 5 8 +M V30 7 1 5 9 +M V30 8 1 6 10 +M V30 9 1 6 11 +M V30 10 1 8 12 +M V30 11 1 8 13 +M V30 12 2 9 14 +M V30 13 1 10 15 +M V30 14 1 11 16 +M V30 15 2 12 17 +M V30 16 1 12 18 +M V30 17 1 15 19 +M V30 18 1 14 17 +M V30 19 1 16 19 +M V30 END BOND +M V30 END CTAB +M END +> +280 + +> +Z1456278017 + +> +246.348 + +> +1.167 + +> +1 + +> +32.340 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL597890 + +> +0.9 + +$$$$ +Compound 281 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 O 5.4874 -0.6622 0.0 0 +M V30 3 C 6.7765 0.0946 0.0 0 +M V30 4 N 8.0655 -0.6386 0.0 0 +M V30 5 C 6.7646 1.6083 0.0 0 +M V30 6 C 8.0537 -2.1287 0.0 0 +M V30 7 C 9.3546 0.1182 0.0 0 +M V30 8 C 8.0537 2.3652 0.0 0 +M V30 9 C 5.4519 2.3652 0.0 0 +M V30 10 C 9.3428 -2.8738 0.0 0 +M V30 11 C 6.741 -2.8738 0.0 0 +M V30 12 C 8.0419 3.879 0.0 0 +M V30 13 C 9.3428 1.632 0.0 0 +M V30 14 C 5.4401 3.879 0.0 0 +M V30 15 C 9.331 -4.3639 0.0 0 +M V30 16 C 6.7292 -4.3639 0.0 0 +M V30 17 C 6.7292 4.6359 0.0 0 +M V30 18 C 4.1274 4.6359 0.0 0 +M V30 19 N 8.0182 -5.1089 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 1 3 4 +M V30 3 1 3 5 +M V30 4 1 4 6 +M V30 5 1 4 7 +M V30 6 2 5 8 +M V30 7 1 5 9 +M V30 8 1 6 10 +M V30 9 1 6 11 +M V30 10 1 8 12 +M V30 11 1 8 13 +M V30 12 2 9 14 +M V30 13 1 10 15 +M V30 14 1 11 16 +M V30 15 2 12 17 +M V30 16 1 14 18 +M V30 17 1 15 19 +M V30 18 1 14 17 +M V30 19 1 16 19 +M V30 END BOND +M V30 END CTAB +M END +> +281 + +> +Z1456280676 + +> +246.348 + +> +1.217 + +> +1 + +> +32.340 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL597890 + +> +0.88 + +$$$$ +Compound 282 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.3089 -0.7311 0.0 0 +M V30 3 C -1.3207 -2.2169 0.0 0 +M V30 4 C -2.6179 0.0235 0.0 0 +M V30 5 C -2.6297 -2.9599 0.0 0 +M V30 6 C -3.9269 -0.7075 0.0 0 +M V30 7 C -3.9386 -2.1934 0.0 0 +M V30 8 C -5.2476 -2.9363 0.0 0 +M V30 9 N -5.2594 -4.4221 0.0 0 +M V30 10 C -6.5684 -5.1533 0.0 0 +M V30 11 C -7.8773 -4.3986 0.0 0 +M V30 12 C -6.5802 -6.6391 0.0 0 +M V30 13 C -9.1863 -5.1297 0.0 0 +M V30 14 C -7.8891 -7.3703 0.0 0 +M V30 15 N -10.625 -4.658 0.0 0 +M V30 16 C -9.1981 -6.6155 0.0 0 +M V30 17 N -11.5212 -5.8608 0.0 0 +M V30 18 C -10.6368 -7.0637 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 1 9 10 +M V30 10 2 10 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 2 12 14 +M V30 14 1 13 15 +M V30 15 2 13 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 6 7 +M V30 19 1 14 16 +M V30 20 2 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +282 + +> +Z138543234 + +> +257.718 + +> +3.973 + +> +2 + +> +40.710 + +> +3 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1972489 + +> +0.86 + +$$$$ +Compound 283 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.2961 -0.7491 0.0 0 +M V30 3 C 2.5923 0.0118 0.0 0 +M V30 4 C 1.2843 -2.2475 0.0 0 +M V30 5 C 3.8885 -0.7372 0.0 0 +M V30 6 C 2.5805 -2.9967 0.0 0 +M V30 7 C 3.8767 -2.2356 0.0 0 +M V30 8 C 5.1728 -2.9848 0.0 0 +M V30 9 O 5.161 -4.4831 0.0 0 +M V30 10 N 6.469 -2.2118 0.0 0 +M V30 11 C 7.7652 -2.961 0.0 0 +M V30 12 C 9.0614 -2.188 0.0 0 +M V30 13 C 10.3576 -2.9372 0.0 0 +M V30 14 C 11.6538 -2.1642 0.0 0 +M V30 15 C 10.3457 -4.4356 0.0 0 +M V30 16 C 12.95 -2.9134 0.0 0 +M V30 17 C 11.6419 -5.1728 0.0 0 +M V30 18 C 12.9381 -4.4118 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 2 8 9 +M V30 9 1 8 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 2 16 18 +M V30 18 1 6 7 +M V30 19 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +283 + +> +Z27761589 + +> +243.276 + +> +3.303 + +> +1 + +> +29.100 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3629672 + +> +0.85 + +$$$$ +Compound 284 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.8944 1.224 0.0 0 +M V30 3 C 1.4123 0.4707 0.0 0 +M V30 4 N -0.0235 2.4481 0.0 0 +M V30 5 N -2.401 1.2358 0.0 0 +M V30 6 C 1.4005 1.9773 0.0 0 +M V30 7 C 2.6952 -0.2707 0.0 0 +M V30 8 C -3.1542 -0.047 0.0 0 +M V30 9 C 2.6834 2.7423 0.0 0 +M V30 10 C 3.9781 0.4943 0.0 0 +M V30 11 C -4.6608 -0.0353 0.0 0 +M V30 12 C 3.9664 2.0126 0.0 0 +M V30 13 C -5.4258 -1.3182 0.0 0 +M V30 14 C -5.4258 1.2711 0.0 0 +M V30 15 C -6.9323 -1.3064 0.0 0 +M V30 16 C -6.9323 1.2829 0.0 0 +M V30 17 C -7.6974 0.0 0.0 0 +M V30 18 C -7.6974 -2.5893 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 13 15 +M V30 15 2 14 16 +M V30 16 2 15 17 +M V30 17 1 15 18 +M V30 18 1 4 6 +M V30 19 1 10 12 +M V30 20 1 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +284 + +> +Z304821554 + +> +254.350 + +> +4.574 + +> +1 + +> +24.920 + +> +3 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1983534 + +> +0.86 + +$$$$ +Compound 285 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.4161 -0.4484 0.0 0 +M V30 3 C -0.8969 -1.2037 0.0 0 +M V30 4 C 2.7025 0.3068 0.0 0 +M V30 5 C 1.4043 -1.9354 0.0 0 +M V30 6 C -0.0236 -2.4074 0.0 0 +M V30 7 O 2.6907 1.8174 0.0 0 +M V30 8 N 3.9888 -0.4248 0.0 0 +M V30 9 N 2.6907 -2.6671 0.0 0 +M V30 10 C 1.3807 2.5727 0.0 0 +M V30 11 C 3.977 -1.9118 0.0 0 +M V30 12 C 1.3689 4.0832 0.0 0 +M V30 13 C 0.0708 1.841 0.0 0 +M V30 14 C 0.059 4.8503 0.0 0 +M V30 15 C -1.2391 2.5963 0.0 0 +M V30 16 C -1.2509 4.1068 0.0 0 +M V30 17 C -2.5609 4.8739 0.0 0 +M V30 18 N -3.8708 4.1304 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 2 10 12 +M V30 12 1 10 13 +M V30 13 1 12 14 +M V30 14 2 13 15 +M V30 15 2 14 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 1 5 6 +M V30 19 1 9 11 +M V30 20 1 15 16 +M V30 END BOND +M V30 END CTAB +M END +> +285 + +> +Z851012766 + +> +257.311 + +> +2.146 + +> +1 + +> +61.030 + +> +3 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 286 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3093 0.7549 0.0 0 +M V30 3 N -1.3211 2.2647 0.0 0 +M V30 4 C -2.6186 0.0235 0.0 0 +M V30 5 C -0.0353 3.0314 0.0 0 +M V30 6 C -3.9279 0.7785 0.0 0 +M V30 7 C -2.6304 -1.4626 0.0 0 +M V30 8 C -0.0471 4.5413 0.0 0 +M V30 9 C 1.2503 2.2883 0.0 0 +M V30 10 C -5.2372 0.0471 0.0 0 +M V30 11 C -3.9397 -2.1939 0.0 0 +M V30 12 C 1.2385 5.2962 0.0 0 +M V30 13 C 2.536 3.055 0.0 0 +M V30 14 N -6.6763 0.519 0.0 0 +M V30 15 C -5.249 -1.439 0.0 0 +M V30 16 C 2.5242 4.5648 0.0 0 +M V30 17 N -7.5727 -0.6841 0.0 0 +M V30 18 C -6.688 -1.8872 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 10 14 +M V30 14 2 10 15 +M V30 15 2 12 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 11 15 +M V30 19 1 13 16 +M V30 20 2 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +286 + +> +Z241176844 + +> +237.257 + +> +2.784 + +> +2 + +> +57.780 + +> +2 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1973808 + +> +0.87 + +$$$$ +Compound 287 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2884 -0.7328 0.0 0 +M V30 3 O 2.5768 0.0236 0.0 0 +M V30 4 C 1.2766 -2.2222 0.0 0 +M V30 5 C -0.0354 -2.9551 0.0 0 +M V30 6 C 2.565 -2.9551 0.0 0 +M V30 7 N -1.4775 -2.4823 0.0 0 +M V30 8 C -0.0472 -4.4445 0.0 0 +M V30 9 C 2.5532 -4.4445 0.0 0 +M V30 10 C -2.3759 -3.688 0.0 0 +M V30 11 N -1.4893 -4.8937 0.0 0 +M V30 12 C 1.2411 -5.1774 0.0 0 +M V30 13 C -3.8889 -3.6762 0.0 0 +M V30 14 C -4.6454 -4.9646 0.0 0 +M V30 15 C -4.6454 -2.3641 0.0 0 +M V30 16 C -6.1585 -4.9528 0.0 0 +M V30 17 C -6.1585 -2.3523 0.0 0 +M V30 18 C -6.915 -3.6407 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 8 12 +M V30 12 1 10 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 2 16 18 +M V30 18 1 9 12 +M V30 19 1 10 11 +M V30 20 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +287 + +> +Z1065585448 + +> +238.241 + +> +3.731 + +> +2 + +> +65.980 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL336859 + +> +0.92 + +$$$$ +Compound 288 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 O 10.1084 3.3065 0.0 0 +M V30 3 C 11.3956 2.5625 0.0 0 +M V30 4 O 12.6828 3.3183 0.0 0 +M V30 5 C 11.3838 1.0746 0.0 0 +M V30 6 C 10.073 0.3424 0.0 0 +M V30 7 C 12.671 0.3424 0.0 0 +M V30 8 N 8.6323 0.8148 0.0 0 +M V30 9 C 10.0612 -1.1454 0.0 0 +M V30 10 C 12.6592 -1.1454 0.0 0 +M V30 11 C 7.7348 -0.3896 0.0 0 +M V30 12 N 8.6205 -1.5942 0.0 0 +M V30 13 C 11.3484 -1.8894 0.0 0 +M V30 14 C 6.2233 -0.3778 0.0 0 +M V30 15 C 5.4557 0.9329 0.0 0 +M V30 16 C 5.4557 -1.665 0.0 0 +M V30 17 N 3.9442 0.9447 0.0 0 +M V30 18 C 3.9442 -1.6532 0.0 0 +M V30 19 C 3.1884 -0.3424 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 1 3 4 +M V30 3 1 3 5 +M V30 4 2 5 6 +M V30 5 1 5 7 +M V30 6 1 6 8 +M V30 7 1 6 9 +M V30 8 2 7 10 +M V30 9 2 8 11 +M V30 10 1 9 12 +M V30 11 2 9 13 +M V30 12 1 11 14 +M V30 13 2 14 15 +M V30 14 1 14 16 +M V30 15 1 15 17 +M V30 16 2 16 18 +M V30 17 2 17 19 +M V30 18 1 10 13 +M V30 19 1 11 12 +M V30 20 1 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +288 + +> +Z2895972210 + +> +239.229 + +> +2.325 + +> +2 + +> +78.870 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL336859 + +> +0.85 + +$$$$ +Compound 289 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 Cl 3.5816 0.0942 0.0 0 +M V30 3 O 0.2827 -9.6138 0.0 0 +M V30 4 C 0.2709 -8.1057 0.0 0 +M V30 5 N -1.0367 -7.3517 0.0 0 +M V30 6 C 1.5551 -7.3517 0.0 0 +M V30 7 C -1.0485 -5.8437 0.0 0 +M V30 8 C 1.5433 -5.8437 0.0 0 +M V30 9 C 2.7097 -8.2707 0.0 0 +M V30 10 N 0.2356 -5.0896 0.0 0 +M V30 11 N -2.3563 -5.0896 0.0 0 +M V30 12 C 2.698 -4.9011 0.0 0 +M V30 13 C 4.1589 -7.929 0.0 0 +M V30 14 C -3.664 -5.8201 0.0 0 +M V30 15 C -2.3681 -3.5816 0.0 0 +M V30 16 C 4.1471 -5.2192 0.0 0 +M V30 17 C 4.7951 -6.5741 0.0 0 +M V30 18 C -4.9718 -5.0661 0.0 0 +M V30 19 C -3.6758 -2.8275 0.0 0 +M V30 20 N -4.9836 -3.558 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 3 4 +M V30 2 1 4 5 +M V30 3 1 4 6 +M V30 4 1 5 7 +M V30 5 2 6 8 +M V30 6 1 6 9 +M V30 7 2 7 10 +M V30 8 1 7 11 +M V30 9 1 8 12 +M V30 10 1 9 13 +M V30 11 1 11 14 +M V30 12 1 11 15 +M V30 13 1 12 16 +M V30 14 1 13 17 +M V30 15 1 14 18 +M V30 16 1 15 19 +M V30 17 1 18 20 +M V30 18 1 8 10 +M V30 19 1 16 17 +M V30 20 1 19 20 +M V30 END BOND +M V30 END CTAB +M END +> +289 + +> +Z2961198423 + +> +248.324 + +> +0.940 + +> +2 + +> +56.730 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3593716 + +> +0.98 + +$$$$ +Compound 290 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.4928 0.0118 0.0 0 +M V30 3 C -0.7701 -1.2914 0.0 0 +M V30 4 C 2.2393 -1.2796 0.0 0 +M V30 5 C 2.2393 1.3269 0.0 0 +M V30 6 N 1.4691 -2.571 0.0 0 +M V30 7 C 3.7321 -1.2677 0.0 0 +M V30 8 C 3.7321 1.3388 0.0 0 +M V30 9 C 4.4667 0.0473 0.0 0 +M V30 10 C 5.9596 0.0592 0.0 0 +M V30 11 N 6.8363 1.2914 0.0 0 +M V30 12 N 6.8363 -1.1492 0.0 0 +M V30 13 C 8.2581 0.8412 0.0 0 +M V30 14 C 8.2581 -0.6753 0.0 0 +M V30 15 C 9.5496 1.6113 0.0 0 +M V30 16 C 9.5496 -1.4217 0.0 0 +M V30 17 C 10.841 0.8767 0.0 0 +M V30 18 C 10.841 -0.6516 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 9 10 +M V30 10 2 10 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 2 16 18 +M V30 18 1 8 9 +M V30 19 2 13 14 +M V30 20 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +290 + +> +Z1154375493 + +> +239.273 + +> +2.868 + +> +2 + +> +63.930 + +> +2 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL179383 + +> +0.85 + +$$$$ +Compound 291 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 O 9.1084 3.6054 0.0 0 +M V30 3 C 9.5591 2.1822 0.0 0 +M V30 4 N 8.6577 0.9725 0.0 0 +M V30 5 C 10.9822 1.7315 0.0 0 +M V30 6 C 9.5353 -0.2371 0.0 0 +M V30 7 C 7.1396 0.9843 0.0 0 +M V30 8 C 10.9704 0.2371 0.0 0 +M V30 9 C 12.275 2.4905 0.0 0 +M V30 10 O 9.0609 -1.6603 0.0 0 +M V30 11 C 6.3687 -0.3083 0.0 0 CFG=2 +M V30 12 C 12.2631 -0.4981 0.0 0 +M V30 13 C 13.5677 1.7434 0.0 0 +M V30 14 O 4.8507 -0.2964 0.0 0 +M V30 15 C 7.1159 -1.601 0.0 0 +M V30 16 C 13.5559 0.2609 0.0 0 +M V30 17 C 4.0916 -1.5892 0.0 0 +M V30 18 N 6.345 -2.8938 0.0 0 +M V30 19 C 4.8269 -2.8819 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 1 3 4 +M V30 3 1 3 5 +M V30 4 1 4 6 +M V30 5 1 4 7 +M V30 6 2 5 8 +M V30 7 1 5 9 +M V30 8 2 6 10 +M V30 9 1 11 7 CFG=1 +M V30 10 1 8 12 +M V30 11 2 9 13 +M V30 12 1 11 14 +M V30 13 1 11 15 +M V30 14 2 12 16 +M V30 15 1 14 17 +M V30 16 1 15 18 +M V30 17 1 17 19 +M V30 18 1 6 8 +M V30 19 1 13 16 +M V30 20 1 18 19 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 11) +M V30 END COLLECTION +M V30 END CTAB +M END +> +291 + +> +Z1463350069 + +> +246.262 + +> +0.822 + +> +1 + +> +58.640 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3605976 + +> +0.88 + +$$$$ +Compound 292 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3136 -0.7337 0.0 0 +M V30 3 N -2.6273 0.0236 0.0 0 +M V30 4 C -1.3254 -2.2249 0.0 0 +M V30 5 C -2.6391 1.5385 0.0 0 +M V30 6 N -2.5562 -3.1006 0.0 0 +M V30 7 C -0.1183 -3.1006 0.0 0 +M V30 8 C -2.6509 3.0533 0.0 0 +M V30 9 C -4.1539 1.5503 0.0 0 +M V30 10 C -1.1479 1.5503 0.0 0 +M V30 11 N -2.1065 -4.5208 0.0 0 +M V30 12 C -0.5917 -4.5208 0.0 0 +M V30 13 C 0.284 -5.7279 0.0 0 +M V30 14 C 1.7633 -5.5623 0.0 0 +M V30 15 C -0.3432 -7.0889 0.0 0 +M V30 16 C 2.6391 -6.7694 0.0 0 +M V30 17 C 0.5325 -8.2961 0.0 0 +M V30 18 C 2.0118 -8.1304 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 1 5 10 +M V30 10 1 6 11 +M V30 11 2 7 12 +M V30 12 1 12 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 2 16 18 +M V30 18 1 11 12 +M V30 19 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +292 + +> +Z183133284 + +> +243.304 + +> +3.163 + +> +2 + +> +57.780 + +> +3 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1995916 + +> +0.91 + +$$$$ +Compound 293 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2878 -0.7443 0.0 0 +M V30 3 N 1.276 -2.233 0.0 0 +M V30 4 C 2.5757 0.0236 0.0 0 +M V30 5 C -0.0354 -2.9656 0.0 0 +M V30 6 C 2.5639 1.5359 0.0 0 +M V30 7 C 3.8635 -0.7207 0.0 0 +M V30 8 C -1.3469 -2.2094 0.0 0 +M V30 9 C -0.0472 -4.4543 0.0 0 +M V30 10 C 3.8517 2.2921 0.0 0 +M V30 11 C 5.1514 0.0472 0.0 0 +M V30 12 C -2.6584 -2.9419 0.0 0 +M V30 13 C -1.3587 -5.1868 0.0 0 +M V30 14 C 5.1396 1.5596 0.0 0 +M V30 15 C -2.6702 -4.4307 0.0 0 +M V30 16 C -4.0998 -2.4693 0.0 0 +M V30 17 N -4.1117 -4.8796 0.0 0 +M V30 18 N -4.9978 -3.6745 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 2 10 14 +M V30 14 2 12 15 +M V30 15 1 12 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 11 14 +M V30 19 1 13 15 +M V30 20 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +293 + +> +Z56889286 + +> +237.257 + +> +2.719 + +> +2 + +> +57.780 + +> +2 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1973808 + +> +0.92 + +$$$$ +Compound 294 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.2951 -0.7485 0.0 0 +M V30 3 C 2.5902 0.0118 0.0 0 +M V30 4 C 1.2832 -2.2456 0.0 0 +M V30 5 C 3.8853 -0.7366 0.0 0 +M V30 6 C 2.5783 -2.9942 0.0 0 +M V30 7 C 3.8734 -2.2337 0.0 0 +M V30 8 C 5.1685 -2.9823 0.0 0 +M V30 9 C 6.4637 -2.21 0.0 0 +M V30 10 N 7.7588 -2.9585 0.0 0 +M V30 11 C 9.0539 -2.1862 0.0 0 +M V30 12 O 9.042 -0.6653 0.0 0 +M V30 13 C 10.349 -2.9348 0.0 0 +M V30 14 C 11.6441 -2.1624 0.0 0 +M V30 15 C 10.3371 -4.4319 0.0 0 +M V30 16 C 12.9392 -2.911 0.0 0 +M V30 17 C 11.6322 -5.1685 0.0 0 +M V30 18 C 12.9274 -4.4081 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 1 9 10 +M V30 10 1 10 11 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 2 16 18 +M V30 18 1 6 7 +M V30 19 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +294 + +> +Z26769912 + +> +243.276 + +> +3.101 + +> +1 + +> +29.100 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3629672 + +> +0.85 + +$$$$ +Compound 295 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.314 -0.7339 0.0 0 +M V30 3 N -2.6281 0.0236 0.0 0 +M V30 4 C -1.3258 -2.2256 0.0 0 +M V30 5 C -2.6399 1.5389 0.0 0 +M V30 6 C -3.9421 -0.7102 0.0 0 +M V30 7 N -2.557 -3.1016 0.0 0 +M V30 8 C -0.1183 -3.1016 0.0 0 +M V30 9 C -3.954 2.2966 0.0 0 +M V30 10 C -3.954 -2.2019 0.0 0 +M V30 11 N -2.1072 -4.5222 0.0 0 +M V30 12 C -0.5919 -4.5222 0.0 0 +M V30 13 C 0.2841 -5.7297 0.0 0 +M V30 14 C 1.7639 -5.564 0.0 0 +M V30 15 C -0.3433 -7.0911 0.0 0 +M V30 16 C 2.6399 -6.7715 0.0 0 +M V30 17 C 0.5327 -8.2986 0.0 0 +M V30 18 C 2.0125 -8.1329 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 12 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 2 16 18 +M V30 18 1 11 12 +M V30 19 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +295 + +> +Z251650600 + +> +243.304 + +> +2.499 + +> +1 + +> +48.990 + +> +4 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1995916 + +> +0.9 + +$$$$ +Compound 296 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4502 -1.4219 0.0 0 +M V30 3 N -0.4502 -2.6305 0.0 0 +M V30 4 C 1.8722 -1.8722 0.0 0 +M V30 5 C 0.4265 -3.8392 0.0 0 +M V30 6 C -1.967 -2.6187 0.0 0 +M V30 7 C 1.8603 -3.3652 0.0 0 +M V30 8 C 3.1638 -1.102 0.0 0 +M V30 9 O -0.0473 -5.2611 0.0 0 +M V30 10 C -2.7253 -3.9103 0.0 0 CFG=2 +M V30 11 C 3.1519 -4.1117 0.0 0 +M V30 12 C 4.4554 -1.8366 0.0 0 +M V30 13 O -4.2421 -3.8984 0.0 0 +M V30 14 C -1.9907 -5.2019 0.0 0 +M V30 15 C 4.4435 -3.3415 0.0 0 +M V30 16 C -5.0123 -5.19 0.0 0 +M V30 17 N -2.749 -6.4935 0.0 0 +M V30 18 C -4.2658 -6.4816 0.0 0 +M V30 19 C -2.0144 -7.7851 0.0 0 +M V30 20 C -2.7727 -9.0767 0.0 0 +M V30 21 C -2.0381 -10.3683 0.0 0 +M V30 22 C -4.2895 -9.0648 0.0 0 +M V30 23 C -2.7964 -11.6599 0.0 0 +M V30 24 C -5.0597 -10.3564 0.0 0 +M V30 25 C -4.3132 -11.648 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 10 6 CFG=1 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 10 13 +M V30 13 1 10 14 +M V30 14 2 11 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 2 20 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 2 22 24 +M V30 24 2 23 25 +M V30 25 1 5 7 +M V30 26 1 12 15 +M V30 27 1 17 18 +M V30 28 1 24 25 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 10) +M V30 END COLLECTION +M V30 END CTAB +M END +> +296 + +> +Z18271678 + +> +336.384 + +> +3.119 + +> +0 + +> +49.850 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3605976 + +> +0.87 + +$$$$ +Compound 297 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4872 0.0 0 +M V30 3 N -1.3219 -2.2308 0.0 0 +M V30 4 C 1.2747 -2.2308 0.0 0 +M V30 5 C -2.6321 -1.4636 0.0 0 +M V30 6 C 2.5613 -1.4636 0.0 0 +M V30 7 C 1.2629 -3.718 0.0 0 +M V30 8 C -3.9422 -2.2072 0.0 0 +M V30 9 C 3.8478 -2.2072 0.0 0 +M V30 10 C 2.5495 -4.4498 0.0 0 +M V30 11 C -5.2524 -1.4399 0.0 0 +M V30 12 C 3.836 -3.6944 0.0 0 +M V30 13 C -6.5626 -2.1836 0.0 0 +M V30 14 C -5.2642 0.0708 0.0 0 +M V30 15 C -7.8727 -1.4163 0.0 0 +M V30 16 C -6.5744 -3.6708 0.0 0 +M V30 17 C -6.5744 0.8262 0.0 0 +M V30 18 C -7.8845 0.0944 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 13 15 +M V30 15 1 13 16 +M V30 16 2 14 17 +M V30 17 2 15 18 +M V30 18 1 10 12 +M V30 19 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +297 + +> +Z52314223 + +> +239.312 + +> +3.407 + +> +1 + +> +29.100 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1688212 + +> +0.91 + +$$$$ +Compound 298 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.5117 0.0 0 +M V30 3 N -1.3227 2.2675 0.0 0 +M V30 4 C 1.2754 2.2675 0.0 0 +M V30 5 N -1.3345 3.7792 0.0 0 +M V30 6 C 1.2636 3.7792 0.0 0 +M V30 7 C 2.5628 1.5235 0.0 0 +M V30 8 C -0.0472 4.5351 0.0 0 +M V30 9 C 2.5509 4.5351 0.0 0 +M V30 10 C 3.8501 2.2793 0.0 0 +M V30 11 C -0.059 6.0468 0.0 0 +M V30 12 C 3.8383 3.8028 0.0 0 +M V30 13 C 1.2282 6.8026 0.0 0 +M V30 14 C 1.2164 8.3143 0.0 0 +M V30 15 C 2.5155 6.0704 0.0 0 +M V30 16 C 2.5037 9.0702 0.0 0 +M V30 17 C 3.8028 6.8262 0.0 0 +M V30 18 N 3.791 8.3379 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 11 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 2 16 18 +M V30 18 1 6 8 +M V30 19 1 10 12 +M V30 20 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +298 + +> +Z56759694 + +> +237.257 + +> +0.725 + +> +1 + +> +54.350 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL66761 + +> +0.89 + +$$$$ +Compound 299 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O -0.7672 1.3101 0.0 0 +M V30 3 O 0.7318 -1.2865 0.0 0 +M V30 4 N 1.2865 0.7672 0.0 0 +M V30 5 C -1.3101 -0.7318 0.0 0 +M V30 6 C 1.4282 2.2662 0.0 0 +M V30 7 C 2.6439 0.1652 0.0 0 +M V30 8 C -2.6203 0.0236 0.0 0 +M V30 9 C -1.3219 -2.219 0.0 0 +M V30 10 C 2.88 2.5849 0.0 0 CFG=2 +M V30 11 C 3.6354 1.2865 0.0 0 +M V30 12 C -3.9305 -0.7082 0.0 0 +M V30 13 C -2.6321 -2.9508 0.0 0 +M V30 14 N 3.482 3.9659 0.0 0 +M V30 15 C -3.9423 -2.1954 0.0 0 +M V30 16 C -5.3706 -0.236 0.0 0 +M V30 17 O -5.3824 -2.6439 0.0 0 +M V30 18 C -6.2676 -1.44 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 10 14 CFG=1 +M V30 14 2 12 15 +M V30 15 1 12 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 10 11 +M V30 19 1 13 15 +M V30 20 1 17 18 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STEABS ATOMS=(1 10) +M V30 END COLLECTION +M V30 END CTAB +M END +> +299 + +> +Z1480871240 + +> +268.332 + +> +1.123 + +> +1 + +> +72.630 + +> +1 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 300 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C 1.28 0.7622 0.0 0 +M V30 3 C 1.2656 2.2723 0.0 0 +M V30 4 C 2.56 0.0287 0.0 0 +M V30 5 C -0.0431 3.0346 0.0 0 +M V30 6 C 2.5456 3.0346 0.0 0 +M V30 7 C 3.84 0.791 0.0 0 +M V30 8 O -0.0575 4.5447 0.0 0 +M V30 9 N -1.3519 2.3011 0.0 0 +M V30 10 C 3.8256 2.3011 0.0 0 +M V30 11 F 5.12 0.0575 0.0 0 +M V30 12 C -2.6607 3.0634 0.0 0 +M V30 13 C -3.9695 2.3299 0.0 0 +M V30 14 N -5.2782 3.0921 0.0 0 +M V30 15 C -6.587 2.3586 0.0 0 +M V30 16 C -7.8958 3.1209 0.0 0 +M V30 17 O -9.2046 2.3874 0.0 0 +M V30 18 C -10.5134 3.1497 0.0 0 +M V30 19 Cl -14.7274 2.1861 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 7 11 +M V30 11 1 9 12 +M V30 12 1 12 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 1 7 10 +M V30 END BOND +M V30 END CTAB +M END +> +300 + +> +Z1561246235 + +> +319.170 + +> +1.327 + +> +2 + +> +50.360 + +> +7 + +> +ATM + +> + + +> + + +$$$$ +Compound 301 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.5113 0.0 0 +M V30 3 N -1.3223 2.2787 0.0 0 +M V30 4 C 1.2751 2.2787 0.0 0 +M V30 5 C -1.3341 3.79 0.0 0 +M V30 6 C 1.2633 3.79 0.0 0 +M V30 7 C 2.5621 1.5349 0.0 0 +M V30 8 N -0.0472 4.5575 0.0 0 +M V30 9 C -2.6447 4.5575 0.0 0 +M V30 10 C 2.5503 4.5575 0.0 0 +M V30 11 C 3.849 2.3023 0.0 0 +M V30 12 C 3.8372 3.8136 0.0 0 +M V30 13 C 5.136 1.5585 0.0 0 +M V30 14 C 6.423 2.3259 0.0 0 +M V30 15 C 5.1242 0.0708 0.0 0 +M V30 16 C 7.7099 1.5821 0.0 0 +M V30 17 C 6.4112 -0.673 0.0 0 +M V30 18 C 7.6981 0.0944 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 10 12 +M V30 12 1 11 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 2 16 18 +M V30 18 1 6 8 +M V30 19 1 11 12 +M V30 20 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +301 + +> +Z967655824 + +> +236.269 + +> +2.692 + +> +1 + +> +41.460 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL395092 + +> +0.91 + +$$$$ +Compound 302 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5092 0.0 0 +M V30 3 N -1.3205 2.2638 0.0 0 +M V30 4 C 1.2734 2.2638 0.0 0 +M V30 5 C -2.6293 1.5328 0.0 0 CFG=2 +M V30 6 C 1.2616 3.7731 0.0 0 +M V30 7 C 2.5586 1.5328 0.0 0 +M V30 8 C -3.9381 2.2874 0.0 0 +M V30 9 C -2.6411 0.0471 0.0 0 +M V30 10 C 2.5468 4.5277 0.0 0 +M V30 11 C 3.8438 2.2874 0.0 0 +M V30 12 C -5.2469 1.5564 0.0 0 +M V30 13 C -3.9499 3.7966 0.0 0 +M V30 14 O -3.9499 -0.6838 0.0 0 +M V30 15 N 3.832 3.7966 0.0 0 +M V30 16 C -6.5557 2.311 0.0 0 +M V30 17 C -5.2587 4.5513 0.0 0 +M V30 18 C -6.5675 3.8202 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 5 3 CFG=1 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 1 9 14 +M V30 14 2 10 15 +M V30 15 1 12 16 +M V30 16 2 13 17 +M V30 17 2 16 18 +M V30 18 1 11 15 +M V30 19 1 17 18 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STEABS ATOMS=(1 5) +M V30 END COLLECTION +M V30 END CTAB +M END +> +302 + +> +Z1430634745 + +> +242.273 + +> +1.191 + +> +2 + +> +62.220 + +> +4 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1974288 + +> +0.9 + +$$$$ +Compound 303 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.7592 -1.293 0.0 0 +M V30 3 N -2.2776 -1.2811 0.0 0 +M V30 4 C -0.0118 -2.586 0.0 0 +M V30 5 C -3.0368 0.0355 0.0 0 +M V30 6 C -0.771 -3.879 0.0 0 +M V30 7 C 1.4828 -2.5741 0.0 0 +M V30 8 N -2.2894 -3.8672 0.0 0 +M V30 9 C -0.0237 -5.1721 0.0 0 +M V30 10 C 2.2183 -3.8672 0.0 0 +M V30 11 C -3.0487 -5.1602 0.0 0 +M V30 12 C 1.4709 -5.1602 0.0 0 +M V30 13 C -0.7829 -6.4651 0.0 0 +M V30 14 C 3.713 -3.8553 0.0 0 +M V30 15 C -2.3132 -6.4532 0.0 0 +M V30 16 C 2.2064 -6.4532 0.0 0 +M V30 17 C 4.4603 -5.1483 0.0 0 +M V30 18 C 3.7011 -6.4414 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 4 7 +M V30 7 2 6 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 1 9 12 +M V30 12 2 9 13 +M V30 13 2 10 14 +M V30 14 2 11 15 +M V30 15 2 12 16 +M V30 16 1 14 17 +M V30 17 1 16 18 +M V30 18 1 10 12 +M V30 19 1 13 15 +M V30 20 2 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +303 + +> +Z441661418 + +> +236.269 + +> +2.480 + +> +1 + +> +41.990 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL502330 + +> +0.88 + +$$$$ +Compound 304 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 S 5.3863 -0.4724 0.0 0 +M V30 3 O 6.8746 -0.4606 0.0 0 +M V30 4 O 3.8743 -0.4606 0.0 0 +M V30 5 N 5.3745 -1.9608 0.0 0 +M V30 6 C 5.3745 1.0394 0.0 0 +M V30 7 C 6.5793 -2.8349 0.0 0 +M V30 8 C 4.146 -2.8349 0.0 0 +M V30 9 C 6.662 1.7954 0.0 0 +M V30 10 C 4.0633 1.7954 0.0 0 +M V30 11 C 6.1068 -4.2523 0.0 0 CFG=2 +M V30 12 C 4.5949 -4.2523 0.0 0 +M V30 13 C 6.6502 3.3074 0.0 0 +M V30 14 C 7.9495 1.063 0.0 0 +M V30 15 C 4.0515 3.3074 0.0 0 +M V30 16 N 6.9809 -5.4572 0.0 0 +M V30 17 C 7.9377 4.0752 0.0 0 +M V30 18 C 5.3391 4.0752 0.0 0 +M V30 19 C 9.2371 1.819 0.0 0 +M V30 20 N 9.2253 3.331 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 2 2 4 +M V30 3 1 2 5 +M V30 4 1 2 6 +M V30 5 1 5 7 +M V30 6 1 5 8 +M V30 7 1 6 9 +M V30 8 2 6 10 +M V30 9 1 7 11 +M V30 10 1 8 12 +M V30 11 1 9 13 +M V30 12 2 9 14 +M V30 13 1 10 15 +M V30 14 1 11 16 CFG=1 +M V30 15 2 13 17 +M V30 16 1 13 18 +M V30 17 1 14 19 +M V30 18 1 17 20 +M V30 19 1 11 12 +M V30 20 2 15 18 +M V30 21 2 19 20 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 11) +M V30 END COLLECTION +M V30 END CTAB +M END +> +304 + +> +Z1491270667 + +> +277.342 + +> +0.608 + +> +1 + +> +76.290 + +> +1 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL38380 + +> +0.91 + +$$$$ +Compound 305 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.4152 0.4717 0.0 0 +M V30 3 C -0.8963 1.2265 0.0 0 +M V30 4 N 2.7008 -0.2594 0.0 0 +M V30 5 C 1.4034 1.9813 0.0 0 +M V30 6 C -0.0235 2.4531 0.0 0 +M V30 7 C 3.9863 0.4953 0.0 0 +M V30 8 C 2.689 2.7361 0.0 0 +M V30 9 C -0.4953 3.8919 0.0 0 +M V30 10 N 3.9745 2.0049 0.0 0 +M V30 11 O 2.6772 4.2458 0.0 0 +M V30 12 S 0.3774 5.1185 0.0 0 +M V30 13 C -1.9342 4.3637 0.0 0 +M V30 14 C 5.26 2.7597 0.0 0 +M V30 15 C -0.5189 6.3451 0.0 0 +M V30 16 C -1.9459 5.8733 0.0 0 +M V30 17 C 6.5456 2.0285 0.0 0 +M V30 18 O 6.5338 0.5425 0.0 0 +M V30 19 O 7.8311 2.7833 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 9 12 +M V30 12 2 9 13 +M V30 13 1 10 14 +M V30 14 1 12 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 5 6 +M V30 20 1 8 10 +M V30 21 2 15 16 +M V30 END BOND +M V30 END CTAB +M END +> +305 + +> +Z85929777 + +> +292.334 + +> +2.040 + +> +1 + +> +69.970 + +> +3 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL559696 + +> +0.85 + +$$$$ +Compound 306 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O 0.734 -1.2904 0.0 0 +M V30 3 O -0.7577 1.3141 0.0 0 +M V30 4 N -1.3141 -0.7458 0.0 0 +M V30 5 C 1.2904 0.7577 0.0 0 +M V30 6 C -1.326 -2.2376 0.0 0 +M V30 7 C 2.5809 0.0118 0.0 0 +M V30 8 C 1.2786 2.2731 0.0 0 +M V30 9 C -2.6401 -2.9716 0.0 0 +M V30 10 C 3.8714 0.7695 0.0 0 +M V30 11 C 2.5691 3.0308 0.0 0 +M V30 12 O -3.9543 -2.2139 0.0 0 +M V30 13 O -2.652 -4.4634 0.0 0 +M V30 14 N 5.1619 0.0236 0.0 0 +M V30 15 C 3.8596 2.2849 0.0 0 +M V30 16 C 6.4524 0.7813 0.0 0 +M V30 17 S 5.15 3.0426 0.0 0 +M V30 18 O 7.7428 0.0355 0.0 0 +M V30 19 C 6.4405 2.2968 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 2 10 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 1 11 15 +M V30 20 1 17 19 +M V30 END BOND +M V30 END CTAB +M END +> +306 + +> +Z56347480 + +> +302.327 + +> +0.166 + +> +3 + +> +112.570 + +> +3 + +> +parp1 + +> + + +> + + +$$$$ +Compound 307 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.5128 0.0 0 +M V30 3 N 1.2764 2.281 0.0 0 +M V30 4 C -1.3237 2.281 0.0 0 +M V30 5 C 2.5647 1.5364 0.0 0 +M V30 6 C -1.3355 3.7938 0.0 0 +M V30 7 C -2.6356 1.5364 0.0 0 +M V30 8 C 3.8529 2.3046 0.0 0 +M V30 9 N -0.0472 4.5502 0.0 0 +M V30 10 C -2.6474 4.5502 0.0 0 +M V30 11 C -3.9475 2.3046 0.0 0 +M V30 12 C -2.6474 0.0472 0.0 0 +M V30 13 C 5.1412 1.5601 0.0 0 +M V30 14 C -3.9593 3.8056 0.0 0 +M V30 15 C 6.4295 2.3283 0.0 0 +M V30 16 C 5.1294 0.0709 0.0 0 +M V30 17 C 7.7177 1.5837 0.0 0 +M V30 18 C 6.4176 -0.6618 0.0 0 +M V30 19 C 7.7059 0.0945 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 7 12 +M V30 12 1 8 13 +M V30 13 2 10 14 +M V30 14 2 13 15 +M V30 15 1 13 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 2 17 19 +M V30 19 1 11 14 +M V30 20 1 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +307 + +> +Z234895017 + +> +254.327 + +> +2.810 + +> +2 + +> +55.120 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL594759 + +> +0.88 + +$$$$ +Compound 308 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2929 -0.7473 0.0 0 +M V30 3 N 2.5859 0.0118 0.0 0 +M V30 4 C 1.2811 -2.2419 0.0 0 +M V30 5 C 3.8789 -0.7354 0.0 0 +M V30 6 C 2.5741 1.5302 0.0 0 +M V30 7 C 2.5741 -2.9892 0.0 0 +M V30 8 C -0.0355 -2.9892 0.0 0 +M V30 9 C 5.1719 0.0237 0.0 0 +M V30 10 C 3.867 2.2894 0.0 0 +M V30 11 C 2.5622 -4.4839 0.0 0 +M V30 12 C -0.0474 -4.4839 0.0 0 +M V30 13 C 5.16 1.542 0.0 0 +M V30 14 C 6.4649 -0.7235 0.0 0 +M V30 15 O 3.8552 -5.2193 0.0 0 +M V30 16 C 1.2455 -5.2193 0.0 0 +M V30 17 C 6.453 2.3012 0.0 0 +M V30 18 C 7.7579 0.0355 0.0 0 +M V30 19 C 7.746 1.5658 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 2 9 13 +M V30 13 1 9 14 +M V30 14 1 11 15 +M V30 15 2 11 16 +M V30 16 1 13 17 +M V30 17 2 14 18 +M V30 18 2 17 19 +M V30 19 1 10 13 +M V30 20 1 12 16 +M V30 21 1 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +308 + +> +Z31444084 + +> +253.296 + +> +2.954 + +> +1 + +> +40.540 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3629676 + +> +0.87 + +$$$$ +Compound 309 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.497 0.0 0 +M V30 3 N -1.3307 -2.2337 0.0 0 +M V30 4 C 1.2832 -2.2337 0.0 0 +M V30 5 N -1.3426 -3.7308 0.0 0 +M V30 6 C 1.2713 -3.7308 0.0 0 +M V30 7 C 2.5783 -1.4733 0.0 0 +M V30 8 C -0.0475 -4.4793 0.0 0 +M V30 9 C 2.5664 -4.4793 0.0 0 +M V30 10 C 3.8733 -2.2099 0.0 0 +M V30 11 C -0.0594 -5.9764 0.0 0 +M V30 12 C 3.8615 -3.7189 0.0 0 +M V30 13 C -1.3782 -6.7249 0.0 0 +M V30 14 C 1.2356 -6.7249 0.0 0 +M V30 15 C -1.3901 -8.222 0.0 0 +M V30 16 C 1.2238 -8.222 0.0 0 +M V30 17 C -0.095 -8.9587 0.0 0 +M V30 18 O -0.1069 -10.4557 0.0 0 +M V30 19 C 1.1881 -11.2043 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 13 15 +M V30 15 2 14 16 +M V30 16 2 15 17 +M V30 17 1 17 18 +M V30 18 1 18 19 +M V30 19 1 6 8 +M V30 20 1 10 12 +M V30 21 1 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +309 + +> +Z275211486 + +> +252.268 + +> +2.216 + +> +1 + +> +50.690 + +> +2 + +> +parp2 + +> + + +> + + +$$$$ +Compound 310 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 F 11.3372 -5.2469 0.0 0 +M V30 3 C 10.4471 -4.0289 0.0 0 +M V30 4 C 11.0444 -2.6586 0.0 0 +M V30 5 C 8.9596 -4.1694 0.0 0 +M V30 6 C 10.1543 -1.4405 0.0 0 +M V30 7 C 8.0695 -2.9514 0.0 0 +M V30 8 C 8.6668 -1.5811 0.0 0 +M V30 9 N 7.7767 -0.363 0.0 0 +M V30 10 N 8.2218 1.0657 0.0 0 +M V30 11 C 6.2776 -0.3513 0.0 0 +M V30 12 C 7.0037 1.9559 0.0 0 +M V30 13 N 5.3875 -1.5459 0.0 0 +M V30 14 C 5.8091 1.0775 0.0 0 +M V30 15 C 6.992 3.455 0.0 0 +M V30 16 C 4.3802 1.5459 0.0 0 +M V30 17 C 5.692 4.2046 0.0 0 +M V30 18 N 2.9514 2.0144 0.0 0 +M V30 19 C 5.6803 5.7037 0.0 0 +M V30 20 N 4.3802 6.4533 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 2 3 +M V30 2 2 3 4 +M V30 3 1 3 5 +M V30 4 1 4 6 +M V30 5 2 5 7 +M V30 6 2 6 8 +M V30 7 1 8 9 +M V30 8 1 9 10 +M V30 9 1 9 11 +M V30 10 2 10 12 +M V30 11 1 11 13 +M V30 12 2 11 14 +M V30 13 1 12 15 +M V30 14 1 14 16 +M V30 15 1 15 17 +M V30 16 3 16 18 +M V30 17 1 17 19 +M V30 18 1 19 20 +M V30 19 1 7 8 +M V30 20 1 12 14 +M V30 END BOND +M V30 END CTAB +M END +> +310 + +> +Z361907536 + +> +259.282 + +> +1.005 + +> +2 + +> +93.650 + +> +4 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 311 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.4946 0.0118 0.0 0 +M V30 3 C 2.2419 -1.2811 0.0 0 +M V30 4 C 2.2419 1.3285 0.0 0 +M V30 5 C 3.7365 -1.2692 0.0 0 +M V30 6 C 3.7365 1.3404 0.0 0 +M V30 7 C 4.472 0.0474 0.0 0 +M V30 8 C 5.9666 0.0593 0.0 0 +M V30 9 N 6.8444 1.2929 0.0 0 +M V30 10 N 6.8444 -1.1506 0.0 0 +M V30 11 C 8.2679 0.8422 0.0 0 +M V30 12 C 8.2679 -0.6761 0.0 0 +M V30 13 C 9.5608 1.6013 0.0 0 +M V30 14 C 9.5608 -1.4115 0.0 0 +M V30 15 C 10.8538 0.8659 0.0 0 +M V30 16 C 10.8538 -0.6524 0.0 0 +M V30 17 C 12.1468 1.6251 0.0 0 +M V30 18 O 12.1349 3.1434 0.0 0 +M V30 19 O 13.4397 0.8896 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 2 8 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 2 13 15 +M V30 15 2 14 16 +M V30 16 1 15 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 6 7 +M V30 20 2 11 12 +M V30 21 1 15 16 +M V30 END BOND +M V30 END CTAB +M END +> +311 + +> +Z394629336 + +> +272.686 + +> +4.451 + +> +2 + +> +65.980 + +> +2 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL208463 + +> +0.86 + +$$$$ +Compound 312 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.313 0.7688 0.0 0 +M V30 3 C 1.2893 0.7688 0.0 0 +M V30 4 N -1.4786 2.2711 0.0 0 +M V30 5 N -2.697 0.1656 0.0 0 +M V30 6 C 2.5787 0.0236 0.0 0 +M V30 7 N -2.9572 2.5905 0.0 0 +M V30 8 C -3.7143 1.2893 0.0 0 +M V30 9 N 3.868 0.7925 0.0 0 +M V30 10 N 2.5669 -1.4668 0.0 0 +M V30 11 N -5.2166 1.1474 0.0 0 +M V30 12 C 5.1574 0.0473 0.0 0 +M V30 13 C 3.8562 -2.212 0.0 0 +M V30 14 C 5.1456 -1.4431 0.0 0 +M V30 15 C 6.4468 0.8162 0.0 0 +M V30 16 O 3.8444 -3.7024 0.0 0 +M V30 17 C 6.435 -2.1883 0.0 0 +M V30 18 C 7.7361 0.0709 0.0 0 +M V30 19 C 7.7243 -1.4194 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 8 11 +M V30 11 1 9 12 +M V30 12 1 10 13 +M V30 13 2 12 14 +M V30 14 1 12 15 +M V30 15 2 13 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 2 17 19 +M V30 19 2 7 8 +M V30 20 1 13 14 +M V30 21 1 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +312 + +> +Z18356172 + +> +274.302 + +> +-0.213 + +> +3 + +> +109.050 + +> +3 + +> +parp1 + +> + + +> + + +$$$$ +Compound 313 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O -1.5118 0.0118 0.0 0 +M V30 3 O 1.4882 0.0118 0.0 0 +M V30 4 N -0.0118 -1.4882 0.0 0 +M V30 5 C -0.0118 1.5118 0.0 0 +M V30 6 C -1.3229 -2.2205 0.0 0 +M V30 7 C -1.3347 -3.7088 0.0 0 +M V30 8 N -2.6458 -4.4529 0.0 0 +M V30 9 C -2.6576 -5.9412 0.0 0 +M V30 10 O -1.3701 -6.6735 0.0 0 +M V30 11 C -3.9687 -6.6735 0.0 0 +M V30 12 C -5.2798 -5.9176 0.0 0 +M V30 13 C -3.9805 -8.1618 0.0 0 +M V30 14 C -6.5909 -6.6499 0.0 0 +M V30 15 C -5.2916 -8.8941 0.0 0 +M V30 16 C -6.6027 -8.1382 0.0 0 +M V30 17 C -8.0319 -6.1774 0.0 0 +M V30 18 N -8.0437 -8.587 0.0 0 +M V30 19 C -8.9296 -7.3822 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 4 6 +M V30 6 1 6 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 2 13 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 15 16 +M V30 20 1 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +313 + +> +Z431571492 + +> +283.347 + +> +0.399 + +> +3 + +> +87.300 + +> +4 + +> +ATM + +> + + +> + + +$$$$ +Compound 314 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4972 0.0 0 +M V30 3 N -1.3308 -2.2339 0.0 0 +M V30 4 C 1.2833 -2.2339 0.0 0 +M V30 5 N -1.3427 -3.7311 0.0 0 +M V30 6 C 1.2714 -3.7311 0.0 0 +M V30 7 C 2.5785 -1.4734 0.0 0 +M V30 8 C -0.0475 -4.4797 0.0 0 +M V30 9 C 2.5666 -4.4797 0.0 0 +M V30 10 C 3.8737 -2.2101 0.0 0 +M V30 11 C -0.0594 -5.9769 0.0 0 +M V30 12 C 3.8618 -3.7192 0.0 0 +M V30 13 O -1.3783 -6.7255 0.0 0 +M V30 14 N 1.2357 -6.7255 0.0 0 +M V30 15 C 2.5309 -5.9531 0.0 0 +M V30 16 C 1.2239 -8.2227 0.0 0 +M V30 17 C 3.8262 -6.7017 0.0 0 +M V30 18 C 2.5191 -8.9594 0.0 0 +M V30 19 N 3.8143 -8.199 0.0 0 +M V30 20 C 5.1095 -8.9357 0.0 0 +M V30 21 C 5.0976 -10.4329 0.0 0 +M V30 22 C 6.3928 -11.1815 0.0 0 +M V30 23 C 3.7786 -11.1815 0.0 0 +M V30 24 C 6.3809 -12.6787 0.0 0 +M V30 25 C 3.7667 -12.6787 0.0 0 +M V30 26 C 5.0619 -13.4154 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 1 20 21 +M V30 21 2 21 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 2 23 25 +M V30 25 2 24 26 +M V30 26 1 6 8 +M V30 27 1 10 12 +M V30 28 1 18 19 +M V30 29 1 25 26 +M V30 END BOND +M V30 END CTAB +M END +> +314 + +> +Z27763786 + +> +348.398 + +> +1.187 + +> +1 + +> +65.010 + +> +3 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3105422 + +> +0.88 + +$$$$ +Compound 315 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 Cl 3.5969 0.0946 0.0 0 +M V30 3 S -0.7572 -7.3832 0.0 0 +M V30 4 O 0.7335 -7.3713 0.0 0 +M V30 5 O -2.2717 -7.3713 0.0 0 +M V30 6 N -0.769 -8.874 0.0 0 +M V30 7 C -0.769 -5.8686 0.0 0 +M V30 8 C 0.5206 -9.6194 0.0 0 +M V30 9 C -2.0824 -9.6194 0.0 0 +M V30 10 C 0.5206 -5.1114 0.0 0 +M V30 11 C -2.0824 -5.1114 0.0 0 +M V30 12 C 0.5087 -11.1102 0.0 0 +M V30 13 C -2.0942 -11.1102 0.0 0 +M V30 14 C 0.5087 -3.5969 0.0 0 +M V30 15 C 1.8103 -5.845 0.0 0 +M V30 16 C -2.0942 -3.5969 0.0 0 +M V30 17 N -0.8045 -11.8438 0.0 0 +M V30 18 C 1.7984 -2.8396 0.0 0 +M V30 19 C -0.8045 -2.8396 0.0 0 +M V30 20 C 3.0999 -5.0877 0.0 0 +M V30 21 N 3.0881 -3.5851 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 3 4 +M V30 2 2 3 5 +M V30 3 1 3 6 +M V30 4 1 3 7 +M V30 5 1 6 8 +M V30 6 1 6 9 +M V30 7 1 7 10 +M V30 8 2 7 11 +M V30 9 1 8 12 +M V30 10 1 9 13 +M V30 11 1 10 14 +M V30 12 2 10 15 +M V30 13 1 11 16 +M V30 14 1 12 17 +M V30 15 2 14 18 +M V30 16 1 14 19 +M V30 17 1 15 20 +M V30 18 1 18 21 +M V30 19 1 13 17 +M V30 20 2 16 19 +M V30 21 2 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +315 + +> +Z2417559498 + +> +277.342 + +> +1.058 + +> +1 + +> +62.300 + +> +1 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL38380 + +> +0.91 + +$$$$ +Compound 316 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 O 11.3599 2.8399 0.0 0 +M V30 3 C 11.8096 1.4199 0.0 0 +M V30 4 N 10.9103 0.2129 0.0 0 +M V30 5 C 13.2296 0.9703 0.0 0 +M V30 6 C 11.7859 -0.9939 0.0 0 +M V30 7 C 9.3956 0.2248 0.0 0 +M V30 8 C 13.2178 -0.5206 0.0 0 +M V30 9 C 14.5194 1.7276 0.0 0 +M V30 10 O 11.3126 -2.4139 0.0 0 +M V30 11 C 8.6383 1.5383 0.0 0 +M V30 12 C 8.6383 -1.0649 0.0 0 +M V30 13 C 14.5076 -1.2543 0.0 0 +M V30 14 C 15.8093 0.9939 0.0 0 +M V30 15 C 7.1236 1.5501 0.0 0 +M V30 16 C 7.1236 -1.0531 0.0 0 +M V30 17 C 15.7974 -0.4969 0.0 0 +M V30 18 C 6.3663 0.2603 0.0 0 +M V30 19 N 4.8516 0.2721 0.0 0 +M V30 20 C 4.0943 -1.0176 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 1 3 4 +M V30 3 1 3 5 +M V30 4 1 4 6 +M V30 5 1 4 7 +M V30 6 2 5 8 +M V30 7 1 5 9 +M V30 8 2 6 10 +M V30 9 1 7 11 +M V30 10 1 7 12 +M V30 11 1 8 13 +M V30 12 2 9 14 +M V30 13 1 11 15 +M V30 14 1 12 16 +M V30 15 2 13 17 +M V30 16 1 15 18 +M V30 17 1 18 19 +M V30 18 1 19 20 +M V30 19 1 6 8 +M V30 20 1 14 17 +M V30 21 1 16 18 +M V30 END BOND +M V30 END CTAB +M END +> +316 + +> +Z2969284610 + +> +258.316 + +> +1.614 + +> +1 + +> +49.410 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL597888 + +> +0.91 + +$$$$ +Compound 317 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2873 -0.744 0.0 0 +M V30 3 N 1.2755 -2.2322 0.0 0 +M V30 4 C 2.5747 0.0118 0.0 0 +M V30 5 C -0.0354 -2.9644 0.0 0 +M V30 6 C 2.5629 1.5235 0.0 0 +M V30 7 C 3.862 -0.7322 0.0 0 +M V30 8 C -1.3464 -2.2085 0.0 0 +M V30 9 C -0.0472 -4.4526 0.0 0 +M V30 10 C 3.8502 2.2794 0.0 0 +M V30 11 C 5.1494 0.0236 0.0 0 +M V30 12 C -2.6574 -2.9408 0.0 0 +M V30 13 C -1.3582 -5.1848 0.0 0 +M V30 14 N 3.8384 3.7912 0.0 0 +M V30 15 C 5.1376 1.5471 0.0 0 +M V30 16 C -2.6692 -4.429 0.0 0 +M V30 17 C -4.0983 -2.4684 0.0 0 +M V30 18 N -4.1101 -4.8778 0.0 0 +M V30 19 N -4.9959 -3.6731 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 10 14 +M V30 14 2 10 15 +M V30 15 2 12 16 +M V30 16 1 12 17 +M V30 17 1 16 18 +M V30 18 2 17 19 +M V30 19 1 11 15 +M V30 20 1 13 16 +M V30 21 1 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +317 + +> +Z445431098 + +> +252.271 + +> +1.812 + +> +3 + +> +83.800 + +> +2 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1973808 + +> +0.87 + +$$$$ +Compound 318 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2881 -0.7445 0.0 0 +M V30 3 N 1.2763 -2.2335 0.0 0 +M V30 4 C 2.5762 0.0236 0.0 0 +M V30 5 C -0.0354 -2.9662 0.0 0 +M V30 6 C 3.8643 -0.7208 0.0 0 +M V30 7 C 2.5644 1.5363 0.0 0 +M V30 8 C -1.3472 -2.2099 0.0 0 +M V30 9 C -0.0472 -4.4552 0.0 0 +M V30 10 C 5.1525 0.0472 0.0 0 +M V30 11 C 3.8525 2.2926 0.0 0 +M V30 12 C -2.6589 -2.9426 0.0 0 +M V30 13 C -1.359 -5.1879 0.0 0 +M V30 14 C 5.1407 1.5599 0.0 0 +M V30 15 N -4.1007 -2.4699 0.0 0 +M V30 16 C -2.6708 -4.4316 0.0 0 +M V30 17 N 6.4288 2.3162 0.0 0 +M V30 18 N -4.9988 -3.6753 0.0 0 +M V30 19 C -4.1125 -4.8807 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 2 10 14 +M V30 14 1 12 15 +M V30 15 2 12 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 1 11 14 +M V30 20 1 13 16 +M V30 21 2 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +318 + +> +Z445469452 + +> +252.271 + +> +1.812 + +> +3 + +> +83.800 + +> +2 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1973808 + +> +0.87 + +$$$$ +Compound 319 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2907 -0.746 0.0 0 +M V30 3 N 2.5815 0.0236 0.0 0 +M V30 4 C 1.2789 -2.2381 0.0 0 +M V30 5 C 3.8723 -0.7223 0.0 0 +M V30 6 C 2.5697 1.5394 0.0 0 +M V30 7 N 0.0473 -3.1144 0.0 0 +M V30 8 C 2.4868 -3.1144 0.0 0 +M V30 9 C 5.1631 0.0473 0.0 0 +M V30 10 C 3.8605 2.3092 0.0 0 +M V30 11 C 0.4973 -4.5355 0.0 0 +M V30 12 C 2.0131 -4.5355 0.0 0 +M V30 13 C 5.1513 1.5631 0.0 0 +M V30 14 C 6.4539 -0.6986 0.0 0 +M V30 15 O 2.8894 -5.7434 0.0 0 +M V30 16 C 6.4421 2.3329 0.0 0 +M V30 17 C 7.7447 0.071 0.0 0 +M V30 18 C 7.7329 1.5868 0.0 0 +M V30 19 O 9.0355 -0.675 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 9 14 +M V30 14 1 12 15 +M V30 15 1 13 16 +M V30 16 2 14 17 +M V30 17 2 16 18 +M V30 18 1 17 19 +M V30 19 1 10 13 +M V30 20 1 11 12 +M V30 21 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +319 + +> +Z608650148 + +> +262.304 + +> +-0.557 + +> +3 + +> +72.800 + +> +1 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 320 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C -1.3082 -0.7307 0.0 0 +M V30 3 C -1.32 -2.2158 0.0 0 +M V30 4 C -2.6165 0.0235 0.0 0 +M V30 5 C -2.6283 -2.9583 0.0 0 +M V30 6 C -3.9248 -0.7071 0.0 0 +M V30 7 C -3.9365 -2.1922 0.0 0 +M V30 8 C -5.2448 -2.9347 0.0 0 +M V30 9 O -6.5531 -2.1686 0.0 0 +M V30 10 N -5.2566 -4.4198 0.0 0 +M V30 11 C -6.5649 -5.1505 0.0 0 +M V30 12 C -7.8731 -4.3962 0.0 0 +M V30 13 C -6.5766 -6.6356 0.0 0 +M V30 14 C -9.1814 -5.1269 0.0 0 +M V30 15 C -7.8849 -7.3663 0.0 0 +M V30 16 C -9.1932 -6.612 0.0 0 +M V30 17 C -10.6193 -4.6555 0.0 0 +M V30 18 N -10.6311 -7.0599 0.0 0 +M V30 19 N -11.5151 -5.8577 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 2 8 9 +M V30 9 1 8 10 +M V30 10 1 10 11 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 2 13 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 1 16 18 +M V30 18 2 17 19 +M V30 19 1 6 7 +M V30 20 1 15 16 +M V30 21 1 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +320 + +> +Z237450076 + +> +316.153 + +> +3.653 + +> +2 + +> +57.780 + +> +2 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1973808 + +> +0.85 + +$$$$ +Compound 321 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.311 -0.744 0.0 0 +M V30 3 N -1.3228 -2.2322 0.0 0 +M V30 4 C -2.622 0.0236 0.0 0 +M V30 5 C -0.0354 -2.9645 0.0 0 +M V30 6 N -2.7874 1.5236 0.0 0 +M V30 7 C -4.0039 -0.5787 0.0 0 +M V30 8 C -0.0472 -4.4527 0.0 0 +M V30 9 C -4.2637 1.8425 0.0 0 +M V30 10 C -5.0196 0.5433 0.0 0 +M V30 11 C -1.3582 -5.185 0.0 0 +M V30 12 C 1.2401 -5.185 0.0 0 +M V30 13 O -6.5196 0.4015 0.0 0 +M V30 14 O -2.7992 -4.7126 0.0 0 +M V30 15 C -1.37 -6.6732 0.0 0 +M V30 16 C 1.2283 -6.6732 0.0 0 +M V30 17 C -3.6968 -5.9173 0.0 0 +M V30 18 O -2.811 -7.122 0.0 0 +M V30 19 C -0.0826 -7.4055 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 10 13 +M V30 13 1 11 14 +M V30 14 1 11 15 +M V30 15 2 12 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 2 15 19 +M V30 19 1 9 10 +M V30 20 1 16 19 +M V30 21 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +321 + +> +Z764449088 + +> +264.277 + +> +-0.048 + +> +3 + +> +79.820 + +> +3 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 322 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5073 0.0 0 +M V30 3 N 1.2718 2.2728 0.0 0 +M V30 4 C -1.3189 2.2728 0.0 0 +M V30 5 C 1.26 3.7801 0.0 0 +M V30 6 C 2.5554 1.5309 0.0 0 +M V30 7 C -1.3307 3.7801 0.0 0 +M V30 8 C -2.6261 1.5309 0.0 0 +M V30 9 C 2.5436 4.5338 0.0 0 CFG=2 +M V30 10 C 3.839 2.2963 0.0 0 +M V30 11 C -2.6378 4.5338 0.0 0 +M V30 12 C -0.0471 4.5338 0.0 0 +M V30 13 C -3.9332 2.2963 0.0 0 +M V30 14 C 3.8272 3.7919 0.0 0 +M V30 15 C 2.5318 6.0412 0.0 0 +M V30 16 C -3.945 3.7919 0.0 0 +M V30 17 C -2.6496 6.0412 0.0 0 +M V30 18 N 3.8155 6.7948 0.0 0 +M V30 19 C 3.8037 8.3022 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 7 12 +M V30 12 2 8 13 +M V30 13 1 9 14 +M V30 14 1 9 15 CFG=1 +M V30 15 2 11 16 +M V30 16 1 11 17 +M V30 17 1 15 18 +M V30 18 1 18 19 +M V30 19 1 10 14 +M V30 20 1 13 16 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 9) +M V30 END COLLECTION +M V30 END CTAB +M END +> +322 + +> +Z763760412 + +> +260.375 + +> +1.526 + +> +1 + +> +32.340 + +> +3 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL598093 + +> +0.9 + +$$$$ +Compound 323 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.491 0.0 0 +M V30 3 N -1.3253 -2.2246 0.0 0 +M V30 4 C 1.278 -2.2246 0.0 0 CFG=1 +M V30 5 C -2.6388 -1.4673 0.0 0 +M V30 6 O 1.2661 -3.7156 0.0 0 +M V30 7 C 2.5678 -1.4673 0.0 0 +M V30 8 C -3.9523 -2.201 0.0 0 +M V30 9 C 2.556 -4.4493 0.0 0 +M V30 10 N 3.8577 -2.201 0.0 0 +M V30 11 C -5.2658 -1.4436 0.0 0 +M V30 12 C -3.9642 -3.692 0.0 0 +M V30 13 C 3.8458 -3.692 0.0 0 +M V30 14 C -6.5793 -2.1773 0.0 0 +M V30 15 C -5.2777 -4.4257 0.0 0 +M V30 16 O -8.023 -1.704 0.0 0 +M V30 17 C -6.5912 -3.6683 0.0 0 +M V30 18 C -8.9224 -2.911 0.0 0 +M V30 19 O -8.0349 -4.118 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 4 2 CFG=3 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 1 11 14 +M V30 14 2 12 15 +M V30 15 1 14 16 +M V30 16 2 14 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 10 13 +M V30 20 1 15 17 +M V30 21 1 18 19 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 4) +M V30 END COLLECTION +M V30 END CTAB +M END +> +323 + +> +Z854100992 + +> +264.277 + +> +0.662 + +> +2 + +> +68.820 + +> +3 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 324 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.506 0.0 0 +M V30 3 N -1.3178 2.2708 0.0 0 +M V30 4 C 1.2707 2.2708 0.0 0 +M V30 5 C -1.3295 3.7769 0.0 0 +M V30 6 C 1.2589 3.7769 0.0 0 +M V30 7 C 2.5532 1.5296 0.0 0 +M V30 8 N -0.047 4.5417 0.0 0 +M V30 9 C -2.6356 4.5417 0.0 0 +M V30 10 C 2.5415 4.5417 0.0 0 +M V30 11 C 3.8358 2.2944 0.0 0 +M V30 12 N -3.9417 3.8005 0.0 0 +M V30 13 C 3.824 3.8005 0.0 0 +M V30 14 C -5.2477 4.5653 0.0 0 +M V30 15 C -3.9534 2.3179 0.0 0 +M V30 16 C -6.5538 3.824 0.0 0 +M V30 17 C -5.2595 1.5766 0.0 0 +M V30 18 C -6.5655 2.3414 0.0 0 +M V30 19 O -7.8716 1.6002 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 1 12 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 18 19 +M V30 19 1 6 8 +M V30 20 1 11 13 +M V30 21 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +324 + +> +Z44471823 + +> +259.304 + +> +-0.755 + +> +2 + +> +64.930 + +> +2 + +> +ATM + +> + + +> + + +$$$$ +Compound 325 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 Cl 3.5868 0.0943 0.0 0 +M V30 3 N 0.5663 -5.569 0.0 0 +M V30 4 C -0.3303 -4.3419 0.0 0 +M V30 5 C 1.9821 -5.097 0.0 0 +M V30 6 N 0.5427 -3.1148 0.0 0 +M V30 7 C -1.8406 -4.3301 0.0 0 +M V30 8 C 1.9703 -3.5868 0.0 0 +M V30 9 C 3.2682 -5.8285 0.0 0 +M V30 10 C -2.5957 -5.6162 0.0 0 +M V30 11 C -2.5957 -3.0204 0.0 0 +M V30 12 C 3.2564 -2.8317 0.0 0 +M V30 13 C 4.5543 -5.0734 0.0 0 +M V30 14 C -4.1059 -5.6044 0.0 0 +M V30 15 C -4.1059 -3.0086 0.0 0 +M V30 16 C 4.5425 -3.575 0.0 0 +M V30 17 C 5.8403 -5.8049 0.0 0 +M V30 18 C -4.8728 -4.2947 0.0 0 +M V30 19 C 5.8285 -2.8199 0.0 0 +M V30 20 C -6.3831 -4.2829 0.0 0 +M V30 21 N -7.1382 -5.569 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 3 4 +M V30 2 1 3 5 +M V30 3 1 4 6 +M V30 4 1 4 7 +M V30 5 2 5 8 +M V30 6 1 5 9 +M V30 7 2 7 10 +M V30 8 1 7 11 +M V30 9 1 8 12 +M V30 10 2 9 13 +M V30 11 1 10 14 +M V30 12 2 11 15 +M V30 13 2 12 16 +M V30 14 1 13 17 +M V30 15 2 14 18 +M V30 16 1 16 19 +M V30 17 1 18 20 +M V30 18 1 20 21 +M V30 19 1 6 8 +M V30 20 1 13 16 +M V30 21 1 15 18 +M V30 END BOND +M V30 END CTAB +M END +> +325 + +> +Z1357774200 + +> +251.326 + +> +3.563 + +> +2 + +> +54.700 + +> +2 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL176115 + +> +0.85 + +$$$$ +Compound 326 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.506 0.0 0 +M V30 3 N -1.3178 2.2708 0.0 0 +M V30 4 C 1.2707 2.2708 0.0 0 +M V30 5 C -1.3295 3.7769 0.0 0 +M V30 6 C 1.2589 3.7769 0.0 0 +M V30 7 C 2.5532 1.5296 0.0 0 +M V30 8 N -2.6356 4.5417 0.0 0 +M V30 9 C -0.047 4.5417 0.0 0 +M V30 10 C 2.5415 4.5417 0.0 0 +M V30 11 C 3.8358 2.2944 0.0 0 +M V30 12 C -3.9417 3.8005 0.0 0 +M V30 13 C 3.824 3.8005 0.0 0 +M V30 14 C -5.2477 4.5653 0.0 0 +M V30 15 C -3.9534 2.3179 0.0 0 +M V30 16 C -6.5538 3.824 0.0 0 +M V30 17 C -5.2595 1.5766 0.0 0 +M V30 18 C -6.5655 2.3414 0.0 0 +M V30 19 N -7.8716 1.6002 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 8 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 1 12 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 18 19 +M V30 19 1 6 9 +M V30 20 1 11 13 +M V30 21 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +326 + +> +Z975847804 + +> +257.331 + +> +0.895 + +> +3 + +> +67.150 + +> +2 + +> +parp10 + +> + + +> + + +$$$$ +Compound 327 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 N 0.0 0.0 0.0 0 +M V30 2 C -1.3057 0.7528 0.0 0 +M V30 3 C -0.0117 -1.4821 0.0 0 +M V30 4 N -1.3174 2.2585 0.0 0 +M V30 5 C -2.6114 0.0235 0.0 0 +M V30 6 N -1.3174 -2.2114 0.0 0 +M V30 7 C -0.0352 3.0113 0.0 0 +M V30 8 C -2.6231 3.0113 0.0 0 +M V30 9 N -4.0465 0.494 0.0 0 +M V30 10 C -2.6231 -1.4586 0.0 0 +M V30 11 C -0.047 4.517 0.0 0 +M V30 12 C -2.6349 4.517 0.0 0 +M V30 13 C -4.9405 -0.7057 0.0 0 +M V30 14 N -4.0582 -1.9056 0.0 0 +M V30 15 C -1.3527 5.2698 0.0 0 +M V30 16 C -1.3645 6.7755 0.0 0 +M V30 17 C -2.6702 7.5283 0.0 0 +M V30 18 N -2.6819 9.034 0.0 0 +M V30 19 C -3.9876 9.7869 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 4 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 2 5 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 1 18 19 +M V30 19 1 6 10 +M V30 20 1 12 15 +M V30 21 2 13 14 +M V30 END BOND +M V30 END CTAB +M END +> +327 + +> +Z1187869802 + +> +260.338 + +> +0.718 + +> +2 + +> +69.730 + +> +4 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL2420911 + +> +0.94 + +$$$$ +Compound 328 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.5055 0.0117 0.0 0 +M V30 3 C 0.4469 1.435 0.0 0 +M V30 4 N -1.976 1.4467 0.0 0 +M V30 5 C -2.3995 -1.188 0.0 0 +M V30 6 C -0.7763 2.3289 0.0 0 +M V30 7 S -3.9051 -1.1762 0.0 0 +M V30 8 C -1.9525 -2.5994 0.0 0 +M V30 9 C -0.788 3.8345 0.0 0 +M V30 10 C -4.3756 -2.5877 0.0 0 +M V30 11 C -3.1758 -3.4699 0.0 0 +M V30 12 O -2.0937 4.5991 0.0 0 +M V30 13 N 0.494 4.5991 0.0 0 +M V30 14 C 0.4822 6.1046 0.0 0 +M V30 15 C -0.8233 6.8574 0.0 0 +M V30 16 C 1.7643 6.8574 0.0 0 +M V30 17 C -0.8351 8.363 0.0 0 +M V30 18 C 1.7526 8.363 0.0 0 +M V30 19 C 0.4469 9.1158 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 1 13 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 2 17 19 +M V30 19 1 4 6 +M V30 20 2 10 11 +M V30 21 1 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +328 + +> +Z95493634 + +> +286.372 + +> +4.092 + +> +1 + +> +41.990 + +> +3 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL2037102 + +> +0.87 + +$$$$ +Compound 329 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5062 0.0 0 +M V30 3 N -1.3179 2.2711 0.0 0 +M V30 4 C 1.2709 2.2711 0.0 0 +M V30 5 C -1.3297 3.7774 0.0 0 +M V30 6 C 1.2591 3.7774 0.0 0 +M V30 7 C 2.5536 1.5298 0.0 0 +M V30 8 C -2.6359 4.5306 0.0 0 +M V30 9 C -0.047 4.5306 0.0 0 +M V30 10 C 2.5418 4.5306 0.0 0 +M V30 11 C 3.8363 2.2947 0.0 0 +M V30 12 O -2.6477 6.0369 0.0 0 +M V30 13 N -3.9422 3.801 0.0 0 +M V30 14 C 3.8245 3.801 0.0 0 +M V30 15 C -5.2484 4.5541 0.0 0 +M V30 16 C -6.6252 3.9539 0.0 0 +M V30 17 C -5.4132 6.0486 0.0 0 +M V30 18 C -7.6373 5.0719 0.0 0 +M V30 19 C -6.8841 6.3664 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 6 9 +M V30 20 1 11 14 +M V30 21 1 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +329 + +> +Z79570990 + +> +256.300 + +> +1.587 + +> +2 + +> +58.200 + +> +2 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 330 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 O 6.3448 3.2197 0.0 0 +M V30 3 C 7.5522 2.3437 0.0 0 +M V30 4 N 7.5403 0.8522 0.0 0 +M V30 5 N 8.9726 2.8172 0.0 0 +M V30 6 C 6.3092 -0.0236 0.0 0 +M V30 7 C 8.9608 0.4024 0.0 0 +M V30 8 C 9.8486 1.6098 0.0 0 +M V30 9 C 6.4513 -1.5033 0.0 0 +M V30 10 C 4.9243 0.6037 0.0 0 +M V30 11 O 9.4106 -1.018 0.0 0 +M V30 12 C 10.7246 2.8409 0.0 0 +M V30 13 C 10.7246 0.4024 0.0 0 +M V30 14 C 5.2202 -2.3793 0.0 0 +M V30 15 C 3.6932 -0.2722 0.0 0 +M V30 16 C 12.1451 2.3911 0.0 0 +M V30 17 C 12.1451 0.8759 0.0 0 +M V30 18 C 3.8352 -1.7519 0.0 0 +M V30 19 C 5.3623 -3.8589 0.0 0 +M V30 20 N 4.1312 -4.7349 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 1 3 4 +M V30 3 1 3 5 +M V30 4 1 4 6 +M V30 5 1 4 7 +M V30 6 1 5 8 +M V30 7 2 6 9 +M V30 8 1 6 10 +M V30 9 2 7 11 +M V30 10 1 8 12 +M V30 11 1 8 13 +M V30 12 1 9 14 +M V30 13 2 10 15 +M V30 14 1 12 16 +M V30 15 1 13 17 +M V30 16 2 14 18 +M V30 17 1 14 19 +M V30 18 1 19 20 +M V30 19 1 7 8 +M V30 20 1 15 18 +M V30 21 1 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +330 + +> +Z2168499557 + +> +259.304 + +> +0.428 + +> +2 + +> +75.430 + +> +2 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 331 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 S 5.3863 -0.4724 0.0 0 +M V30 3 O 6.8746 -0.4606 0.0 0 +M V30 4 O 3.8743 -0.4606 0.0 0 +M V30 5 N 5.3745 -1.9608 0.0 0 +M V30 6 C 5.3745 1.0394 0.0 0 +M V30 7 C 6.5793 -2.8349 0.0 0 +M V30 8 C 4.146 -2.8349 0.0 0 +M V30 9 C 6.662 1.7954 0.0 0 +M V30 10 C 4.0633 1.7954 0.0 0 +M V30 11 C 6.1068 -4.2523 0.0 0 CFG=2 +M V30 12 C 4.5949 -4.2523 0.0 0 +M V30 13 C 6.6502 3.3074 0.0 0 +M V30 14 C 7.9495 1.063 0.0 0 +M V30 15 C 4.0515 3.3074 0.0 0 +M V30 16 N 6.9809 -5.4572 0.0 0 +M V30 17 C 7.9377 4.0752 0.0 0 +M V30 18 C 5.3391 4.0752 0.0 0 +M V30 19 C 9.2371 1.819 0.0 0 +M V30 20 N 9.2253 3.331 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 2 2 4 +M V30 3 1 2 5 +M V30 4 1 2 6 +M V30 5 1 5 7 +M V30 6 1 5 8 +M V30 7 1 6 9 +M V30 8 2 6 10 +M V30 9 1 7 11 +M V30 10 1 8 12 +M V30 11 1 9 13 +M V30 12 2 9 14 +M V30 13 1 10 15 +M V30 14 1 11 16 CFG=1 +M V30 15 2 13 17 +M V30 16 1 13 18 +M V30 17 1 14 19 +M V30 18 1 17 20 +M V30 19 1 11 12 +M V30 20 2 15 18 +M V30 21 2 19 20 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STEABS ATOMS=(1 11) +M V30 END COLLECTION +M V30 END CTAB +M END +> +331 + +> +Z1491353418 + +> +277.342 + +> +0.608 + +> +1 + +> +76.290 + +> +1 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 332 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.2409 -0.8509 0.0 0 +M V30 3 C 1.1819 -0.8982 0.0 0 +M V30 4 N -2.7183 -0.4963 0.0 0 +M V30 5 C -0.8155 -2.281 0.0 0 +M V30 6 C 0.6736 -2.3047 0.0 0 +M V30 7 C 2.6711 -0.9218 0.0 0 +M V30 8 C -3.7584 -1.5837 0.0 0 +M V30 9 C -1.8555 -3.3684 0.0 0 +M V30 10 C 1.8555 -3.2029 0.0 0 +M V30 11 C 3.0965 -2.3519 0.0 0 +M V30 12 N -3.3329 -3.0138 0.0 0 +M V30 13 C -5.2358 -1.2291 0.0 0 +M V30 14 O -1.4301 -4.7985 0.0 0 +M V30 15 N -6.2759 -2.3165 0.0 0 +M V30 16 C -7.7532 -1.9619 0.0 0 +M V30 17 C -5.8504 -3.7466 0.0 0 +M V30 18 C -8.7933 -3.0493 0.0 0 +M V30 19 C -4.3966 -4.0775 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 5 6 +M V30 20 1 9 12 +M V30 21 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +332 + +> +Z55321873 + +> +277.385 + +> +2.029 + +> +1 + +> +44.700 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3616846 + +> +0.96 + +$$$$ +Compound 333 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.474 0.165 0.0 0 +M V30 3 N 2.0754 1.5447 0.0 0 +M V30 4 C 2.3466 -1.0377 0.0 0 +M V30 5 C 1.1792 2.7711 0.0 0 +M V30 6 C 1.7216 -2.3938 0.0 0 +M V30 7 N -0.3301 2.7829 0.0 0 +M V30 8 C 1.6273 4.2098 0.0 0 +M V30 9 C 2.5943 -3.5966 0.0 0 +M V30 10 C 0.224 -2.5353 0.0 0 +M V30 11 N -0.8018 4.2216 0.0 0 +M V30 12 C 0.4009 5.106 0.0 0 +M V30 13 C 1.9693 -4.9527 0.0 0 +M V30 14 C -0.4009 -3.8914 0.0 0 +M V30 15 C 0.3891 6.6154 0.0 0 +M V30 16 C 0.4716 -5.0942 0.0 0 +M V30 17 C -0.9198 7.3702 0.0 0 +M V30 18 C 1.6745 7.3702 0.0 0 +M V30 19 C -0.1533 -6.4504 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 12 15 +M V30 15 2 13 16 +M V30 16 1 15 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 2 11 12 +M V30 20 1 14 16 +M V30 END BOND +M V30 END CTAB +M END +> +333 + +> +Z829332122 + +> +257.331 + +> +3.276 + +> +2 + +> +57.780 + +> +4 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL115220 + +> +0.88 + +$$$$ +Compound 334 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4914 0.0 0 +M V30 3 N 1.2783 -2.2253 0.0 0 +M V30 4 C -1.3257 -2.2253 0.0 0 +M V30 5 C 2.5686 -1.4677 0.0 0 CFG=2 +M V30 6 C -2.6396 -1.4677 0.0 0 +M V30 7 C -1.3375 -3.7167 0.0 0 +M V30 8 C 3.8588 -2.2016 0.0 0 +M V30 9 C 2.5567 0.0473 0.0 0 +M V30 10 C -3.9535 -2.2016 0.0 0 +M V30 11 C -2.6514 -4.4506 0.0 0 +M V30 12 N 5.22 -1.5743 0.0 0 +M V30 13 N 4.0008 -3.6812 0.0 0 +M V30 14 C -3.9653 -3.6931 0.0 0 +M V30 15 C -5.3976 -1.7281 0.0 0 +M V30 16 C 6.2143 -2.6751 0.0 0 +M V30 17 C 5.4568 -3.9771 0.0 0 +M V30 18 C -5.4094 -4.1429 0.0 0 +M V30 19 C -6.2972 -2.9355 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 5 3 CFG=1 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 2 10 14 +M V30 14 1 10 15 +M V30 15 1 12 16 +M V30 16 1 13 17 +M V30 17 1 14 18 +M V30 18 1 15 19 +M V30 19 1 11 14 +M V30 20 2 16 17 +M V30 21 1 18 19 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 5) +M V30 END COLLECTION +M V30 END CTAB +M END +> +334 + +> +Z1712175619 + +> +255.315 + +> +1.980 + +> +2 + +> +57.780 + +> +3 + +> +ATM + +> + + +> + + +$$$$ +Compound 335 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3102 -0.7318 0.0 0 +M V30 3 N -2.6205 0.0236 0.0 0 +M V30 4 C -1.322 -2.2191 0.0 0 +M V30 5 C -3.9307 -0.7082 0.0 0 +M V30 6 C -2.6323 -2.9628 0.0 0 +M V30 7 C -3.9425 -2.1955 0.0 0 +M V30 8 C -5.241 0.0472 0.0 0 +M V30 9 C -5.2528 -2.9392 0.0 0 +M V30 10 C -6.5512 -0.6846 0.0 0 +M V30 11 C -6.563 -2.1837 0.0 0 +M V30 12 C -7.8733 -2.9274 0.0 0 +M V30 13 O -9.1835 -2.1719 0.0 0 +M V30 14 N -7.8851 -4.4147 0.0 0 +M V30 15 C -6.5984 -5.1583 0.0 0 +M V30 16 C -9.1954 -5.1583 0.0 0 +M V30 17 C -6.6103 -6.6457 0.0 0 +M V30 18 C -9.2072 -6.6457 0.0 0 +M V30 19 O -7.9205 -7.3775 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 7 9 +M V30 9 2 8 10 +M V30 10 2 9 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 6 7 +M V30 20 1 10 11 +M V30 21 1 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +335 + +> +Z168210258 + +> +260.288 + +> +0.395 + +> +1 + +> +58.640 + +> +1 + +> +parp2 + +> + + +> + + +$$$$ +Compound 336 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4916 0.0 0 +M V30 3 C -1.3259 -2.2256 0.0 0 +M V30 4 C 1.2785 -2.2256 0.0 0 +M V30 5 C -1.3377 -3.7173 0.0 0 +M V30 6 C -2.64 -1.4679 0.0 0 +M V30 7 C 1.2667 -3.7173 0.0 0 +M V30 8 O -0.0473 -4.4631 0.0 0 +M V30 9 C -2.6518 -4.4631 0.0 0 +M V30 10 O -2.6518 0.0473 0.0 0 +M V30 11 C -3.9541 -2.2019 0.0 0 +M V30 12 C 2.5571 -4.4631 0.0 0 +M V30 13 C -3.9659 -3.6936 0.0 0 +M V30 14 C 3.8475 -3.6936 0.0 0 +M V30 15 C 2.5453 -5.9548 0.0 0 +M V30 16 O -5.28 -4.4394 0.0 0 +M V30 17 C 5.1379 -4.4394 0.0 0 +M V30 18 C 3.8357 -6.7006 0.0 0 +M V30 19 C 5.1261 -5.9311 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 6 11 +M V30 11 1 7 12 +M V30 12 2 9 13 +M V30 13 2 12 14 +M V30 14 1 12 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 2 17 19 +M V30 19 1 7 8 +M V30 20 1 11 13 +M V30 21 1 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +336 + +> +Z1824566175 + +> +254.238 + +> +3.563 + +> +2 + +> +66.760 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL275638 + +> +0.94 + +$$$$ +Compound 337 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4933 0.0 0 +M V30 3 C 1.28 -2.2282 0.0 0 +M V30 4 C -1.3274 -2.2282 0.0 0 +M V30 5 C 1.2681 -3.7215 0.0 0 +M V30 6 C 2.5719 -1.4696 0.0 0 +M V30 7 C -1.3392 -3.7215 0.0 0 +M V30 8 O -0.0474 -4.4682 0.0 0 +M V30 9 C 2.56 -4.4682 0.0 0 +M V30 10 C 3.8637 -2.2044 0.0 0 +M V30 11 C -2.6548 -4.4682 0.0 0 +M V30 12 C 3.8519 -3.7097 0.0 0 +M V30 13 C -2.6667 -5.9616 0.0 0 +M V30 14 C -3.9704 -3.7097 0.0 0 +M V30 15 O -1.3748 -6.7083 0.0 0 +M V30 16 C -3.9823 -6.7083 0.0 0 +M V30 17 C -5.286 -4.4564 0.0 0 +M V30 18 C -1.3867 -8.2016 0.0 0 +M V30 19 C -5.2979 -5.9379 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 7 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 13 15 +M V30 15 1 13 16 +M V30 16 2 14 17 +M V30 17 1 15 18 +M V30 18 2 16 19 +M V30 19 1 7 8 +M V30 20 1 10 12 +M V30 21 1 17 19 +M V30 END BOND +M V30 END CTAB +M END +> +337 + +> +Z1833628185 + +> +252.265 + +> +2.889 + +> +0 + +> +35.530 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL275638 + +> +0.92 + +$$$$ +Compound 338 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4937 0.0 0 +M V30 3 C 1.2803 -2.2405 0.0 0 +M V30 4 C -1.3277 -2.2405 0.0 0 +M V30 5 C 2.5724 -1.4818 0.0 0 +M V30 6 C 1.2684 -3.7342 0.0 0 +M V30 7 C -1.3395 -3.7342 0.0 0 +M V30 8 O 2.5606 0.0355 0.0 0 +M V30 9 N 3.8646 -2.2287 0.0 0 +M V30 10 C -0.0474 -4.4811 0.0 0 +M V30 11 C 5.1568 -1.4699 0.0 0 CFG=2 +M V30 12 C -0.0592 -5.9748 0.0 0 +M V30 13 C 6.449 -2.2168 0.0 0 +M V30 14 C 5.1449 0.0474 0.0 0 +M V30 15 F -0.0711 -7.4685 0.0 0 +M V30 16 F 1.4344 -5.9629 0.0 0 +M V30 17 F -1.5766 -5.9629 0.0 0 +M V30 18 O 7.7411 -1.4581 0.0 0 +M V30 19 N 6.4371 -3.7105 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 11 9 CFG=1 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 1 11 14 +M V30 14 1 12 15 +M V30 15 1 12 16 +M V30 16 1 12 17 +M V30 17 2 13 18 +M V30 18 1 13 19 +M V30 19 1 7 10 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STEABS ATOMS=(1 11) +M V30 END COLLECTION +M V30 END CTAB +M END +> +338 + +> +Z1629039861 + +> +278.203 + +> +1.088 + +> +2 + +> +72.190 + +> +4 + +> +ATM + +> + + +> + + +$$$$ +Compound 339 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 S 5.4937 -0.2841 0.0 0 +M V30 3 O 6.9855 -0.2723 0.0 0 +M V30 4 O 3.9782 -0.2723 0.0 0 +M V30 5 N 5.4818 -1.7759 0.0 0 +M V30 6 C 5.4818 1.2313 0.0 0 +M V30 7 C 6.7724 -2.51 0.0 0 +M V30 8 C 4.1676 -2.51 0.0 0 +M V30 9 C 6.7724 1.9891 0.0 0 +M V30 10 C 4.1676 1.9891 0.0 0 +M V30 11 C 6.7606 -4.0019 0.0 0 CFG=1 +M V30 12 C 4.1558 -4.0019 0.0 0 +M V30 13 C 6.7606 3.5046 0.0 0 +M V30 14 C 8.063 1.255 0.0 0 +M V30 15 C 4.1558 3.5046 0.0 0 +M V30 16 N 8.0511 -4.7359 0.0 0 +M V30 17 C 5.4463 -4.7359 0.0 0 +M V30 18 C 8.0511 4.2623 0.0 0 +M V30 19 C 5.4463 4.2623 0.0 0 +M V30 20 C 9.3535 2.0127 0.0 0 +M V30 21 N 9.3417 3.5283 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 2 2 4 +M V30 3 1 2 5 +M V30 4 1 2 6 +M V30 5 1 5 7 +M V30 6 1 5 8 +M V30 7 1 6 9 +M V30 8 2 6 10 +M V30 9 1 7 11 +M V30 10 1 8 12 +M V30 11 1 9 13 +M V30 12 2 9 14 +M V30 13 1 10 15 +M V30 14 1 11 16 CFG=1 +M V30 15 1 11 17 +M V30 16 2 13 18 +M V30 17 1 13 19 +M V30 18 1 14 20 +M V30 19 1 18 21 +M V30 20 1 12 17 +M V30 21 2 15 19 +M V30 22 2 20 21 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 11) +M V30 END COLLECTION +M V30 END CTAB +M END +> +339 + +> +Z1491212039 + +> +291.369 + +> +1.167 + +> +1 + +> +76.290 + +> +1 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 340 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 S 5.387 -0.0945 0.0 0 +M V30 3 O 6.8755 -0.0826 0.0 0 +M V30 4 O 3.8748 -0.0826 0.0 0 +M V30 5 N 5.3752 -1.583 0.0 0 +M V30 6 C 5.3752 1.4176 0.0 0 +M V30 7 C 6.5802 -2.4572 0.0 0 +M V30 8 C 4.1466 -2.4572 0.0 0 +M V30 9 C 6.6629 2.1855 0.0 0 +M V30 10 C 4.0639 2.1855 0.0 0 +M V30 11 C 6.1076 -3.8748 0.0 0 CFG=2 +M V30 12 C 4.5955 -3.8748 0.0 0 +M V30 13 C 6.6511 3.6976 0.0 0 +M V30 14 C 7.9506 1.4412 0.0 0 +M V30 15 C 4.0521 3.6976 0.0 0 +M V30 16 C 6.9819 -5.0798 0.0 0 +M V30 17 C 7.9388 4.4537 0.0 0 +M V30 18 C 5.3397 4.4537 0.0 0 +M V30 19 C 9.2383 2.2091 0.0 0 +M V30 20 N 6.3557 -6.4384 0.0 0 +M V30 21 N 9.2265 3.7213 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 2 2 4 +M V30 3 1 2 5 +M V30 4 1 2 6 +M V30 5 1 5 7 +M V30 6 1 5 8 +M V30 7 1 6 9 +M V30 8 2 6 10 +M V30 9 1 7 11 +M V30 10 1 8 12 +M V30 11 1 9 13 +M V30 12 2 9 14 +M V30 13 1 10 15 +M V30 14 1 11 16 CFG=1 +M V30 15 2 13 17 +M V30 16 1 13 18 +M V30 17 1 14 19 +M V30 18 1 16 20 +M V30 19 1 17 21 +M V30 20 1 11 12 +M V30 21 2 15 18 +M V30 22 2 19 21 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 11) +M V30 END COLLECTION +M V30 END CTAB +M END +> +340 + +> +Z1491272050 + +> +291.369 + +> +0.393 + +> +1 + +> +76.290 + +> +2 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL2420922 + +> +0.9 + +$$$$ +Compound 341 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4897 0.0 0 +M V30 3 O -1.3242 -2.2228 0.0 0 +M V30 4 C 1.2769 -2.2228 0.0 0 +M V30 5 C 2.5657 -1.4661 0.0 0 +M V30 6 C 1.2651 -3.7126 0.0 0 +M V30 7 N 2.5538 0.0472 0.0 0 +M V30 8 C 3.8544 -2.1991 0.0 0 +M V30 9 C 2.5538 -4.4456 0.0 0 +M V30 10 C 3.8426 0.8158 0.0 0 +M V30 11 C 3.8426 -3.6889 0.0 0 +M V30 12 O 5.1314 0.0709 0.0 0 +M V30 13 C 3.8308 2.3292 0.0 0 +M V30 14 C 5.1196 3.0859 0.0 0 +M V30 15 C 2.5184 3.0859 0.0 0 +M V30 16 C 5.1077 4.5993 0.0 0 +M V30 17 C 2.5066 4.5993 0.0 0 +M V30 18 C 3.7953 5.356 0.0 0 +M V30 19 O 3.7835 6.8695 0.0 0 +M V30 20 C 2.4711 7.6262 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 2 10 12 +M V30 12 1 10 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 2 16 18 +M V30 18 1 18 19 +M V30 19 1 19 20 +M V30 20 1 9 11 +M V30 21 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +341 + +> +Z55928796 + +> +271.268 + +> +3.439 + +> +2 + +> +75.630 + +> +4 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1997495 + +> +0.91 + +$$$$ +Compound 342 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O 0.9546 -1.1314 0.0 0 +M V30 3 O -0.9782 -1.1314 0.0 0 +M V30 4 C -1.3082 0.766 0.0 0 +M V30 5 C 1.2846 0.766 0.0 0 +M V30 6 C -1.32 2.2746 0.0 0 +M V30 7 C -2.6164 0.0235 0.0 0 +M V30 8 C 1.2728 2.2746 0.0 0 +M V30 9 C 2.5692 0.0235 0.0 0 +M V30 10 C -0.0353 3.0289 0.0 0 +M V30 11 C -2.6282 3.0289 0.0 0 +M V30 12 C -3.9246 0.7896 0.0 0 +M V30 13 C 2.5575 3.0289 0.0 0 +M V30 14 C 3.8539 0.7896 0.0 0 +M V30 15 O -0.0471 4.5375 0.0 0 +M V30 16 C -3.9364 2.2864 0.0 0 +M V30 17 C -5.2328 0.0471 0.0 0 +M V30 18 C 3.8421 2.2864 0.0 0 +M V30 19 O -6.541 0.8132 0.0 0 +M V30 20 O -5.2446 -1.4378 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 6 11 +M V30 11 2 7 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 2 10 15 +M V30 15 2 11 16 +M V30 16 1 12 17 +M V30 17 2 13 18 +M V30 18 2 17 19 +M V30 19 1 17 20 +M V30 20 1 8 10 +M V30 21 1 12 16 +M V30 22 1 14 18 +M V30 END BOND +M V30 END CTAB +M END +> +342 + +> +Z56769216 + +> +288.275 + +> +2.327 + +> +1 + +> +88.510 + +> +1 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL554147 + +> +0.92 + +$$$$ +Compound 343 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.2372 -0.8484 0.0 0 +M V30 3 C 1.1783 -0.8955 0.0 0 +M V30 4 N -2.7102 -0.4949 0.0 0 +M V30 5 C -0.813 -2.2742 0.0 0 +M V30 6 C 0.6716 -2.2978 0.0 0 +M V30 7 C 2.6631 -0.9191 0.0 0 +M V30 8 C -3.7472 -1.579 0.0 0 +M V30 9 C -1.85 -3.3583 0.0 0 +M V30 10 C 1.85 -3.1934 0.0 0 +M V30 11 C 3.0873 -2.3449 0.0 0 +M V30 12 N -3.323 -3.0048 0.0 0 +M V30 13 C -5.2202 -1.2255 0.0 0 +M V30 14 O -1.4258 -4.7842 0.0 0 +M V30 15 N -5.668 0.2238 0.0 0 +M V30 16 C -7.1409 0.5774 0.0 0 +M V30 17 C -4.6545 1.3315 0.0 0 +M V30 18 C -7.5887 2.0268 0.0 0 +M V30 19 C -5.1023 2.7809 0.0 0 +M V30 20 C -6.5753 3.1344 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 5 6 +M V30 21 1 9 12 +M V30 22 1 10 11 +M V30 23 1 19 20 +M V30 END BOND +M V30 END CTAB +M END +> +343 + +> +Z44489819 + +> +289.396 + +> +2.164 + +> +1 + +> +44.700 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3616846 + +> +0.93 + +$$$$ +Compound 344 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 K 0.0 0.0 0.0 0 CHG=1 +M V30 2 S 10.8133 -3.1029 0.0 0 +M V30 3 C 10.3431 -4.5133 0.0 0 +M V30 4 C 9.5909 -2.2096 0.0 0 +M V30 5 O 11.2129 -5.7122 0.0 0 +M V30 6 N 8.8387 -4.5016 0.0 0 CHG=-1 +M V30 7 C 8.3685 -3.0794 0.0 0 +M V30 8 C 9.5791 -0.7052 0.0 0 +M V30 9 O 6.9346 -2.6092 0.0 0 +M V30 10 C 8.2745 0.047 0.0 0 +M V30 11 C 8.2627 1.5514 0.0 0 +M V30 12 C 6.9698 -0.6817 0.0 0 +M V30 13 C 6.9581 2.3037 0.0 0 +M V30 14 C 5.6652 0.0705 0.0 0 +M V30 15 O 6.9463 3.8081 0.0 0 +M V30 16 C 5.6534 1.5632 0.0 0 +M V30 17 C 8.2275 4.5603 0.0 0 +M V30 18 O 4.3488 2.3154 0.0 0 +M V30 19 C 4.337 3.8199 0.0 0 +M V30 20 F 3.0324 4.5721 0.0 0 +M V30 21 F 5.6182 4.5721 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 2 3 +M V30 2 1 2 4 +M V30 3 2 3 5 +M V30 4 1 3 6 +M V30 5 1 4 7 +M V30 6 2 4 8 CFG=2 +M V30 7 2 7 9 +M V30 8 1 8 10 +M V30 9 2 10 11 +M V30 10 1 10 12 +M V30 11 1 11 13 +M V30 12 2 12 14 +M V30 13 1 13 15 +M V30 14 2 13 16 +M V30 15 1 15 17 +M V30 16 1 16 18 +M V30 17 1 18 19 +M V30 18 1 19 20 +M V30 19 1 19 21 +M V30 20 1 6 7 +M V30 21 1 14 16 +M V30 END BOND +M V30 END CTAB +M END +> +344 + +> +Z3464806452 + +> +301.266 + +> +1.561 + +> +0 + +> +61.830 + +> +4 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL2003286 + +> +0.91 + +$$$$ +Compound 345 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3087 -0.731 0.0 0 +M V30 3 N -2.6175 0.0235 0.0 0 +M V30 4 N -1.3205 -2.2166 0.0 0 +M V30 5 C -3.9263 -0.7074 0.0 0 +M V30 6 C -2.6293 1.5328 0.0 0 +M V30 7 C -2.6293 -2.9476 0.0 0 +M V30 8 N -5.3648 -0.2358 0.0 0 +M V30 9 C -3.9381 -2.193 0.0 0 +M V30 10 C -1.3441 2.2874 0.0 0 +M V30 11 O -2.6411 -4.4333 0.0 0 +M V30 12 C -6.2609 -1.4384 0.0 0 +M V30 13 N -5.3765 -2.6411 0.0 0 +M V30 14 C -1.3559 3.7966 0.0 0 +M V30 15 C -7.7701 -1.4266 0.0 0 +M V30 16 C -5.8482 -4.056 0.0 0 +M V30 17 C -0.0707 4.563 0.0 0 +M V30 18 O -8.5247 -0.1179 0.0 0 +M V30 19 C -7.322 -4.3507 0.0 0 +M V30 20 C -7.7937 -5.7656 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 8 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 1 12 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 1 19 20 +M V30 20 1 7 9 +M V30 21 1 12 13 +M V30 END BOND +M V30 END CTAB +M END +> +345 + +> +Z56943381 + +> +280.323 + +> +0.935 + +> +2 + +> +87.460 + +> +6 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 346 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4487 -1.4169 0.0 0 +M V30 3 N -0.4487 -2.6214 0.0 0 +M V30 4 C 1.8657 -1.8657 0.0 0 +M V30 5 C -1.9601 -2.6096 0.0 0 +M V30 6 C 0.425 -3.8258 0.0 0 +M V30 7 C 1.8539 -3.3535 0.0 0 +M V30 8 C 3.1528 -1.0981 0.0 0 +M V30 9 C -2.7159 -1.2989 0.0 0 +M V30 10 C -2.7159 -3.8967 0.0 0 +M V30 11 O -0.0472 -5.2428 0.0 0 +M V30 12 C 3.141 -4.0974 0.0 0 +M V30 13 C 4.4399 -1.8302 0.0 0 +M V30 14 C -4.2273 -1.2871 0.0 0 +M V30 15 C -4.2273 -3.8849 0.0 0 +M V30 16 C 4.4281 -3.3299 0.0 0 +M V30 17 C -4.9949 -2.5742 0.0 0 +M V30 18 C -6.5063 -2.5624 0.0 0 +M V30 19 O -7.2739 -1.2516 0.0 0 +M V30 20 O -7.2739 -3.8495 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 5 10 +M V30 10 2 6 11 +M V30 11 1 7 12 +M V30 12 2 8 13 +M V30 13 1 9 14 +M V30 14 2 10 15 +M V30 15 2 12 16 +M V30 16 2 14 17 +M V30 17 1 17 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 6 7 +M V30 21 1 13 16 +M V30 22 1 15 17 +M V30 END BOND +M V30 END CTAB +M END +> +346 + +> +Z56855744 + +> +267.236 + +> +2.456 + +> +1 + +> +74.680 + +> +2 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1972820 + +> +0.86 + +$$$$ +Compound 347 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3136 0.7692 0.0 0 +M V30 3 N -1.3254 2.284 0.0 0 +M V30 4 N -2.6272 0.0236 0.0 0 +M V30 5 C -0.0355 3.0414 0.0 0 +M V30 6 C -2.639 -1.4674 0.0 0 +M V30 7 C 1.2544 2.2958 0.0 0 +M V30 8 C -0.0473 4.5562 0.0 0 +M V30 9 C -1.3491 -2.2012 0.0 0 +M V30 10 C -3.9527 -2.2012 0.0 0 +M V30 11 C 2.5444 3.0533 0.0 0 +M V30 12 C 1.2426 5.3137 0.0 0 +M V30 13 C -1.3609 -3.6923 0.0 0 +M V30 14 C -3.9645 -3.6923 0.0 0 +M V30 15 C 2.5325 4.5681 0.0 0 +M V30 16 C 3.8343 2.3077 0.0 0 +M V30 17 C -2.6746 -4.4379 0.0 0 +M V30 18 C 3.8225 5.3255 0.0 0 +M V30 19 C 5.1243 3.0651 0.0 0 +M V30 20 C 5.1125 4.5917 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 11 15 +M V30 15 2 11 16 +M V30 16 2 13 17 +M V30 17 2 15 18 +M V30 18 1 16 19 +M V30 19 1 18 20 +M V30 20 1 12 15 +M V30 21 1 14 17 +M V30 22 2 19 20 +M V30 END BOND +M V30 END CTAB +M END +> +347 + +> +Z44590349 + +> +262.306 + +> +4.184 + +> +2 + +> +41.130 + +> +2 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL354676 + +> +0.87 + +$$$$ +Compound 348 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4879 0.0 0 +M V30 3 N -1.3226 -2.2319 0.0 0 +M V30 4 C 1.2754 -2.2319 0.0 0 +M V30 5 C -2.6334 -1.4643 0.0 0 +M V30 6 O 2.5626 -1.4643 0.0 0 +M V30 7 C -3.9442 -2.2083 0.0 0 +M V30 8 C -2.6452 0.0472 0.0 0 +M V30 9 C 3.8498 -2.2083 0.0 0 +M V30 10 C -5.2551 -1.4407 0.0 0 +M V30 11 C -3.9561 0.803 0.0 0 +M V30 12 C 5.137 -1.4407 0.0 0 +M V30 13 C 3.838 -3.6963 0.0 0 +M V30 14 C -6.5659 -2.1847 0.0 0 +M V30 15 C -5.2669 0.0708 0.0 0 +M V30 16 C 6.4242 -2.1847 0.0 0 +M V30 17 C 5.1252 -4.4284 0.0 0 +M V30 18 O -6.5777 -3.6726 0.0 0 +M V30 19 C -7.8767 -1.4171 0.0 0 +M V30 20 C 6.4124 -3.6726 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 2 10 15 +M V30 15 1 12 16 +M V30 16 2 13 17 +M V30 17 2 14 18 +M V30 18 1 14 19 +M V30 19 2 16 20 +M V30 20 1 11 15 +M V30 21 1 17 20 +M V30 END BOND +M V30 END CTAB +M END +> +348 + +> +Z19735716 + +> +269.295 + +> +2.586 + +> +1 + +> +55.400 + +> +5 + +> +parp14 + +> + + +> + + +$$$$ +Compound 349 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.1653 1.4999 0.0 0 +M V30 3 C 1.4527 -0.2952 0.0 0 +M V30 4 C 1.1929 2.1259 0.0 0 +M V30 5 C -1.4763 2.2558 0.0 0 +M V30 6 C 2.185 1.0157 0.0 0 +M V30 7 C -2.7873 1.5236 0.0 0 +M V30 8 C -4.0984 2.2795 0.0 0 +M V30 9 C -5.4094 1.5472 0.0 0 +M V30 10 O -5.4212 0.059 0.0 0 +M V30 11 N -6.7204 2.3031 0.0 0 +M V30 12 C -8.0314 1.5708 0.0 0 +M V30 13 C -8.0432 0.0826 0.0 0 +M V30 14 C -9.3424 2.3267 0.0 0 +M V30 15 C -9.3542 -0.6496 0.0 0 +M V30 16 C -10.6534 1.5944 0.0 0 +M V30 17 C -9.366 -2.1377 0.0 0 +M V30 18 C -10.6653 0.1062 0.0 0 +M V30 19 O -8.0787 -2.8818 0.0 0 +M V30 20 C -10.6771 -2.8818 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 2 14 16 +M V30 16 1 15 17 +M V30 17 2 15 18 +M V30 18 2 17 19 +M V30 19 1 17 20 +M V30 20 1 4 6 +M V30 21 1 16 18 +M V30 END BOND +M V30 END CTAB +M END +> +349 + +> +Z30815408 + +> +287.377 + +> +3.191 + +> +1 + +> +46.170 + +> +6 + +> +parp14 + +> + + +> + + +$$$$ +Compound 350 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5103 0.0 0 +M V30 3 N 1.2743 2.2772 0.0 0 +M V30 4 C -1.3215 2.2772 0.0 0 +M V30 5 C 2.5604 1.5339 0.0 0 +M V30 6 C -1.3333 3.7875 0.0 0 +M V30 7 C -2.6312 1.5339 0.0 0 +M V30 8 C 3.8465 2.3008 0.0 0 +M V30 9 C 2.5486 0.0471 0.0 0 +M V30 10 C -2.643 4.5427 0.0 0 +M V30 11 C -0.0471 4.5427 0.0 0 +M V30 12 C -3.9409 2.3008 0.0 0 +M V30 13 C 3.8347 3.8111 0.0 0 +M V30 14 C 5.1326 1.5575 0.0 0 +M V30 15 C 3.8347 -0.6843 0.0 0 +M V30 16 C -3.9527 3.7993 0.0 0 +M V30 17 C -5.2506 1.5575 0.0 0 +M V30 18 O 2.525 4.5663 0.0 0 +M V30 19 C 5.1208 4.5663 0.0 0 +M V30 20 C 5.1208 0.0707 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 6 11 +M V30 11 2 7 12 +M V30 12 1 8 13 +M V30 13 1 8 14 +M V30 14 2 9 15 +M V30 15 2 10 16 +M V30 16 1 12 17 +M V30 17 2 13 18 +M V30 18 1 13 19 +M V30 19 2 14 20 +M V30 20 1 12 16 +M V30 21 1 15 20 +M V30 END BOND +M V30 END CTAB +M END +> +350 + +> +Z27879858 + +> +267.322 + +> +3.646 + +> +1 + +> +46.170 + +> +3 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL242201 + +> +0.87 + +$$$$ +Compound 351 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.51 0.0 0 +M V30 3 N 1.274 2.2768 0.0 0 +M V30 4 C -1.3212 2.2768 0.0 0 +M V30 5 C 2.5599 1.5336 0.0 0 +M V30 6 C -1.333 3.7868 0.0 0 +M V30 7 C -2.6307 1.5336 0.0 0 +M V30 8 C 3.8458 2.3004 0.0 0 +M V30 9 C 2.5481 0.0471 0.0 0 +M V30 10 C -2.6425 4.5418 0.0 0 +M V30 11 C -0.0471 4.5418 0.0 0 +M V30 12 C -3.9402 2.3004 0.0 0 +M V30 13 C 3.834 3.8104 0.0 0 +M V30 14 C 5.1317 1.5572 0.0 0 +M V30 15 C 3.834 -0.6842 0.0 0 +M V30 16 C -3.952 3.7986 0.0 0 +M V30 17 O 2.5245 4.5654 0.0 0 +M V30 18 C 5.1199 4.5654 0.0 0 +M V30 19 C 5.1199 0.0707 0.0 0 +M V30 20 C -5.2615 4.5536 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 6 11 +M V30 11 2 7 12 +M V30 12 1 8 13 +M V30 13 1 8 14 +M V30 14 2 9 15 +M V30 15 2 10 16 +M V30 16 2 13 17 +M V30 17 1 13 18 +M V30 18 2 14 19 +M V30 19 1 16 20 +M V30 20 1 12 16 +M V30 21 1 15 19 +M V30 END BOND +M V30 END CTAB +M END +> +351 + +> +Z27879914 + +> +267.322 + +> +3.646 + +> +1 + +> +46.170 + +> +3 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL242201 + +> +0.88 + +$$$$ +Compound 352 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2921 -0.7468 0.0 0 +M V30 3 C 1.2802 -2.2404 0.0 0 +M V30 4 C 2.5842 0.0237 0.0 0 +M V30 5 C 2.5723 -2.9754 0.0 0 +M V30 6 C 3.8763 -0.7231 0.0 0 +M V30 7 C 3.8645 -2.2167 0.0 0 +M V30 8 C 5.1566 -2.9517 0.0 0 +M V30 9 C 6.4487 -2.193 0.0 0 +M V30 10 O 6.4368 -0.6756 0.0 0 +M V30 11 N 7.7408 -2.928 0.0 0 +M V30 12 C 9.0329 -2.1693 0.0 0 +M V30 13 C 9.0211 -0.6519 0.0 0 +M V30 14 C 10.3251 -2.9043 0.0 0 +M V30 15 C 10.3132 0.1185 0.0 0 +M V30 16 C 11.6172 -2.1456 0.0 0 +M V30 17 C 10.3014 1.6358 0.0 0 +M V30 18 C 11.6053 -0.6282 0.0 0 +M V30 19 O 8.9855 2.3945 0.0 0 +M V30 20 C 11.5935 2.3945 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 2 14 16 +M V30 16 1 15 17 +M V30 17 2 15 18 +M V30 18 2 17 19 +M V30 19 1 17 20 +M V30 20 1 6 7 +M V30 21 1 16 18 +M V30 END BOND +M V30 END CTAB +M END +> +352 + +> +Z30816350 + +> +287.741 + +> +3.320 + +> +1 + +> +46.170 + +> +4 + +> +parp14 + +> + + +> + + +$$$$ +Compound 353 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2942 -0.7361 0.0 0 +M V30 3 O 2.5885 0.0237 0.0 0 +M V30 4 C 1.2824 -2.2323 0.0 0 +M V30 5 C 2.5766 -2.9685 0.0 0 +M V30 6 C -0.0356 -2.9685 0.0 0 +M V30 7 C 2.5648 -4.4646 0.0 0 +M V30 8 C -0.0474 -4.4646 0.0 0 +M V30 9 C 1.2467 -5.2127 0.0 0 +M V30 10 C 3.859 -5.2127 0.0 0 +M V30 11 O 3.8472 -6.7088 0.0 0 +M V30 12 C 5.1414 -7.4569 0.0 0 +M V30 13 C 6.4357 -6.6851 0.0 0 +M V30 14 C 5.1296 -8.953 0.0 0 +M V30 15 C 6.4239 -5.1652 0.0 0 +M V30 16 C 7.73 -7.4332 0.0 0 +M V30 17 C 6.4239 -9.6892 0.0 0 +M V30 18 O 5.1058 -4.4053 0.0 0 +M V30 19 N 7.7181 -4.4053 0.0 0 +M V30 20 C 7.7181 -8.9293 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 1 13 16 +M V30 16 2 14 17 +M V30 17 2 15 18 +M V30 18 1 15 19 +M V30 19 2 16 20 +M V30 20 1 8 9 +M V30 21 1 17 20 +M V30 END BOND +M V30 END CTAB +M END +> +353 + +> +Z149200094 + +> +271.268 + +> +2.277 + +> +2 + +> +89.620 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3105429 + +> +0.89 + +$$$$ +Compound 354 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.3073 0.7538 0.0 0 +M V30 3 C -2.6147 0.0235 0.0 0 +M V30 4 C -1.3191 2.2614 0.0 0 +M V30 5 O -2.6265 -1.4605 0.0 0 +M V30 6 C -3.9221 0.7773 0.0 0 +M V30 7 C -2.6265 3.027 0.0 0 +M V30 8 C -1.3427 -2.2025 0.0 0 +M V30 9 C -3.9339 2.285 0.0 0 +M V30 10 C -2.6383 4.5346 0.0 0 +M V30 11 N -3.9457 5.2884 0.0 0 +M V30 12 C -1.3545 5.2884 0.0 0 +M V30 13 N -3.9575 6.7961 0.0 0 +M V30 14 C -1.3662 6.7961 0.0 0 +M V30 15 C -0.0706 4.5464 0.0 0 +M V30 16 C -2.6736 7.5499 0.0 0 +M V30 17 C -0.0824 7.5499 0.0 0 +M V30 18 C 1.2131 5.3002 0.0 0 +M V30 19 O -2.6854 9.0575 0.0 0 +M V30 20 C 1.2013 6.8196 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 2 10 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 2 12 14 +M V30 14 1 12 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 2 16 19 +M V30 19 2 17 20 +M V30 20 1 7 9 +M V30 21 1 14 16 +M V30 22 1 18 20 +M V30 END BOND +M V30 END CTAB +M END +> +354 + +> +Z362839680 + +> +286.713 + +> +2.810 + +> +1 + +> +50.690 + +> +2 + +> +parp2 + +> + + +> + + +$$$$ +Compound 355 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4486 1.4404 0.0 0 +M V30 3 C 1.8654 1.9126 0.0 0 +M V30 4 C -0.4486 2.6683 0.0 0 +M V30 5 C 1.8536 3.4239 0.0 0 +M V30 6 C 3.1523 1.1688 0.0 0 +M V30 7 O 0.425 3.8962 0.0 0 +M V30 8 C -1.9599 2.6801 0.0 0 +M V30 9 C 3.1405 4.1913 0.0 0 +M V30 10 C 4.4393 1.9363 0.0 0 +M V30 11 C -2.7155 1.3931 0.0 0 +M V30 12 C 4.4275 3.4593 0.0 0 +M V30 13 C -4.2268 1.4049 0.0 0 +M V30 14 C -1.9835 0.1062 0.0 0 +M V30 15 O 5.7144 4.2268 0.0 0 +M V30 16 C -4.9942 0.118 0.0 0 +M V30 17 C -2.7391 -1.1806 0.0 0 +M V30 18 C -4.2504 -1.1688 0.0 0 +M V30 19 O -5.0178 -2.4557 0.0 0 +M V30 20 C -4.274 -3.7427 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 4 8 CFG=2 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 12 15 +M V30 15 1 13 16 +M V30 16 2 14 17 +M V30 17 2 16 18 +M V30 18 1 18 19 +M V30 19 1 19 20 +M V30 20 1 5 7 +M V30 21 1 10 12 +M V30 22 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +355 + +> +Z196385664 + +> +268.264 + +> +3.696 + +> +1 + +> +55.760 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3290475 + +> +0.86 + +$$$$ +Compound 356 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4469 1.4348 0.0 0 +M V30 3 C 1.8583 1.9053 0.0 0 +M V30 4 C -0.4469 2.658 0.0 0 +M V30 5 C 1.8465 3.4108 0.0 0 +M V30 6 C 3.1403 1.1643 0.0 0 +M V30 7 O 0.4234 3.8812 0.0 0 +M V30 8 C -1.9523 2.6698 0.0 0 +M V30 9 C 3.1285 4.1753 0.0 0 +M V30 10 C 4.4222 1.9288 0.0 0 +M V30 11 C -2.7051 3.9753 0.0 0 +M V30 12 C 4.4105 3.446 0.0 0 +M V30 13 C -4.2105 3.9871 0.0 0 +M V30 14 C -1.9759 5.2808 0.0 0 +M V30 15 O 5.6925 4.2105 0.0 0 +M V30 16 O -4.975 2.7051 0.0 0 +M V30 17 C -4.975 5.2926 0.0 0 +M V30 18 C -2.7286 6.5863 0.0 0 +M V30 19 C -6.4805 2.7168 0.0 0 +M V30 20 C -4.2341 6.5981 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 4 8 CFG=2 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 12 15 +M V30 15 1 13 16 +M V30 16 1 13 17 +M V30 17 2 14 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 5 7 +M V30 21 1 10 12 +M V30 22 1 18 20 +M V30 END BOND +M V30 END CTAB +M END +> +356 + +> +Z196385698 + +> +268.264 + +> +3.696 + +> +1 + +> +55.760 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3290477 + +> +0.85 + +$$$$ +Compound 357 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C 1.2978 -0.7382 0.0 0 +M V30 3 C 2.5957 0.0238 0.0 0 +M V30 4 C 1.2859 -2.2385 0.0 0 +M V30 5 C 3.8936 -0.7144 0.0 0 +M V30 6 C 2.5838 -2.9767 0.0 0 +M V30 7 C 3.8817 -2.2147 0.0 0 +M V30 8 N 5.1796 -2.9529 0.0 0 +M V30 9 C 6.4775 -2.1909 0.0 0 +M V30 10 C 7.7754 -2.9291 0.0 0 +M V30 11 N 9.0732 -2.1671 0.0 0 +M V30 12 N 7.7634 -4.4294 0.0 0 +M V30 13 C 10.3711 -2.9053 0.0 0 +M V30 14 C 9.0613 -5.1796 0.0 0 +M V30 15 C 10.3592 -4.4056 0.0 0 +M V30 16 C 11.669 -2.1432 0.0 0 +M V30 17 O 9.0494 -6.6799 0.0 0 +M V30 18 C 11.6571 -5.1558 0.0 0 +M V30 19 C 12.9669 -2.8815 0.0 0 +M V30 20 C 12.955 -4.3818 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 1 9 10 +M V30 10 2 10 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 2 13 15 +M V30 15 1 13 16 +M V30 16 2 14 17 +M V30 17 1 15 18 +M V30 18 2 16 19 +M V30 19 2 18 20 +M V30 20 1 6 7 +M V30 21 1 14 15 +M V30 22 1 19 20 +M V30 END BOND +M V30 END CTAB +M END +> +357 + +> +Z44495149 + +> +330.179 + +> +2.417 + +> +2 + +> +53.490 + +> +3 + +> +parp2 + +> + + +> + + +$$$$ +Compound 358 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.1646 1.4933 0.0 0 +M V30 3 C 1.4463 -0.2939 0.0 0 +M V30 4 S -1.4698 2.2576 0.0 0 +M V30 5 N 1.1876 2.1165 0.0 0 +M V30 6 N 2.1871 1.0112 0.0 0 +M V30 7 N 2.046 -1.6462 0.0 0 +M V30 8 C -1.4815 3.7627 0.0 0 +M V30 9 C -2.7868 4.5271 0.0 0 +M V30 10 N -4.092 3.7863 0.0 0 +M V30 11 N -2.7985 6.0322 0.0 0 +M V30 12 C -5.3972 4.5506 0.0 0 +M V30 13 C -4.1037 6.7847 0.0 0 +M V30 14 C -5.409 6.0557 0.0 0 +M V30 15 C -6.7024 3.8098 0.0 0 +M V30 16 O -4.1155 8.2898 0.0 0 +M V30 17 C -6.7142 6.8082 0.0 0 +M V30 18 C -8.0076 4.5741 0.0 0 +M V30 19 C -6.7142 2.3282 0.0 0 +M V30 20 C -8.0194 6.0792 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 1 4 8 +M V30 8 1 8 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 2 12 14 +M V30 14 1 12 15 +M V30 15 2 13 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 1 15 19 +M V30 19 2 17 20 +M V30 20 1 5 6 +M V30 21 1 13 14 +M V30 22 1 18 20 +M V30 END BOND +M V30 END CTAB +M END +> +358 + +> +Z96209211 + +> +305.379 + +> +0.607 + +> +2 + +> +93.260 + +> +3 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 359 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.2847 -0.7425 0.0 0 +M V30 3 C -0.0117 1.5087 0.0 0 +M V30 4 N 2.6402 -0.1178 0.0 0 +M V30 5 N 1.4262 -2.2159 0.0 0 +M V30 6 C -1.3201 2.2748 0.0 0 +M V30 7 N 3.6303 -1.214 0.0 0 +M V30 8 C 2.876 -2.5106 0.0 0 +M V30 9 N -2.6284 1.5322 0.0 0 +M V30 10 N -1.3319 3.7835 0.0 0 +M V30 11 N 3.4771 -3.8661 0.0 0 +M V30 12 C -3.9368 2.2984 0.0 0 +M V30 13 C -2.6402 4.5379 0.0 0 +M V30 14 C -3.9486 3.8071 0.0 0 +M V30 15 C -5.2451 1.5558 0.0 0 +M V30 16 O -2.652 6.0466 0.0 0 +M V30 17 C -5.2569 4.5615 0.0 0 +M V30 18 C -6.5535 2.322 0.0 0 +M V30 19 C -5.2569 0.0707 0.0 0 +M V30 20 C -6.5653 3.8307 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 8 11 +M V30 11 1 9 12 +M V30 12 1 10 13 +M V30 13 2 12 14 +M V30 14 1 12 15 +M V30 15 2 13 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 1 15 19 +M V30 19 2 17 20 +M V30 20 2 7 8 +M V30 21 1 13 14 +M V30 22 1 18 20 +M V30 END BOND +M V30 END CTAB +M END +> +359 + +> +Z96184293 + +> +288.328 + +> +0.286 + +> +3 + +> +109.050 + +> +3 + +> +parp15, parp1 + +> + + +> + + +$$$$ +Compound 360 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -1.3073 0.7538 0.0 0 +M V30 3 C -2.6147 0.0235 0.0 0 +M V30 4 C -1.3191 2.2614 0.0 0 +M V30 5 O -2.6265 -1.4605 0.0 0 +M V30 6 C -3.9221 0.7773 0.0 0 +M V30 7 C -2.6265 3.027 0.0 0 +M V30 8 C -1.3427 -2.2025 0.0 0 +M V30 9 C -3.9339 2.285 0.0 0 +M V30 10 C -2.6383 4.5346 0.0 0 +M V30 11 N -3.9457 5.2884 0.0 0 +M V30 12 C -1.3545 5.2884 0.0 0 +M V30 13 N -3.9575 6.7961 0.0 0 +M V30 14 C -1.3662 6.7961 0.0 0 +M V30 15 C -0.0706 4.5464 0.0 0 +M V30 16 C -2.6736 7.5499 0.0 0 +M V30 17 C -0.0824 7.5499 0.0 0 +M V30 18 C 1.2131 5.3002 0.0 0 +M V30 19 O -2.6854 9.0575 0.0 0 +M V30 20 C 1.2013 6.8196 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 2 10 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 2 12 14 +M V30 14 1 12 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 2 16 19 +M V30 19 2 17 20 +M V30 20 1 7 9 +M V30 21 1 14 16 +M V30 22 1 18 20 +M V30 END BOND +M V30 END CTAB +M END +> +360 + +> +Z425469912 + +> +270.258 + +> +2.270 + +> +1 + +> +50.690 + +> +2 + +> +parp2 + +> + + +> + + +$$$$ +Compound 361 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3126 0.7686 0.0 0 +M V30 3 N -2.6253 0.0236 0.0 0 +M V30 4 C -1.3245 2.2824 0.0 0 +M V30 5 C -2.6372 -1.4664 0.0 0 +M V30 6 C -0.0354 3.0392 0.0 0 +M V30 7 C -2.6372 3.0392 0.0 0 +M V30 8 C -3.9498 -2.2114 0.0 0 +M V30 9 C -0.0473 4.553 0.0 0 +M V30 10 C -2.649 4.553 0.0 0 +M V30 11 N 1.2417 5.3098 0.0 0 +M V30 12 C -1.3599 5.3098 0.0 0 +M V30 13 C 2.5307 4.5766 0.0 0 +M V30 14 O 2.5189 3.0865 0.0 0 +M V30 15 C 3.8198 5.3335 0.0 0 +M V30 16 C 5.1088 4.6003 0.0 0 +M V30 17 C 6.3978 5.3571 0.0 0 +M V30 18 C 7.6869 4.6239 0.0 0 +M V30 19 O 8.9759 5.3808 0.0 0 +M V30 20 O 7.675 3.1338 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 9 11 +M V30 11 2 9 12 +M V30 12 1 11 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 10 12 +M V30 END BOND +M V30 END CTAB +M END +> +361 + +> +Z90425248 + +> +278.304 + +> +0.860 + +> +3 + +> +95.500 + +> +7 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3414770 + +> +0.91 + +$$$$ +Compound 362 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5053 0.0 0 +M V30 3 N 1.2701 2.2697 0.0 0 +M V30 4 C -1.3171 2.2697 0.0 0 +M V30 5 C 2.552 1.5288 0.0 0 +M V30 6 C -2.6226 1.5288 0.0 0 +M V30 7 C -1.3289 3.7751 0.0 0 +M V30 8 C 4.0338 1.5406 0.0 0 +M V30 9 C 3.2811 0.2469 0.0 0 +M V30 10 C -3.928 2.2933 0.0 0 +M V30 11 C -2.6343 4.5278 0.0 0 +M V30 12 N -5.2334 1.5523 0.0 0 +M V30 13 C -3.9397 3.7986 0.0 0 +M V30 14 C -6.5388 2.3168 0.0 0 +M V30 15 O -6.5506 3.8221 0.0 0 +M V30 16 C -7.8442 1.5759 0.0 0 +M V30 17 C -9.1496 2.3403 0.0 0 +M V30 18 C -10.4551 1.5994 0.0 0 +M V30 19 O -10.4668 0.1176 0.0 0 +M V30 20 O -11.7605 2.3638 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 10 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 8 9 +M V30 21 1 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +362 + +> +Z432037678 + +> +276.288 + +> +0.541 + +> +3 + +> +95.500 + +> +6 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3414756 + +> +0.86 + +$$$$ +Compound 363 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4974 0.0 0 +M V30 3 N -1.331 -2.2342 0.0 0 +M V30 4 C 1.2835 -2.2342 0.0 0 +M V30 5 N -1.3429 -3.7317 0.0 0 +M V30 6 C 1.2716 -3.7317 0.0 0 +M V30 7 C 2.5789 -1.4736 0.0 0 +M V30 8 C -0.0475 -4.4804 0.0 0 +M V30 9 C 2.567 -4.4804 0.0 0 +M V30 10 C 3.8743 -2.2105 0.0 0 +M V30 11 C -0.0594 -5.9778 0.0 0 +M V30 12 C 3.8624 -3.7198 0.0 0 +M V30 13 O -1.3786 -6.7266 0.0 0 +M V30 14 N 1.2359 -6.7266 0.0 0 +M V30 15 C 1.2241 -8.224 0.0 0 +M V30 16 C 2.5313 -5.9541 0.0 0 +M V30 17 C 2.5195 -8.9609 0.0 0 +M V30 18 C 3.8268 -6.7028 0.0 0 +M V30 19 N 3.8149 -8.2002 0.0 0 +M V30 20 C 5.1103 -8.9371 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 1 6 8 +M V30 21 1 10 12 +M V30 22 1 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +363 + +> +Z31433955 + +> +272.302 + +> +-0.535 + +> +1 + +> +65.010 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3105422 + +> +0.89 + +$$$$ +Compound 364 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O 1.4911 0.0118 0.0 0 +M V30 3 O -1.5147 0.0118 0.0 0 +M V30 4 N -0.0118 -1.4911 0.0 0 +M V30 5 C -0.0118 1.5147 0.0 0 +M V30 6 C -1.3727 -2.1301 0.0 0 +M V30 7 C 1.3254 -2.1301 0.0 0 +M V30 8 C 1.278 2.2721 0.0 0 +M V30 9 C -1.3254 2.2721 0.0 0 +M V30 10 C -1.7159 -3.5857 0.0 0 +M V30 11 C 1.6449 -3.5857 0.0 0 +M V30 12 C 1.2662 3.7869 0.0 0 +M V30 13 C 2.568 1.5266 0.0 0 +M V30 14 C -1.3372 3.7869 0.0 0 +M V30 15 N -0.7928 -4.7455 0.0 0 +M V30 16 C 0.6982 -4.7455 0.0 0 +M V30 17 C 2.5561 4.5443 0.0 0 +M V30 18 C -0.0473 4.5443 0.0 0 +M V30 19 C 3.8579 2.284 0.0 0 +M V30 20 N 3.8461 3.8106 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 2 8 13 +M V30 13 1 9 14 +M V30 14 1 10 15 +M V30 15 1 11 16 +M V30 16 2 12 17 +M V30 17 1 12 18 +M V30 18 1 13 19 +M V30 19 1 17 20 +M V30 20 2 14 18 +M V30 21 1 15 16 +M V30 22 2 19 20 +M V30 END BOND +M V30 END CTAB +M END +> +364 + +> +Z432085130 + +> +291.369 + +> +1.038 + +> +1 + +> +62.300 + +> +1 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL38380 + +> +1.0 + +$$$$ +Compound 365 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5072 0.0 0 +M V30 3 C -1.3188 2.2608 0.0 0 +M V30 4 C 1.2717 2.2608 0.0 0 +M V30 5 C -2.6258 1.5189 0.0 0 +M V30 6 C -1.3305 3.768 0.0 0 +M V30 7 C 1.2599 3.768 0.0 0 +M V30 8 O -3.9329 2.2726 0.0 0 +M V30 9 N -2.6376 0.0353 0.0 0 +M V30 10 C -0.0471 4.5216 0.0 0 +M V30 11 C -3.9446 -0.6947 0.0 0 +M V30 12 F -0.0588 6.0288 0.0 0 +M V30 13 C -5.2517 0.0588 0.0 0 +M V30 14 C -3.9564 -2.1784 0.0 0 +M V30 15 C -6.5587 -0.6711 0.0 0 +M V30 16 C -5.2635 -2.9084 0.0 0 +M V30 17 C -6.5705 -2.1548 0.0 0 +M V30 18 C -7.9953 -0.2001 0.0 0 +M V30 19 N -8.0071 -2.6023 0.0 0 +M V30 20 N -8.8902 -1.4012 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 13 15 +M V30 15 2 14 16 +M V30 16 2 15 17 +M V30 17 1 15 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 1 7 10 +M V30 21 1 16 17 +M V30 22 1 19 20 +M V30 END BOND +M V30 END CTAB +M END +> +365 + +> +Z237451704 + +> +334.143 + +> +2.901 + +> +2 + +> +57.780 + +> +2 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1461728 + +> +0.86 + +$$$$ +Compound 366 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 Cl 3.6031 0.0948 0.0 0 +M V30 3 S -1.043 -5.8788 0.0 0 +M V30 4 O -0.3081 -7.1707 0.0 0 +M V30 5 O -1.8015 -4.5632 0.0 0 +M V30 6 N -2.3586 -6.6255 0.0 0 +M V30 7 C 0.2489 -5.1202 0.0 0 +M V30 8 C -2.3704 -8.1189 0.0 0 +M V30 9 C -3.6742 -5.8669 0.0 0 +M V30 10 C 1.5408 -5.8551 0.0 0 +M V30 11 C 0.237 -3.6031 0.0 0 +M V30 12 C -1.0785 -8.8538 0.0 0 +M V30 13 C -4.9898 -6.6136 0.0 0 +M V30 14 C 2.8327 -5.0965 0.0 0 +M V30 15 C 1.5289 -7.3485 0.0 0 +M V30 16 C 1.5289 -2.8445 0.0 0 +M V30 17 N -1.0904 -10.3472 0.0 0 +M V30 18 C -6.3055 -5.8551 0.0 0 +M V30 19 C 4.1246 -5.8314 0.0 0 +M V30 20 C 2.8208 -3.5794 0.0 0 +M V30 21 C 2.8208 -8.0833 0.0 0 +M V30 22 N 4.1128 -7.3248 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 3 4 +M V30 2 2 3 5 +M V30 3 1 3 6 +M V30 4 1 3 7 +M V30 5 1 6 8 +M V30 6 1 6 9 +M V30 7 1 7 10 +M V30 8 2 7 11 +M V30 9 1 8 12 +M V30 10 1 9 13 +M V30 11 1 10 14 +M V30 12 2 10 15 +M V30 13 1 11 16 +M V30 14 1 12 17 +M V30 15 1 13 18 +M V30 16 2 14 19 +M V30 17 1 14 20 +M V30 18 1 15 21 +M V30 19 1 19 22 +M V30 20 2 16 20 +M V30 21 2 21 22 +M V30 END BOND +M V30 END CTAB +M END +> +366 + +> +Z2044731698 + +> +293.385 + +> +1.770 + +> +1 + +> +76.290 + +> +5 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL38380 + +> +0.98 + +$$$$ +Compound 367 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 0.7442 1.3112 0.0 0 +M V30 3 C -0.0236 2.6224 0.0 0 +M V30 4 C 2.2326 1.323 0.0 0 +M V30 5 C 0.7205 3.9336 0.0 0 +M V30 6 C 2.9768 2.6342 0.0 0 +M V30 7 C 2.2089 3.9454 0.0 0 +M V30 8 N 2.9532 5.2567 0.0 0 +M V30 9 C 4.4416 5.2685 0.0 0 +M V30 10 C 5.174 6.5797 0.0 0 +M V30 11 N 6.6624 6.5915 0.0 0 +M V30 12 N 7.5365 7.82 0.0 0 +M V30 13 C 7.5365 5.3866 0.0 0 +M V30 14 C 8.9541 7.3711 0.0 0 +M V30 15 O 7.064 3.9691 0.0 0 +M V30 16 N 8.9541 5.8591 0.0 0 +M V30 17 C 10.2417 8.139 0.0 0 +M V30 18 C 10.2417 5.1149 0.0 0 +M V30 19 C 11.5293 7.4066 0.0 0 +M V30 20 C 11.5293 5.8827 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 1 9 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 1 11 13 +M V30 13 2 12 14 +M V30 14 2 13 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 1 16 18 +M V30 18 2 17 19 +M V30 19 2 18 20 +M V30 20 1 6 7 +M V30 21 1 14 16 +M V30 22 1 19 20 +M V30 END BOND +M V30 END CTAB +M END +> +367 + +> +Z368662882 + +> +272.278 + +> +2.756 + +> +1 + +> +47.940 + +> +4 + +> +DNA_pk + +> + + +> + + +$$$$ +Compound 368 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 S 5.4887 -0.4731 0.0 0 +M V30 3 O 6.9792 -0.4613 0.0 0 +M V30 4 O 3.9746 -0.4613 0.0 0 +M V30 5 N 5.4769 -1.9636 0.0 0 +M V30 6 C 5.4769 1.0409 0.0 0 +M V30 7 C 6.7663 -2.697 0.0 0 CFG=2 +M V30 8 C 4.1638 -2.697 0.0 0 +M V30 9 C 6.7663 1.798 0.0 0 +M V30 10 C 4.1638 1.798 0.0 0 +M V30 11 C 6.7545 -4.1875 0.0 0 +M V30 12 C 8.0557 -1.9281 0.0 0 +M V30 13 C 4.152 -4.1875 0.0 0 +M V30 14 C 6.7545 3.3121 0.0 0 +M V30 15 C 8.0557 1.0646 0.0 0 +M V30 16 C 4.152 3.3121 0.0 0 +M V30 17 N 5.4414 -4.9209 0.0 0 +M V30 18 C 8.0438 4.0692 0.0 0 +M V30 19 C 5.4414 4.081 0.0 0 +M V30 20 C 9.3451 1.8217 0.0 0 +M V30 21 N 9.3332 3.3358 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 2 2 4 +M V30 3 1 2 5 +M V30 4 1 2 6 +M V30 5 1 5 7 +M V30 6 1 5 8 +M V30 7 1 6 9 +M V30 8 2 6 10 +M V30 9 1 7 11 +M V30 10 1 7 12 CFG=1 +M V30 11 1 8 13 +M V30 12 1 9 14 +M V30 13 2 9 15 +M V30 14 1 10 16 +M V30 15 1 11 17 +M V30 16 2 14 18 +M V30 17 1 14 19 +M V30 18 1 15 20 +M V30 19 1 18 21 +M V30 20 1 13 17 +M V30 21 2 16 19 +M V30 22 2 20 21 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 7) +M V30 END COLLECTION +M V30 END CTAB +M END +> +368 + +> +Z1491263437 + +> +291.369 + +> +1.578 + +> +1 + +> +62.300 + +> +1 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL38380 + +> +0.9 + +$$$$ +Compound 369 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O 0.7454 -1.2897 0.0 0 +M V30 3 O -0.7572 1.3133 0.0 0 +M V30 4 N -1.3133 -0.7336 0.0 0 +M V30 5 C 1.2897 0.7572 0.0 0 +M V30 6 C -2.6267 0.0236 0.0 0 +M V30 7 C 2.5794 0.0236 0.0 0 +M V30 8 C 1.2778 2.2718 0.0 0 +M V30 9 C -3.9401 -0.7099 0.0 0 +M V30 10 C 3.8691 0.7809 0.0 0 +M V30 11 C 2.5676 -1.4672 0.0 0 +M V30 12 C 2.5676 3.029 0.0 0 +M V30 13 N -5.2535 0.0473 0.0 0 +M V30 14 C 5.1589 0.0473 0.0 0 +M V30 15 C 3.8573 2.2954 0.0 0 +M V30 16 C 3.8573 -2.2008 0.0 0 +M V30 17 C -6.5669 -0.6862 0.0 0 +M V30 18 N 5.147 -1.4435 0.0 0 +M V30 19 C -7.8803 0.0709 0.0 0 +M V30 20 C -9.1937 -0.6626 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 7 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 10 15 +M V30 15 1 11 16 +M V30 16 1 13 17 +M V30 17 1 14 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 2 12 15 +M V30 21 2 16 18 +M V30 END BOND +M V30 END CTAB +M END +> +369 + +> +Z994855764 + +> +293.385 + +> +2.266 + +> +2 + +> +71.090 + +> +6 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL38380 + +> +0.91 + +$$$$ +Compound 370 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.5113 0.0 0 +M V30 3 N -1.3224 2.2669 0.0 0 +M V30 4 C 1.2751 2.2669 0.0 0 +M V30 5 C 1.2633 3.7782 0.0 0 +M V30 6 C 2.5621 1.5349 0.0 0 +M V30 7 C 2.5503 4.5339 0.0 0 +M V30 8 C 3.8491 2.2905 0.0 0 +M V30 9 N 2.5385 6.0452 0.0 0 +M V30 10 C 3.8373 3.8019 0.0 0 +M V30 11 C 3.8255 6.8009 0.0 0 +M V30 12 O 5.1125 6.0688 0.0 0 +M V30 13 C 3.8137 8.3122 0.0 0 +M V30 14 C 5.1007 9.0797 0.0 0 +M V30 15 C 6.3876 9.8353 0.0 0 +M V30 16 C 4.345 10.3903 0.0 0 +M V30 17 C 5.8445 7.7927 0.0 0 +M V30 18 C 6.3758 11.3466 0.0 0 +M V30 19 O 7.6628 12.1141 0.0 0 +M V30 20 O 5.0652 12.1141 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 2 7 10 +M V30 10 1 9 11 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 8 10 +M V30 END BOND +M V30 END CTAB +M END +> +370 + +> +Z221346208 + +> +278.304 + +> +0.923 + +> +3 + +> +109.490 + +> +6 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3414770 + +> +0.91 + +$$$$ +Compound 371 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 S 8.1249 -0.4723 0.0 0 +M V30 3 O 9.6129 -0.4605 0.0 0 +M V30 4 O 6.6133 -0.4605 0.0 0 +M V30 5 N 8.1131 -1.9603 0.0 0 +M V30 6 C 8.1131 1.0392 0.0 0 +M V30 7 C 6.8022 -2.7043 0.0 0 CFG=2 +M V30 8 C 9.4003 1.795 0.0 0 +M V30 9 C 6.8022 1.795 0.0 0 +M V30 10 C 5.4914 -1.9485 0.0 0 +M V30 11 C 6.7904 -4.1923 0.0 0 +M V30 12 C 9.3885 3.3066 0.0 0 +M V30 13 C 10.6875 1.0628 0.0 0 +M V30 14 C 6.7904 3.3066 0.0 0 +M V30 15 N 4.1805 -2.6807 0.0 0 +M V30 16 C 5.4796 -4.9245 0.0 0 +M V30 17 C 10.6757 4.0624 0.0 0 +M V30 18 C 8.0776 4.0624 0.0 0 +M V30 19 C 11.9748 1.8186 0.0 0 +M V30 20 C 4.1687 -4.1687 0.0 0 +M V30 21 N 11.963 3.3184 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 2 2 4 +M V30 3 1 2 5 +M V30 4 1 2 6 +M V30 5 1 7 5 CFG=1 +M V30 6 1 6 8 +M V30 7 2 6 9 +M V30 8 1 7 10 +M V30 9 1 7 11 +M V30 10 1 8 12 +M V30 11 2 8 13 +M V30 12 1 9 14 +M V30 13 1 10 15 +M V30 14 1 11 16 +M V30 15 2 12 17 +M V30 16 1 12 18 +M V30 17 1 13 19 +M V30 18 1 15 20 +M V30 19 1 17 21 +M V30 20 2 14 18 +M V30 21 1 16 20 +M V30 22 2 19 21 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 7) +M V30 END COLLECTION +M V30 END CTAB +M END +> +371 + +> +Z1491234214 + +> +291.369 + +> +1.827 + +> +2 + +> +71.090 + +> +2 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 372 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2925 -0.7352 0.0 0 +M V30 3 C -1.3162 -0.7352 0.0 0 +M V30 4 N 2.5851 0.0237 0.0 0 +M V30 5 C 1.2807 -2.2293 0.0 0 +M V30 6 N -2.763 -0.2608 0.0 0 +M V30 7 C -1.3281 -2.2293 0.0 0 +M V30 8 C 2.5732 -2.9764 0.0 0 +M V30 9 C -0.0355 -2.9764 0.0 0 CFG=1 +M V30 10 N -3.6642 -1.4704 0.0 0 +M V30 11 C -2.7748 -2.68 0.0 0 +M V30 12 N 3.8658 -3.7235 0.0 0 +M V30 13 C -0.0474 -4.4706 0.0 0 +M V30 14 C -3.2492 -4.103 0.0 0 +M V30 15 C 1.2451 -5.2177 0.0 0 +M V30 16 C -1.3637 -5.2177 0.0 0 +M V30 17 C 1.2332 -6.7118 0.0 0 +M V30 18 C -1.3755 -6.7118 0.0 0 +M V30 19 C -0.083 -7.4471 0.0 0 +M V30 20 O -0.0948 -8.9412 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 1 3 6 +M V30 6 2 3 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 3 8 12 +M V30 12 1 9 13 CFG=3 +M V30 13 1 11 14 +M V30 14 2 13 15 +M V30 15 1 13 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 2 17 19 +M V30 19 1 19 20 +M V30 20 1 7 9 +M V30 21 2 10 11 +M V30 22 1 18 19 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 9) +M V30 END COLLECTION +M V30 END CTAB +M END +> +372 + +> +Z56790118 + +> +268.271 + +> +2.237 + +> +3 + +> +107.950 + +> +1 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1979318 + +> +0.88 + +$$$$ +Compound 373 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.513 0.0 0 +M V30 3 N 1.2766 2.2814 0.0 0 +M V30 4 C -1.3239 2.2814 0.0 0 +M V30 5 C 2.5651 1.5367 0.0 0 +M V30 6 C -2.636 1.5367 0.0 0 +M V30 7 C -1.3357 3.7944 0.0 0 +M V30 8 C 3.8535 2.305 0.0 0 +M V30 9 C -3.9481 2.305 0.0 0 +M V30 10 C -2.6478 4.5628 0.0 0 +M V30 11 C 5.142 1.5603 0.0 0 +M V30 12 C -3.9599 3.8181 0.0 0 +M V30 13 C 5.2839 0.0827 0.0 0 +M V30 14 C 6.5014 2.1868 0.0 0 +M V30 15 C 6.7378 -0.2127 0.0 0 +M V30 16 C 4.2673 -1.0165 0.0 0 +M V30 17 N 7.4944 1.0875 0.0 0 +M V30 18 C 7.187 -1.6312 0.0 0 +M V30 19 C 4.7165 -2.435 0.0 0 +M V30 20 C 6.1704 -2.7306 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 11 13 +M V30 13 2 11 14 +M V30 14 2 13 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 2 16 19 +M V30 19 2 18 20 +M V30 20 1 10 12 +M V30 21 1 15 17 +M V30 22 1 19 20 +M V30 END BOND +M V30 END CTAB +M END +> +373 + +> +Z26395492 + +> +264.322 + +> +2.948 + +> +2 + +> +44.890 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL343641 + +> +0.92 + +$$$$ +Compound 374 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.506 0.0 0 +M V30 3 N -1.3178 2.2708 0.0 0 +M V30 4 C 1.2707 2.2708 0.0 0 +M V30 5 C -1.3295 3.7769 0.0 0 +M V30 6 C 1.2589 3.7769 0.0 0 +M V30 7 C 2.5532 1.5296 0.0 0 +M V30 8 C -2.6356 4.5299 0.0 0 +M V30 9 C -0.047 4.5299 0.0 0 +M V30 10 C 2.5414 4.5299 0.0 0 +M V30 11 C 3.8357 2.2944 0.0 0 +M V30 12 O -2.6473 6.036 0.0 0 +M V30 13 N -3.9416 3.8004 0.0 0 +M V30 14 C 3.824 3.8004 0.0 0 +M V30 15 C -5.2477 4.5535 0.0 0 +M V30 16 C -6.5537 3.824 0.0 0 +M V30 17 C -5.2594 6.0595 0.0 0 +M V30 18 C -7.8598 4.577 0.0 0 +M V30 19 C -6.5655 6.8126 0.0 0 +M V30 20 C -7.8715 6.0831 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 6 9 +M V30 21 1 11 14 +M V30 22 1 19 20 +M V30 END BOND +M V30 END CTAB +M END +> +374 + +> +Z79410386 + +> +270.326 + +> +2.146 + +> +2 + +> +58.200 + +> +2 + +> +parp10 + +> + + +> + + +$$$$ +Compound 375 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.0163 1.1227 0.0 0 +M V30 3 N -2.4936 0.8272 0.0 0 +M V30 4 C -0.5672 2.5645 0.0 0 +M V30 5 C -2.9663 -0.5909 0.0 0 +M V30 6 N -1.4654 3.7936 0.0 0 +M V30 7 C 0.8509 3.0373 0.0 0 +M V30 8 C -4.4436 -0.8863 0.0 0 +M V30 9 C -1.9736 -1.69 0.0 0 +M V30 10 N -0.5909 5.0227 0.0 0 +M V30 11 C 0.8391 4.55 0.0 0 +M V30 12 C 2.1391 2.2927 0.0 0 +M V30 13 C -4.9164 -2.3045 0.0 0 +M V30 14 C -2.4463 -3.1082 0.0 0 +M V30 15 C 2.1272 5.3182 0.0 0 +M V30 16 C 3.4273 3.0609 0.0 0 +M V30 17 C -3.9236 -3.4036 0.0 0 +M V30 18 C 3.4154 4.5736 0.0 0 +M V30 19 O -4.3964 -4.8218 0.0 0 +M V30 20 C -3.4036 -5.9209 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 7 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 11 15 +M V30 15 2 12 16 +M V30 16 2 13 17 +M V30 17 2 15 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 1 10 11 +M V30 21 1 14 17 +M V30 22 1 16 18 +M V30 END BOND +M V30 END CTAB +M END +> +375 + +> +Z31695416 + +> +267.283 + +> +2.975 + +> +2 + +> +67.010 + +> +3 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL2007002 + +> +0.87 + +$$$$ +Compound 376 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3051 0.7525 0.0 0 +M V30 3 N -1.3169 2.2575 0.0 0 +M V30 4 N -2.6102 0.0235 0.0 0 +M V30 5 C -2.622 3.01 0.0 0 +M V30 6 C -0.0352 3.01 0.0 0 +M V30 7 C -3.9154 0.776 0.0 0 +M V30 8 C -2.622 -1.458 0.0 0 +M V30 9 O -2.6338 4.5151 0.0 0 +M V30 10 C -3.9272 2.281 0.0 0 +M V30 11 N -5.3499 0.3292 0.0 0 +M V30 12 N -5.3616 2.7513 0.0 0 +M V30 13 C -6.2435 1.552 0.0 0 +M V30 14 C -7.7485 1.5638 0.0 0 +M V30 15 N -8.5011 2.8689 0.0 0 +M V30 16 C -10.0061 2.8807 0.0 0 +M V30 17 C -7.7721 4.1741 0.0 0 +M V30 18 C -10.7586 4.1858 0.0 0 +M V30 19 C -8.5246 5.4792 0.0 0 +M V30 20 N -10.0179 5.491 0.0 0 +M V30 21 C -10.7704 6.7961 0.0 0 +M V30 22 C -10.0296 8.1013 0.0 0 +M V30 23 C -10.7821 9.4064 0.0 0 +M V30 24 C -8.5481 8.113 0.0 0 +M V30 25 C -10.0414 10.7116 0.0 0 +M V30 26 C -7.8191 9.4182 0.0 0 +M V30 27 C -8.5598 10.7233 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 5 10 +M V30 10 1 7 11 +M V30 11 1 10 12 +M V30 12 2 11 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 20 21 +M V30 21 1 21 22 +M V30 22 2 22 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 2 24 26 +M V30 26 2 25 27 +M V30 27 2 7 10 +M V30 28 1 12 13 +M V30 29 1 19 20 +M V30 30 1 26 27 +M V30 END BOND +M V30 END CTAB +M END +> +376 + +> +Z239083000 + +> +368.433 + +> +1.996 + +> +1 + +> +75.780 + +> +4 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 377 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.4403 -0.4486 0.0 0 +M V30 3 C 0.8736 -1.2042 0.0 0 +M V30 4 N -2.7508 0.3069 0.0 0 +M V30 5 C -1.4521 -1.9362 0.0 0 +M V30 6 C -0.0236 -2.4084 0.0 0 +M V30 7 C 2.2904 -1.6528 0.0 0 +M V30 8 C -4.0613 -0.425 0.0 0 +M V30 9 C -2.7626 -2.68 0.0 0 +M V30 10 C 0.85 -3.6127 0.0 0 +M V30 11 C 2.2786 -3.1404 0.0 0 +M V30 12 N -4.0731 -1.9126 0.0 0 +M V30 13 C -5.3718 0.3305 0.0 0 +M V30 14 O -2.7744 -4.1676 0.0 0 +M V30 15 N -6.6823 -0.4014 0.0 0 +M V30 16 C -7.9928 0.3541 0.0 0 +M V30 17 C -6.6941 -1.8889 0.0 0 +M V30 18 C -9.3033 -0.3777 0.0 0 +M V30 19 C -10.6138 0.3777 0.0 0 +M V30 20 C -11.9243 -0.3541 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 18 19 +M V30 19 1 19 20 +M V30 20 1 5 6 +M V30 21 1 9 12 +M V30 22 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +377 + +> +Z44505227 + +> +291.412 + +> +2.558 + +> +1 + +> +44.700 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3616846 + +> +0.94 + +$$$$ +Compound 378 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2854 0.7547 0.0 0 +M V30 3 N 2.5709 0.0235 0.0 0 +M V30 4 C 1.2736 2.2643 0.0 0 +M V30 5 C 2.5591 -1.4623 0.0 0 +M V30 6 C -0.0353 3.019 0.0 0 +M V30 7 N 1.3326 -2.335 0.0 0 +M V30 8 C 3.762 -2.335 0.0 0 +M V30 9 C -0.0471 4.5286 0.0 0 +M V30 10 C -1.3444 2.2879 0.0 0 +M V30 11 N 1.7807 -3.7502 0.0 0 +M V30 12 C 3.2903 -3.7502 0.0 0 +M V30 13 C -1.3562 5.2952 0.0 0 +M V30 14 C -2.6534 3.0426 0.0 0 +M V30 15 C 4.163 -4.9531 0.0 0 +M V30 16 C -2.6652 4.5522 0.0 0 +M V30 17 C 5.0357 -6.1561 0.0 0 +M V30 18 C 2.9365 -5.8259 0.0 0 +M V30 19 C 5.3659 -4.0569 0.0 0 +M V30 20 C -3.9743 5.3187 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 12 15 +M V30 15 2 13 16 +M V30 16 1 15 17 +M V30 17 1 15 18 +M V30 18 1 15 19 +M V30 19 1 16 20 +M V30 20 2 11 12 +M V30 21 1 14 16 +M V30 END BOND +M V30 END CTAB +M END +> +378 + +> +Z759006690 + +> +271.357 + +> +3.675 + +> +2 + +> +57.780 + +> +4 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL115220 + +> +0.86 + +$$$$ +Compound 379 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 S 4.6462 0.8533 0.0 0 +M V30 3 O 6.1397 0.8652 0.0 0 +M V30 4 O 3.1291 0.8652 0.0 0 +M V30 5 N 4.6344 2.3705 0.0 0 +M V30 6 C 4.6344 -0.64 0.0 0 +M V30 7 C 5.9263 3.1291 0.0 0 +M V30 8 C 5.9263 -1.3867 0.0 0 +M V30 9 C 3.3187 -1.3867 0.0 0 +M V30 10 C 5.9145 4.6462 0.0 0 +M V30 11 C 5.9145 -2.8802 0.0 0 +M V30 12 C 7.2183 -0.6281 0.0 0 +M V30 13 C 3.3069 -2.8802 0.0 0 +M V30 14 N 4.3973 4.6581 0.0 0 +M V30 15 C 5.9026 6.1634 0.0 0 +M V30 16 C 7.4079 4.6581 0.0 0 +M V30 17 N 7.2064 -3.615 0.0 0 +M V30 18 C 4.5988 -3.615 0.0 0 +M V30 19 C 8.5102 -1.3749 0.0 0 +M V30 20 C 8.4984 -2.8565 0.0 0 +M V30 21 C 4.587 -5.1085 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 2 2 4 +M V30 3 1 2 5 +M V30 4 1 2 6 +M V30 5 1 5 7 +M V30 6 1 6 8 +M V30 7 2 6 9 +M V30 8 1 7 10 +M V30 9 1 8 11 +M V30 10 2 8 12 +M V30 11 1 9 13 +M V30 12 1 10 14 +M V30 13 1 10 15 +M V30 14 1 10 16 +M V30 15 2 11 17 +M V30 16 1 11 18 +M V30 17 1 12 19 +M V30 18 1 17 20 +M V30 19 1 18 21 +M V30 20 2 13 18 +M V30 21 2 19 20 +M V30 END BOND +M V30 END CTAB +M END +> +379 + +> +Z1491333825 + +> +293.385 + +> +2.320 + +> +2 + +> +85.080 + +> +3 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 380 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2914 -0.7464 0.0 0 +M V30 3 N 1.2796 -2.2393 0.0 0 +M V30 4 C 2.5829 0.0118 0.0 0 CFG=2 +M V30 5 C -0.0355 -2.9857 0.0 0 +M V30 6 C 2.571 1.5284 0.0 0 +M V30 7 C 3.8743 -0.7345 0.0 0 +M V30 8 C -0.0473 -4.4786 0.0 0 +M V30 9 O 3.8625 2.2867 0.0 0 +M V30 10 C 5.1658 0.0236 0.0 0 +M V30 11 C 1.244 -5.2132 0.0 0 +M V30 12 C -1.3625 -5.2132 0.0 0 +M V30 13 C 5.1539 1.5521 0.0 0 +M V30 14 C 6.4573 -0.7227 0.0 0 +M V30 15 C 1.2322 -6.7061 0.0 0 +M V30 16 C -1.3743 -6.7061 0.0 0 +M V30 17 C 6.4454 2.3104 0.0 0 +M V30 18 C 7.7487 0.0355 0.0 0 +M V30 19 N -0.0829 -7.4525 0.0 0 +M V30 20 C 7.7369 1.5758 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 4 2 CFG=3 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 1 13 17 +M V30 17 2 14 18 +M V30 18 1 15 19 +M V30 19 2 17 20 +M V30 20 2 10 13 +M V30 21 1 16 19 +M V30 22 1 18 20 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 4) +M V30 END COLLECTION +M V30 END CTAB +M END +> +380 + +> +Z1742107738 + +> +272.342 + +> +1.065 + +> +2 + +> +50.360 + +> +3 + +> +ATM + +> + + +> + + +$$$$ +Compound 381 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3102 -0.7318 0.0 0 +M V30 3 N -2.6204 0.0236 0.0 0 +M V30 4 C -1.322 -2.2191 0.0 0 +M V30 5 C -3.9306 -0.7082 0.0 0 +M V30 6 C -2.6322 -2.9627 0.0 0 +M V30 7 C -3.9424 -2.1955 0.0 0 +M V30 8 C -5.2408 0.0472 0.0 0 +M V30 9 C -5.2527 -2.9391 0.0 0 +M V30 10 C -6.5511 -0.6846 0.0 0 +M V30 11 C -6.5629 -2.1837 0.0 0 +M V30 12 C -7.8731 -2.9273 0.0 0 +M V30 13 O -9.1833 -2.1719 0.0 0 +M V30 14 N -7.8849 -4.4146 0.0 0 +M V30 15 C -9.1951 -5.1582 0.0 0 +M V30 16 C -6.5983 -5.1582 0.0 0 +M V30 17 C -9.2069 -6.6455 0.0 0 CFG=1 +M V30 18 C -6.6101 -6.6455 0.0 0 +M V30 19 O -7.9203 -7.3773 0.0 0 +M V30 20 C -10.5172 -7.3773 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 7 9 +M V30 9 2 8 10 +M V30 10 2 9 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 17 20 CFG=1 +M V30 20 1 6 7 +M V30 21 1 10 11 +M V30 22 1 18 19 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 17) +M V30 END COLLECTION +M V30 END CTAB +M END +> +381 + +> +Z212855030 + +> +274.315 + +> +0.914 + +> +1 + +> +58.640 + +> +1 + +> +parp2 + +> + + +> + + +$$$$ +Compound 382 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.513 0.0 0 +M V30 3 N 1.2766 2.2814 0.0 0 +M V30 4 C -1.3239 2.2814 0.0 0 +M V30 5 C 2.5651 1.5367 0.0 0 +M V30 6 C -1.3357 3.7944 0.0 0 +M V30 7 C -2.636 1.5367 0.0 0 +M V30 8 C 3.8535 2.305 0.0 0 +M V30 9 N -2.6478 4.5628 0.0 0 +M V30 10 C -3.9481 2.305 0.0 0 +M V30 11 C 5.142 1.5603 0.0 0 +M V30 12 C -3.9599 3.8181 0.0 0 +M V30 13 C 5.2839 0.0827 0.0 0 +M V30 14 C 6.5014 2.1868 0.0 0 +M V30 15 C 6.7378 -0.2127 0.0 0 +M V30 16 C 4.2673 -1.0165 0.0 0 +M V30 17 N 7.4944 1.0875 0.0 0 +M V30 18 C 7.187 -1.6312 0.0 0 +M V30 19 C 4.7165 -2.435 0.0 0 +M V30 20 C 6.1704 -2.7306 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 11 13 +M V30 13 2 11 14 +M V30 14 2 13 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 2 16 19 +M V30 19 2 18 20 +M V30 20 1 10 12 +M V30 21 1 15 17 +M V30 22 1 19 20 +M V30 END BOND +M V30 END CTAB +M END +> +382 + +> +Z26395443 + +> +265.310 + +> +2.195 + +> +2 + +> +57.780 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL551127 + +> +0.86 + +$$$$ +Compound 383 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.513 0.0 0 +M V30 3 N 1.2766 2.2814 0.0 0 +M V30 4 C -1.3239 2.2814 0.0 0 +M V30 5 C 2.5651 1.5367 0.0 0 +M V30 6 C -2.636 1.5367 0.0 0 +M V30 7 C -1.3357 3.7944 0.0 0 +M V30 8 C 3.8535 2.305 0.0 0 +M V30 9 C -3.9481 2.305 0.0 0 +M V30 10 C -2.6478 4.5628 0.0 0 +M V30 11 C 5.142 1.5603 0.0 0 +M V30 12 N -3.9599 3.8181 0.0 0 +M V30 13 C 5.2839 0.0827 0.0 0 +M V30 14 C 6.5014 2.1868 0.0 0 +M V30 15 C 6.7378 -0.2127 0.0 0 +M V30 16 C 4.2673 -1.0165 0.0 0 +M V30 17 N 7.4944 1.0875 0.0 0 +M V30 18 C 7.187 -1.6312 0.0 0 +M V30 19 C 4.7165 -2.435 0.0 0 +M V30 20 C 6.1704 -2.7306 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 11 13 +M V30 13 2 11 14 +M V30 14 2 13 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 2 16 19 +M V30 19 2 18 20 +M V30 20 1 10 12 +M V30 21 1 15 17 +M V30 22 1 19 20 +M V30 END BOND +M V30 END CTAB +M END +> +383 + +> +Z26395434 + +> +265.310 + +> +2.195 + +> +2 + +> +57.780 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL343641 + +> +0.91 + +$$$$ +Compound 384 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 O 11.1263 -1.6029 0.0 0 +M V30 3 C 10.1127 -0.4832 0.0 0 +M V30 4 N 8.6394 -0.7779 0.0 0 +M V30 5 C 10.5606 0.9546 0.0 0 +M V30 6 C 8.0147 -2.1333 0.0 0 +M V30 7 C 7.5197 0.2357 0.0 0 +M V30 8 C 11.9749 1.4261 0.0 0 +M V30 9 C 9.6648 2.1804 0.0 0 +M V30 10 C 6.5178 -1.9683 0.0 0 +M V30 11 C 6.2114 -0.495 0.0 0 +M V30 12 C 11.9631 2.9348 0.0 0 +M V30 13 C 13.2596 0.6953 0.0 0 +M V30 14 O 10.537 3.4062 0.0 0 +M V30 15 C 5.2095 -2.7108 0.0 0 +M V30 16 C 4.7145 -0.33 0.0 0 +M V30 17 C 13.2479 3.6891 0.0 0 +M V30 18 C 14.5444 1.4497 0.0 0 +M V30 19 N 5.0445 -4.1841 0.0 0 +M V30 20 C 4.0898 -1.6972 0.0 0 +M V30 21 C 14.5326 2.9465 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 1 3 4 +M V30 3 1 3 5 +M V30 4 1 4 6 +M V30 5 1 4 7 +M V30 6 1 5 8 +M V30 7 2 5 9 +M V30 8 1 6 10 +M V30 9 1 7 11 +M V30 10 2 8 12 +M V30 11 1 8 13 +M V30 12 1 9 14 +M V30 13 1 10 15 +M V30 14 1 11 16 +M V30 15 1 12 17 +M V30 16 2 13 18 +M V30 17 1 15 19 +M V30 18 1 15 20 +M V30 19 2 17 21 +M V30 20 1 10 11 +M V30 21 1 12 14 +M V30 22 1 16 20 +M V30 23 1 18 21 +M V30 END BOND +M V30 END CTAB +M END +> +384 + +> +Z1672911866 + +> +270.326 + +> +1.341 + +> +1 + +> +59.470 + +> +1 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 385 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.2298 -0.8701 0.0 0 +M V30 3 C 1.2065 -0.8701 0.0 0 +M V30 4 N -2.7148 -0.5452 0.0 0 +M V30 5 C -0.7773 -2.2855 0.0 0 +M V30 6 C 0.7309 -2.2855 0.0 0 +M V30 7 C 2.6684 -0.5452 0.0 0 +M V30 8 C -3.7358 -1.6474 0.0 0 +M V30 9 C -1.7982 -3.3877 0.0 0 +M V30 10 C 1.7286 -3.3877 0.0 0 +M V30 11 C 3.6662 -1.6474 0.0 0 +M V30 12 N -3.2833 -3.0629 0.0 0 +M V30 13 C -5.2208 -1.3226 0.0 0 +M V30 14 O -1.3458 -4.8031 0.0 0 +M V30 15 C 3.1905 -3.0629 0.0 0 +M V30 16 N -6.2418 -2.4247 0.0 0 +M V30 17 C -5.7893 -3.8402 0.0 0 +M V30 18 C -7.7268 -2.0999 0.0 0 +M V30 19 C -6.8103 -4.9424 0.0 0 +M V30 20 C -8.7478 -3.2021 0.0 0 +M V30 21 O -8.2953 -4.6175 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 10 15 +M V30 15 1 13 16 +M V30 16 1 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 5 6 +M V30 22 1 9 12 +M V30 23 1 11 15 +M V30 24 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +385 + +> +Z44499000 + +> +305.395 + +> +1.443 + +> +1 + +> +53.930 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3616846 + +> +0.88 + +$$$$ +Compound 386 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.2399 -0.8502 0.0 0 +M V30 3 C 1.1809 -0.8975 0.0 0 +M V30 4 N -2.7161 -0.4959 0.0 0 +M V30 5 C -0.8148 -2.2791 0.0 0 +M V30 6 C 0.6731 -2.3028 0.0 0 +M V30 7 C 2.6688 -0.9211 0.0 0 +M V30 8 C -3.7553 -1.5824 0.0 0 +M V30 9 C -1.854 -3.3656 0.0 0 +M V30 10 C 1.854 -3.2003 0.0 0 +M V30 11 C 3.094 -2.35 0.0 0 +M V30 12 N -3.3302 -3.0113 0.0 0 +M V30 13 C -5.2314 -1.2281 0.0 0 +M V30 14 O -1.4289 -4.7945 0.0 0 +M V30 15 N -6.2707 -2.3146 0.0 0 +M V30 16 C -7.7468 -1.9603 0.0 0 +M V30 17 C -5.8455 -3.7435 0.0 0 +M V30 18 C -8.786 -3.0467 0.0 0 +M V30 19 C -6.8847 -4.8299 0.0 0 +M V30 20 C -8.3609 -4.4757 0.0 0 +M V30 21 C -9.4001 -5.5621 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 20 21 +M V30 21 1 5 6 +M V30 22 1 9 12 +M V30 23 1 10 11 +M V30 24 1 19 20 +M V30 END BOND +M V30 END CTAB +M END +> +386 + +> +Z44525557 + +> +303.422 + +> +2.683 + +> +1 + +> +44.700 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3616846 + +> +0.92 + +$$$$ +Compound 387 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5063 0.0 0 +M V30 3 C 1.271 2.2713 0.0 0 +M V30 4 C -1.318 2.2713 0.0 0 +M V30 5 C 1.2592 3.7777 0.0 0 +M V30 6 C 2.5537 1.5299 0.0 0 +M V30 7 C -1.3298 3.7777 0.0 0 +M V30 8 C -2.6243 1.5299 0.0 0 +M V30 9 O -0.047 4.5309 0.0 0 +M V30 10 C 2.542 4.5309 0.0 0 +M V30 11 C 3.8365 2.2948 0.0 0 +M V30 12 C -2.6361 4.5309 0.0 0 +M V30 13 C 2.5302 6.0372 0.0 0 +M V30 14 C 3.8247 3.8012 0.0 0 +M V30 15 C -3.9424 3.8012 0.0 0 +M V30 16 C -2.6479 6.0372 0.0 0 +M V30 17 O 3.813 6.7904 0.0 0 +M V30 18 O 1.2239 6.7904 0.0 0 +M V30 19 C -5.2487 4.5544 0.0 0 +M V30 20 C -3.9542 6.7904 0.0 0 +M V30 21 C -5.2605 6.0608 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 1 5 10 +M V30 10 2 6 11 +M V30 11 1 7 12 +M V30 12 1 10 13 +M V30 13 2 10 14 +M V30 14 2 12 15 +M V30 15 1 12 16 +M V30 16 2 13 17 +M V30 17 1 13 18 +M V30 18 1 15 19 +M V30 19 2 16 20 +M V30 20 2 19 21 +M V30 21 1 7 9 +M V30 22 1 11 14 +M V30 23 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +387 + +> +Z56899057 + +> +280.275 + +> +3.553 + +> +1 + +> +63.600 + +> +2 + +> +Tankyrase-1 + +> +CHEMBL2431806 + +> +0.88 + +$$$$ +Compound 388 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.2946 0.772 0.0 0 +M V30 3 C 1.2827 2.2923 0.0 0 +M V30 4 C 2.5892 0.0237 0.0 0 +M V30 5 C 2.5773 3.0524 0.0 0 +M V30 6 C 3.8838 0.7957 0.0 0 +M V30 7 C 3.8719 2.3041 0.0 0 +M V30 8 N 5.1666 3.0643 0.0 0 +M V30 9 C 6.4612 2.316 0.0 0 +M V30 10 O 6.4493 0.8195 0.0 0 +M V30 11 C 7.7558 3.0762 0.0 0 +M V30 12 C 9.0504 2.3279 0.0 0 +M V30 13 O 9.0385 0.8314 0.0 0 +M V30 14 N 10.345 3.088 0.0 0 +M V30 15 C 11.6397 2.3398 0.0 0 +M V30 16 C 12.9343 3.0999 0.0 0 +M V30 17 C 11.6278 0.8432 0.0 0 +M V30 18 C 14.2289 2.3516 0.0 0 +M V30 19 C 12.9224 0.095 0.0 0 +M V30 20 C 14.217 0.8551 0.0 0 +M V30 21 F 15.5117 0.1068 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 17 19 +M V30 19 2 18 20 +M V30 20 1 20 21 +M V30 21 1 6 7 +M V30 22 1 19 20 +M V30 END BOND +M V30 END CTAB +M END +> +388 + +> +Z56887790 + +> +290.265 + +> +2.867 + +> +2 + +> +58.200 + +> +4 + +> +ATM + +> + + +> + + +$$$$ +Compound 389 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 O 4.166 0.0946 0.0 0 +M V30 3 C 5.4561 0.8521 0.0 0 +M V30 4 N 6.7462 0.1183 0.0 0 +M V30 5 C 5.4443 2.3671 0.0 0 CFG=2 +M V30 6 C 6.7344 -1.3729 0.0 0 +M V30 7 C 6.7344 3.1245 0.0 0 +M V30 8 C 4.1305 3.1245 0.0 0 +M V30 9 C 8.0244 -2.1067 0.0 0 +M V30 10 C 5.4206 -2.1067 0.0 0 +M V30 11 N 6.7225 4.6395 0.0 0 +M V30 12 C 4.1187 4.6395 0.0 0 +M V30 13 C 9.3145 -1.3492 0.0 0 +M V30 14 C 8.0126 -3.5979 0.0 0 +M V30 15 C 5.4088 -3.5979 0.0 0 +M V30 16 C 5.4088 5.3969 0.0 0 +M V30 17 C 9.3027 0.1656 0.0 0 +M V30 18 C 10.6046 -2.083 0.0 0 +M V30 19 C 6.6988 -4.3436 0.0 0 +M V30 20 C 10.5927 0.9231 0.0 0 +M V30 21 C 11.8946 -1.3255 0.0 0 +M V30 22 C 11.8828 0.1893 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 1 3 4 +M V30 3 1 5 3 CFG=1 +M V30 4 1 4 6 +M V30 5 1 5 7 +M V30 6 1 5 8 +M V30 7 2 6 9 +M V30 8 1 6 10 +M V30 9 1 7 11 +M V30 10 1 8 12 +M V30 11 1 9 13 +M V30 12 1 9 14 +M V30 13 2 10 15 +M V30 14 1 11 16 +M V30 15 2 13 17 +M V30 16 1 13 18 +M V30 17 2 14 19 +M V30 18 1 17 20 +M V30 19 2 18 21 +M V30 20 2 20 22 +M V30 21 1 12 16 +M V30 22 1 15 19 +M V30 23 1 21 22 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 5) +M V30 END COLLECTION +M V30 END CTAB +M END +> +389 + +> +Z1449770524 + +> +280.364 + +> +2.385 + +> +2 + +> +41.130 + +> +3 + +> +ATM + +> + + +> + + +$$$$ +Compound 390 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 S 11.8348 -1.221 0.0 0 +M V30 3 C 11.3652 -2.6299 0.0 0 +M V30 4 C 10.6138 -0.3287 0.0 0 +M V30 5 O 12.234 -3.8275 0.0 0 +M V30 6 N 9.8624 -2.6182 0.0 0 +M V30 7 C 9.3927 -1.1975 0.0 0 +M V30 8 C 10.602 1.174 0.0 0 +M V30 9 C 8.97 -3.8158 0.0 0 +M V30 10 O 7.9603 -0.7279 0.0 0 +M V30 11 C 9.2988 1.9372 0.0 0 +M V30 12 C 7.4789 -3.6514 0.0 0 +M V30 13 C 7.9955 1.1975 0.0 0 +M V30 14 C 9.2871 3.4401 0.0 0 +M V30 15 N 6.5866 -4.849 0.0 0 +M V30 16 C 6.6923 1.9607 0.0 0 +M V30 17 C 7.9838 4.1915 0.0 0 +M V30 18 O 5.3891 1.221 0.0 0 +M V30 19 C 6.6806 3.4635 0.0 0 +M V30 20 C 4.0858 1.9842 0.0 0 +M V30 21 O 5.3773 4.215 0.0 0 +M V30 22 C 4.0741 3.487 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 2 3 +M V30 2 1 2 4 +M V30 3 2 3 5 +M V30 4 1 3 6 +M V30 5 1 4 7 +M V30 6 2 4 8 CFG=2 +M V30 7 1 6 9 +M V30 8 2 7 10 +M V30 9 1 8 11 +M V30 10 1 9 12 +M V30 11 2 11 13 +M V30 12 1 11 14 +M V30 13 1 12 15 +M V30 14 1 13 16 +M V30 15 2 14 17 +M V30 16 1 16 18 +M V30 17 2 16 19 +M V30 18 1 18 20 +M V30 19 1 19 21 +M V30 20 1 21 22 +M V30 21 1 6 7 +M V30 22 1 17 19 +M V30 END BOND +M V30 END CTAB +M END +> +390 + +> +Z56855141 + +> +308.353 + +> +1.558 + +> +1 + +> +81.860 + +> +5 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL2003286 + +> +0.86 + +$$$$ +Compound 391 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.3011 0.7619 0.0 0 +M V30 3 C -1.3128 2.2623 0.0 0 +M V30 4 C -2.6023 0.0234 0.0 0 +M V30 5 C -2.614 3.0243 0.0 0 +M V30 6 C -3.9034 0.7853 0.0 0 +M V30 7 C -3.9151 2.2858 0.0 0 +M V30 8 C -5.2163 3.0477 0.0 0 +M V30 9 N -6.5175 2.3092 0.0 0 +M V30 10 N -5.228 4.5481 0.0 0 +M V30 11 C -7.8186 3.0712 0.0 0 +M V30 12 C -6.5292 5.2984 0.0 0 +M V30 13 C -7.8303 4.5716 0.0 0 +M V30 14 C -9.1198 2.3327 0.0 0 +M V30 15 O -6.5409 6.7988 0.0 0 +M V30 16 C -9.1315 5.3218 0.0 0 +M V30 17 C -10.4209 3.0946 0.0 0 +M V30 18 C -10.4327 4.595 0.0 0 +M V30 19 C -11.7221 2.3561 0.0 0 +M V30 20 O -11.7338 0.8791 0.0 0 +M V30 21 O -13.0232 3.118 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 2 8 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 2 12 15 +M V30 15 1 13 16 +M V30 16 2 14 17 +M V30 17 2 16 18 +M V30 18 1 17 19 +M V30 19 2 19 20 +M V30 20 1 19 21 +M V30 21 1 6 7 +M V30 22 1 12 13 +M V30 23 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +391 + +> +Z364012620 + +> +300.697 + +> +3.213 + +> +2 + +> +78.760 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1163173 + +> +0.88 + +$$$$ +Compound 392 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C -1.3011 0.7619 0.0 0 +M V30 3 C -1.3128 2.2623 0.0 0 +M V30 4 C -2.6023 0.0234 0.0 0 +M V30 5 C -2.614 3.0243 0.0 0 +M V30 6 C -3.9034 0.7853 0.0 0 +M V30 7 C -3.9151 2.2858 0.0 0 +M V30 8 C -5.2163 3.0477 0.0 0 +M V30 9 N -6.5175 2.3092 0.0 0 +M V30 10 N -5.228 4.5481 0.0 0 +M V30 11 C -7.8186 3.0712 0.0 0 +M V30 12 C -6.5292 5.2984 0.0 0 +M V30 13 C -7.8303 4.5716 0.0 0 +M V30 14 C -9.1198 2.3327 0.0 0 +M V30 15 O -6.5409 6.7988 0.0 0 +M V30 16 C -9.1315 5.3218 0.0 0 +M V30 17 C -10.4209 3.0946 0.0 0 +M V30 18 C -10.4327 4.595 0.0 0 +M V30 19 C -11.7221 2.3561 0.0 0 +M V30 20 O -11.7338 0.8791 0.0 0 +M V30 21 O -13.0232 3.118 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 2 8 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 2 12 15 +M V30 15 1 13 16 +M V30 16 2 14 17 +M V30 17 2 16 18 +M V30 18 1 17 19 +M V30 19 2 19 20 +M V30 20 1 19 21 +M V30 21 1 6 7 +M V30 22 1 12 13 +M V30 23 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +392 + +> +Z364012662 + +> +345.148 + +> +3.363 + +> +2 + +> +78.760 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL2058057 + +> +0.87 + +$$$$ +Compound 393 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.2298 -0.8701 0.0 0 +M V30 3 C 1.2065 -0.8701 0.0 0 +M V30 4 N -2.7148 -0.5452 0.0 0 +M V30 5 C -0.7773 -2.2855 0.0 0 +M V30 6 C 0.7309 -2.2855 0.0 0 +M V30 7 C 2.6684 -0.5452 0.0 0 +M V30 8 C -3.7358 -1.6474 0.0 0 +M V30 9 C -1.7982 -3.3877 0.0 0 +M V30 10 C 1.7286 -3.3877 0.0 0 +M V30 11 C 3.6662 -1.6474 0.0 0 +M V30 12 N -3.2833 -3.0629 0.0 0 +M V30 13 C -5.2208 -1.3226 0.0 0 +M V30 14 O -1.3458 -4.8031 0.0 0 +M V30 15 C 3.1905 -3.0629 0.0 0 +M V30 16 N -6.2418 -2.4247 0.0 0 +M V30 17 C -5.7893 -3.8402 0.0 0 +M V30 18 C -7.7268 -2.0999 0.0 0 +M V30 19 C -6.8103 -4.9424 0.0 0 +M V30 20 C -8.7478 -3.2021 0.0 0 +M V30 21 N -8.2953 -4.6175 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 10 15 +M V30 15 1 13 16 +M V30 16 1 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 5 6 +M V30 22 1 9 12 +M V30 23 1 11 15 +M V30 24 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +393 + +> +Z57931108 + +> +304.411 + +> +1.429 + +> +2 + +> +56.730 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3616846 + +> +0.92 + +$$$$ +Compound 394 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.3014 0.7503 0.0 0 +M V30 3 C -1.3131 2.251 0.0 0 +M V30 4 C -2.6028 0.0234 0.0 0 +M V30 5 C -2.6145 3.0131 0.0 0 +M V30 6 C -3.9042 0.7738 0.0 0 +M V30 7 C -3.9159 2.2745 0.0 0 +M V30 8 O -5.2173 3.0366 0.0 0 +M V30 9 C -6.5187 2.2979 0.0 0 +M V30 10 C -7.8201 3.06 0.0 0 +M V30 11 O -7.8319 4.5607 0.0 0 +M V30 12 N -9.1215 2.3214 0.0 0 +M V30 13 C -10.4229 3.0835 0.0 0 +M V30 14 C -11.7244 2.3448 0.0 0 +M V30 15 C -10.4347 4.5842 0.0 0 +M V30 16 C -13.0258 3.1069 0.0 0 +M V30 17 C -11.7361 5.3346 0.0 0 +M V30 18 C -14.3272 2.3683 0.0 0 +M V30 19 C -13.0375 4.6076 0.0 0 +M V30 20 O -14.3389 0.891 0.0 0 +M V30 21 C -15.6286 3.1304 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 1 9 10 +M V30 10 2 10 11 +M V30 11 1 10 12 +M V30 12 1 12 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 1 16 18 +M V30 18 2 16 19 +M V30 19 2 18 20 +M V30 20 1 18 21 +M V30 21 1 6 7 +M V30 22 1 17 19 +M V30 END BOND +M V30 END CTAB +M END +> +394 + +> +Z19727554 + +> +303.740 + +> +3.439 + +> +1 + +> +55.400 + +> +5 + +> +parp14 + +> + + +> + + +$$$$ +Compound 395 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.2925 0.7707 0.0 0 +M V30 3 C 1.2806 2.2886 0.0 0 +M V30 4 C 2.585 0.0237 0.0 0 +M V30 5 C 2.5732 3.0594 0.0 0 +M V30 6 C 3.8776 0.7944 0.0 0 +M V30 7 C 3.8657 2.3123 0.0 0 +M V30 8 C 5.1583 3.0831 0.0 0 +M V30 9 N 6.4508 2.336 0.0 0 +M V30 10 C 7.7433 3.1068 0.0 0 +M V30 11 C 9.0359 2.3597 0.0 0 +M V30 12 N 10.3284 3.1305 0.0 0 +M V30 13 N 9.024 0.8656 0.0 0 +M V30 14 C 11.621 2.3834 0.0 0 +M V30 15 C 10.3166 0.1185 0.0 0 +M V30 16 C 11.6091 0.8893 0.0 0 +M V30 17 C 12.9135 3.1542 0.0 0 +M V30 18 O 10.3047 -1.3755 0.0 0 +M V30 19 C 12.9017 0.1422 0.0 0 +M V30 20 C 14.2061 2.4072 0.0 0 +M V30 21 C 14.1942 0.913 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 1 9 10 +M V30 10 1 10 11 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 2 19 21 +M V30 21 1 6 7 +M V30 22 1 15 16 +M V30 23 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +395 + +> +Z44531315 + +> +283.300 + +> +0.974 + +> +2 + +> +53.490 + +> +4 + +> +parp10 + +> + + +> + + +$$$$ +Compound 396 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4517 -1.4131 0.0 0 +M V30 3 N 1.9111 -1.7142 0.0 0 +M V30 4 C -0.5675 -2.5135 0.0 0 +M V30 5 C -2.0501 -2.1891 0.0 0 +M V30 6 C -0.1158 -3.9266 0.0 0 +M V30 7 C -3.0695 -3.2895 0.0 0 +M V30 8 C -1.1351 -5.027 0.0 0 +M V30 9 N -4.5521 -2.9652 0.0 0 +M V30 10 C -2.6177 -4.7027 0.0 0 +M V30 11 C -5.5714 -4.0656 0.0 0 +M V30 12 O -5.1196 -5.4787 0.0 0 +M V30 13 C -7.054 -3.7413 0.0 0 +M V30 14 C -8.0733 -4.8417 0.0 0 +M V30 15 C -9.0926 -3.7181 0.0 0 +M V30 16 C -9.3011 -5.7104 0.0 0 +M V30 17 C -6.8687 -5.7104 0.0 0 +M V30 18 O -10.5753 -4.0193 0.0 0 +M V30 19 O -8.6409 -2.2818 0.0 0 +M V30 20 C -8.8494 -7.1235 0.0 0 +M V30 21 C -7.3436 -7.1235 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 2 7 10 +M V30 10 1 9 11 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 1 15 19 +M V30 19 1 16 20 +M V30 20 1 17 21 +M V30 21 1 8 10 +M V30 22 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +396 + +> +Z221346202 + +> +290.314 + +> +1.213 + +> +3 + +> +109.490 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3414766 + +> +0.92 + +$$$$ +Compound 397 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4943 0.0 0 +M V30 3 N -1.3283 -2.2297 0.0 0 +M V30 4 C 1.2808 -2.2297 0.0 0 +M V30 5 N -1.3401 -3.724 0.0 0 +M V30 6 C 1.269 -3.724 0.0 0 +M V30 7 C 2.5736 -1.4706 0.0 0 +M V30 8 C -0.0474 -4.4712 0.0 0 +M V30 9 C 2.5617 -4.4712 0.0 0 +M V30 10 C 3.8664 -2.2059 0.0 0 +M V30 11 C -0.0593 -5.9656 0.0 0 +M V30 12 C 3.8545 -3.7122 0.0 0 +M V30 13 O 1.2334 -6.7128 0.0 0 +M V30 14 N -1.3757 -6.7128 0.0 0 +M V30 15 C -1.3876 -8.2072 0.0 0 CFG=2 +M V30 16 C -2.7041 -8.9425 0.0 0 +M V30 17 C -0.0948 -8.9425 0.0 0 +M V30 18 O -4.0917 -8.3139 0.0 0 +M V30 19 C -2.8701 -10.425 0.0 0 +M V30 20 C -5.1117 -9.4169 0.0 0 +M V30 21 C -4.3526 -10.7215 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 15 14 CFG=1 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 16 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 6 8 +M V30 22 1 10 12 +M V30 23 2 20 21 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 15) +M V30 END COLLECTION +M V30 END CTAB +M END +> +397 + +> +Z89606264 + +> +283.282 + +> +0.645 + +> +2 + +> +83.700 + +> +3 + +> +parp2 + +> + + +> + + +$$$$ +Compound 398 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4972 0.0 0 +M V30 3 N -1.3308 -2.2339 0.0 0 +M V30 4 C 1.2833 -2.2339 0.0 0 +M V30 5 N -1.3427 -3.7312 0.0 0 +M V30 6 C 1.2714 -3.7312 0.0 0 +M V30 7 C 2.5785 -1.4734 0.0 0 +M V30 8 C -0.0475 -4.4798 0.0 0 +M V30 9 C 2.5667 -4.4798 0.0 0 +M V30 10 C 3.8738 -2.2102 0.0 0 +M V30 11 C -0.0594 -5.9771 0.0 0 +M V30 12 C 3.8619 -3.7193 0.0 0 +M V30 13 O -1.3784 -6.7257 0.0 0 +M V30 14 N 1.2358 -6.7257 0.0 0 +M V30 15 C 1.2239 -8.2229 0.0 0 +M V30 16 C 2.531 -5.9533 0.0 0 +M V30 17 C 2.5191 -8.9597 0.0 0 +M V30 18 O 2.6617 -10.445 0.0 0 +M V30 19 C 3.8857 -8.3299 0.0 0 +M V30 20 C 4.1233 -10.7421 0.0 0 +M V30 21 C 4.8838 -9.435 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 17 18 +M V30 18 2 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 6 8 +M V30 22 1 10 12 +M V30 23 2 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +398 + +> +Z32456027 + +> +283.282 + +> +0.103 + +> +1 + +> +74.910 + +> +3 + +> +parp2 + +> + + +> + + +$$$$ +Compound 399 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.512 0.0 0 +M V30 3 N 1.2758 2.2799 0.0 0 +M V30 4 C -1.323 2.2799 0.0 0 CFG=1 +M V30 5 C 2.5634 1.5357 0.0 0 +M V30 6 N -1.3348 3.792 0.0 0 +M V30 7 C -2.6343 1.5357 0.0 0 +M V30 8 C 2.5516 0.0472 0.0 0 +M V30 9 C 3.851 2.3035 0.0 0 +M V30 10 C -3.9455 2.3035 0.0 0 +M V30 11 C 3.8392 -0.6851 0.0 0 +M V30 12 C 5.1387 1.5593 0.0 0 +M V30 13 C -5.2568 1.5593 0.0 0 +M V30 14 C -3.9573 3.8156 0.0 0 +M V30 15 C 5.1268 0.0708 0.0 0 +M V30 16 C 4.1345 -2.1381 0.0 0 +M V30 17 C -6.568 2.3271 0.0 0 +M V30 18 C -5.2686 4.5716 0.0 0 +M V30 19 N 6.2255 -0.9214 0.0 0 +M V30 20 N 5.6112 -2.2799 0.0 0 +M V30 21 C -6.5799 3.8392 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 CFG=3 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 2 11 15 +M V30 15 1 11 16 +M V30 16 1 13 17 +M V30 17 2 14 18 +M V30 18 1 15 19 +M V30 19 2 16 20 +M V30 20 2 17 21 +M V30 21 1 12 15 +M V30 22 1 18 21 +M V30 23 1 19 20 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 4) +M V30 END COLLECTION +M V30 END CTAB +M END +> +399 + +> +Z445431132 + +> +280.324 + +> +2.046 + +> +3 + +> +83.800 + +> +4 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 400 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 O 3.7919 0.7583 0.0 0 +M V30 3 C 4.2422 -0.6635 0.0 0 +M V30 4 N 3.3416 -1.8722 0.0 0 +M V30 5 C 5.6642 -1.1138 0.0 0 +M V30 6 C 4.2185 -3.0809 0.0 0 +M V30 7 C 5.6523 -2.6069 0.0 0 +M V30 8 C 6.9558 -0.3554 0.0 0 +M V30 9 O 3.7445 -4.5029 0.0 0 +M V30 10 C 6.944 -3.3535 0.0 0 +M V30 11 C 8.2474 -1.102 0.0 0 +M V30 12 C 8.2356 -2.5951 0.0 0 +M V30 13 N 9.5391 -0.3436 0.0 0 +M V30 14 C 9.5272 1.1731 0.0 0 +M V30 15 O 8.2119 1.9315 0.0 0 +M V30 16 C 10.8189 1.9315 0.0 0 +M V30 17 C 12.1105 1.1849 0.0 0 +M V30 18 C 10.807 3.4483 0.0 0 +M V30 19 C 13.4021 1.9433 0.0 0 +M V30 20 C 12.0986 4.2066 0.0 0 +M V30 21 C 13.3903 3.4601 0.0 0 +M V30 22 N 14.6819 4.2185 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 1 3 4 +M V30 3 1 3 5 +M V30 4 1 4 6 +M V30 5 2 5 7 +M V30 6 1 5 8 +M V30 7 2 6 9 +M V30 8 1 7 10 +M V30 9 2 8 11 +M V30 10 2 10 12 +M V30 11 1 11 13 +M V30 12 1 13 14 +M V30 13 2 14 15 +M V30 14 1 14 16 +M V30 15 1 16 17 +M V30 16 1 16 18 +M V30 17 1 17 19 +M V30 18 1 18 20 +M V30 19 1 19 21 +M V30 20 1 21 22 +M V30 21 1 6 7 +M V30 22 1 11 12 +M V30 23 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +400 + +> +Z1436476353 + +> +287.314 + +> +0.536 + +> +3 + +> +101.290 + +> +2 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL541259 + +> +0.85 + +$$$$ +Compound 401 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5076 0.0 0 +M V30 3 N -1.3192 2.2732 0.0 0 +M V30 4 C 1.272 2.2732 0.0 0 +M V30 5 C -1.3309 3.7809 0.0 0 +M V30 6 C 1.2603 3.7809 0.0 0 +M V30 7 C 2.5559 1.5312 0.0 0 +M V30 8 N -2.6384 4.5465 0.0 0 +M V30 9 C -0.0471 4.5465 0.0 0 +M V30 10 C 2.5441 4.5465 0.0 0 +M V30 11 C 3.8398 2.2968 0.0 0 +M V30 12 C -3.9458 3.8044 0.0 0 +M V30 13 C 3.828 3.8044 0.0 0 +M V30 14 C -3.9576 2.3203 0.0 0 +M V30 15 C -5.2532 4.57 0.0 0 +M V30 16 C -5.265 1.5783 0.0 0 +M V30 17 C -6.5606 3.828 0.0 0 +M V30 18 C -5.2768 0.0942 0.0 0 +M V30 19 C -6.5724 2.3439 0.0 0 +M V30 20 O -3.9929 -0.6478 0.0 0 +M V30 21 O -6.5842 -0.6478 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 8 12 +M V30 12 2 10 13 +M V30 13 2 12 14 +M V30 14 1 12 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 1 16 18 +M V30 18 2 16 19 +M V30 19 2 18 20 +M V30 20 1 18 21 +M V30 21 1 6 9 +M V30 22 1 11 13 +M V30 23 1 17 19 +M V30 END BOND +M V30 END CTAB +M END +> +401 + +> +Z608408108 + +> +280.278 + +> +2.694 + +> +3 + +> +78.430 + +> +3 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 402 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.507 0.0 0 +M V30 3 N -1.3186 2.2605 0.0 0 +M V30 4 C 1.2715 2.2605 0.0 0 +M V30 5 C -1.3304 3.7675 0.0 0 +M V30 6 C 1.2597 3.7675 0.0 0 +M V30 7 C 2.5548 1.5305 0.0 0 +M V30 8 N -0.047 4.5328 0.0 0 +M V30 9 C -2.6372 4.5328 0.0 0 +M V30 10 C 2.543 4.5328 0.0 0 +M V30 11 C 3.8381 2.284 0.0 0 +M V30 12 N -3.9441 3.791 0.0 0 +M V30 13 C 3.8264 3.791 0.0 0 +M V30 14 C 2.5313 6.0398 0.0 0 +M V30 15 C -5.251 4.5563 0.0 0 +M V30 16 C -3.9559 2.3076 0.0 0 +M V30 17 C -6.5578 3.8146 0.0 0 +M V30 18 C -2.6725 1.5776 0.0 0 +M V30 19 C -7.8647 4.5799 0.0 0 +M V30 20 O -9.1715 3.8381 0.0 0 +M V30 21 O -7.8765 6.0869 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 1 12 15 +M V30 15 1 12 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 19 20 +M V30 20 1 19 21 +M V30 21 1 6 8 +M V30 22 1 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +402 + +> +Z728862468 + +> +289.330 + +> +-1.198 + +> +2 + +> +82.000 + +> +6 + +> +parp1 + +> + + +> + + +$$$$ +Compound 403 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5097 0.0 0 +M V30 3 N -1.321 2.2764 0.0 0 +M V30 4 C 1.2738 2.2764 0.0 0 +M V30 5 C -1.3328 3.7862 0.0 0 +M V30 6 C 1.262 3.7862 0.0 0 +M V30 7 C 2.5595 1.5333 0.0 0 +M V30 8 N -0.0471 4.5528 0.0 0 +M V30 9 C -2.642 4.5528 0.0 0 +M V30 10 C 2.5477 4.5528 0.0 0 +M V30 11 C 3.8451 2.3 0.0 0 +M V30 12 N -3.9513 3.8098 0.0 0 +M V30 13 C 3.8333 3.8098 0.0 0 +M V30 14 C -4.1164 2.3354 0.0 0 CFG=2 +M V30 15 C -5.3313 4.4349 0.0 0 +M V30 16 C -5.5908 2.0405 0.0 0 +M V30 17 C -3.0195 1.3446 0.0 0 +M V30 18 C -6.3457 3.3379 0.0 0 +M V30 19 C -3.3379 -0.1061 0.0 0 +M V30 20 O -4.7769 -0.5543 0.0 0 +M V30 21 O -2.241 -1.0969 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 1 12 15 +M V30 15 1 14 16 +M V30 16 1 14 17 CFG=1 +M V30 17 1 15 18 +M V30 18 1 17 19 +M V30 19 2 19 20 +M V30 20 1 19 21 +M V30 21 1 6 8 +M V30 22 1 11 13 +M V30 23 1 16 18 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 14) +M V30 END COLLECTION +M V30 END CTAB +M END +> +403 + +> +Z808569596 + +> +287.314 + +> +-1.694 + +> +2 + +> +82.000 + +> +4 + +> +parp1 + +> + + +> + + +$$$$ +Compound 404 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.2969 -0.7495 0.0 0 +M V30 3 F 0.5235 -2.0465 0.0 0 +M V30 4 F 2.0346 0.5711 0.0 0 +M V30 5 C 2.5938 -1.4872 0.0 0 +M V30 6 C 3.8907 -0.7257 0.0 0 +M V30 7 C 2.5819 -2.9864 0.0 0 +M V30 8 C 5.1876 -1.4634 0.0 0 +M V30 9 C 3.8788 -3.7241 0.0 0 +M V30 10 C 5.1757 -2.9626 0.0 0 +M V30 11 C 6.4726 -3.7003 0.0 0 +M V30 12 N 7.7695 -2.9388 0.0 0 +M V30 13 N 6.4607 -5.1995 0.0 0 +M V30 14 C 9.0664 -3.6765 0.0 0 +M V30 15 C 7.7576 -5.9491 0.0 0 +M V30 16 C 9.0545 -5.1757 0.0 0 +M V30 17 C 10.3633 -2.915 0.0 0 +M V30 18 O 7.7457 -7.4483 0.0 0 +M V30 19 C 10.3514 -5.9253 0.0 0 +M V30 20 C 11.6603 -3.6527 0.0 0 +M V30 21 C 11.6484 -5.1519 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 2 5 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 2 8 10 +M V30 10 1 10 11 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 1 16 19 +M V30 19 1 17 20 +M V30 20 1 19 21 +M V30 21 1 9 10 +M V30 22 1 15 16 +M V30 23 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +404 + +> +Z813022034 + +> +294.272 + +> +3.291 + +> +1 + +> +41.460 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL2419891 + +> +1.0 + +$$$$ +Compound 405 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.896 1.2261 0.0 0 +M V30 3 C 1.4147 0.4715 0.0 0 +M V30 4 N -0.0235 2.4523 0.0 0 +M V30 5 N -2.4051 1.2379 0.0 0 +M V30 6 C 1.403 1.9807 0.0 0 +M V30 7 C 2.6999 -0.2711 0.0 0 +M V30 8 C -3.1597 -0.0471 0.0 0 +M V30 9 C 2.6881 2.747 0.0 0 +M V30 10 C 3.985 0.4951 0.0 0 +M V30 11 O -2.4287 -1.3322 0.0 0 +M V30 12 C -4.6688 -0.0353 0.0 0 +M V30 13 C 3.9732 2.016 0.0 0 +M V30 14 C -5.4351 -1.3204 0.0 0 +M V30 15 C -5.4351 1.2733 0.0 0 +M V30 16 C -4.6924 -2.6055 0.0 0 +M V30 17 C -6.9443 -1.3086 0.0 0 +M V30 18 C -6.9443 1.2851 0.0 0 +M V30 19 O -3.2068 -2.5937 0.0 0 +M V30 20 O -5.4587 -3.8906 0.0 0 +M V30 21 C -7.7106 0.0 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 1 12 14 +M V30 14 1 12 15 +M V30 15 1 14 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 2 16 19 +M V30 19 1 16 20 +M V30 20 1 17 21 +M V30 21 1 4 6 +M V30 22 1 10 13 +M V30 23 1 18 21 +M V30 END BOND +M V30 END CTAB +M END +> +405 + +> +Z283696416 + +> +308.396 + +> +3.154 + +> +2 + +> +79.290 + +> +3 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL448447 + +> +0.87 + +$$$$ +Compound 406 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4901 0.0 0 +M V30 3 N -1.3245 -2.2234 0.0 0 +M V30 4 C 1.2772 -2.2234 0.0 0 +M V30 5 N -1.3364 -3.7135 0.0 0 +M V30 6 C 1.2654 -3.7135 0.0 0 +M V30 7 C 2.5663 -1.4665 0.0 0 +M V30 8 C -0.0473 -4.4586 0.0 0 +M V30 9 C 2.5545 -4.4586 0.0 0 +M V30 10 C 3.8555 -2.1997 0.0 0 +M V30 11 C -0.0591 -5.9488 0.0 0 +M V30 12 C 3.8436 -3.6899 0.0 0 +M V30 13 O 1.2299 -6.6939 0.0 0 +M V30 14 N -1.3718 -6.6939 0.0 0 +M V30 15 C -2.6846 -5.9251 0.0 0 +M V30 16 C -3.9974 -6.6702 0.0 0 +M V30 17 N -5.3101 -5.9015 0.0 0 +M V30 18 C -6.6229 -6.6466 0.0 0 +M V30 19 C -5.322 -4.3877 0.0 0 +M V30 20 C -7.9357 -5.8778 0.0 0 +M V30 21 C -4.0329 -3.6189 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 6 8 +M V30 22 1 10 12 +M V30 END BOND +M V30 END CTAB +M END +> +406 + +> +Z31448011 + +> +288.345 + +> +0.661 + +> +2 + +> +73.800 + +> +6 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3105422 + +> +0.85 + +$$$$ +Compound 407 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4879 0.0 0 +M V30 3 N -1.3226 -2.2201 0.0 0 +M V30 4 C 1.2753 -2.2201 0.0 0 CFG=1 +M V30 5 C -2.6334 -1.4643 0.0 0 +M V30 6 O 1.2635 -3.708 0.0 0 +M V30 7 C 2.5625 -1.4643 0.0 0 +M V30 8 C -3.9442 -2.1965 0.0 0 +M V30 9 C 2.5507 -4.452 0.0 0 +M V30 10 N 3.8497 -2.1965 0.0 0 +M V30 11 N -5.255 -1.4407 0.0 0 +M V30 12 C 3.8379 -3.6844 0.0 0 +M V30 13 C -5.4203 0.059 0.0 0 +M V30 14 C -6.6367 -2.0429 0.0 0 +M V30 15 C -6.8965 0.3778 0.0 0 +M V30 16 C -4.4284 1.1809 0.0 0 +M V30 17 N -7.6523 -0.9211 0.0 0 +M V30 18 C -6.9555 -3.4955 0.0 0 +M V30 19 C -7.3689 1.8186 0.0 0 +M V30 20 C -4.9007 2.6216 0.0 0 +M V30 21 C -6.3769 2.9404 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 4 2 CFG=3 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 1 9 12 +M V30 12 1 11 13 +M V30 13 1 11 14 +M V30 14 2 13 15 +M V30 15 1 13 16 +M V30 16 2 14 17 +M V30 17 1 14 18 +M V30 18 1 15 19 +M V30 19 2 16 20 +M V30 20 2 19 21 +M V30 21 1 10 12 +M V30 22 1 15 17 +M V30 23 1 20 21 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 4) +M V30 END COLLECTION +M V30 END CTAB +M END +> +407 + +> +Z854100722 + +> +288.345 + +> +0.899 + +> +2 + +> +68.180 + +> +4 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 408 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 Cl 3.5785 0.0941 0.0 0 +M V30 3 O 2.0718 -8.8522 0.0 0 +M V30 4 C 2.06 -7.3455 0.0 0 +M V30 5 N 0.7533 -6.5921 0.0 0 +M V30 6 C 3.3431 -6.5921 0.0 0 +M V30 7 C 0.7416 -5.0853 0.0 0 +M V30 8 C 3.3313 -5.0853 0.0 0 +M V30 9 C 4.6262 -7.3219 0.0 0 +M V30 10 N 2.0247 -4.3202 0.0 0 +M V30 11 C -0.565 -4.3202 0.0 0 +M V30 12 C 4.6144 -4.3202 0.0 0 +M V30 13 C 5.9093 -6.5685 0.0 0 +M V30 14 N -1.8716 -5.0618 0.0 0 +M V30 15 C 5.8976 -5.0618 0.0 0 +M V30 16 C -3.1783 -4.2966 0.0 0 +M V30 17 C -1.8834 -6.545 0.0 0 +M V30 18 C -4.485 -5.0382 0.0 0 CFG=2 +M V30 19 C -3.1901 -7.2748 0.0 0 +M V30 20 O -4.4967 -6.5215 0.0 0 +M V30 21 C -5.7916 -4.2731 0.0 0 +M V30 22 O -5.8034 -2.7663 0.0 0 +M V30 23 O -7.0983 -5.0147 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 3 4 +M V30 2 1 4 5 +M V30 3 1 4 6 +M V30 4 1 5 7 +M V30 5 2 6 8 +M V30 6 1 6 9 +M V30 7 2 7 10 +M V30 8 1 7 11 +M V30 9 1 8 12 +M V30 10 2 9 13 +M V30 11 1 11 14 +M V30 12 2 12 15 +M V30 13 1 14 16 +M V30 14 1 14 17 +M V30 15 1 16 18 +M V30 16 1 17 19 +M V30 17 1 18 20 +M V30 18 1 18 21 CFG=3 +M V30 19 2 21 22 +M V30 20 1 21 23 +M V30 21 1 8 10 +M V30 22 1 13 15 +M V30 23 1 19 20 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 18) +M V30 END COLLECTION +M V30 END CTAB +M END +> +408 + +> +Z1268152288 + +> +289.287 + +> +-2.621 + +> +2 + +> +91.230 + +> +3 + +> +parp10 + +> + + +> + + +$$$$ +Compound 409 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O 1.4916 0.0118 0.0 0 +M V30 3 O -1.5153 0.0118 0.0 0 +M V30 4 N -0.0118 -1.4916 0.0 0 +M V30 5 C -0.0118 1.5153 0.0 0 +M V30 6 C 1.2785 -2.2375 0.0 0 +M V30 7 C -1.3259 -2.2375 0.0 0 +M V30 8 C 1.2785 2.273 0.0 0 +M V30 9 C -1.3259 2.273 0.0 0 +M V30 10 C 1.2667 -3.7291 0.0 0 CFG=1 +M V30 11 C -1.3377 -3.7291 0.0 0 +M V30 12 C 1.2667 3.7883 0.0 0 +M V30 13 C 2.569 1.539 0.0 0 +M V30 14 C -1.3377 3.7883 0.0 0 +M V30 15 N 2.5571 -4.4631 0.0 0 +M V30 16 C -0.0473 -4.4631 0.0 0 +M V30 17 C 2.5571 4.546 0.0 0 +M V30 18 C -0.0473 4.546 0.0 0 +M V30 19 C 3.8594 2.2967 0.0 0 +M V30 20 C 2.5453 -5.9548 0.0 0 +M V30 21 N 3.8475 3.812 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 2 8 13 +M V30 13 1 9 14 +M V30 14 1 10 15 CFG=1 +M V30 15 1 10 16 +M V30 16 2 12 17 +M V30 17 1 12 18 +M V30 18 1 13 19 +M V30 19 1 15 20 +M V30 20 1 17 21 +M V30 21 1 11 16 +M V30 22 2 14 18 +M V30 23 2 19 21 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 10) +M V30 END COLLECTION +M V30 END CTAB +M END +> +409 + +> +Z992564392 + +> +305.395 + +> +1.454 + +> +1 + +> +62.300 + +> +2 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL38380 + +> +0.9 + +$$$$ +Compound 410 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 N 0.4495 -1.4197 0.0 0 +M V30 3 C 1.2068 0.8991 0.0 0 +M V30 4 C 1.9403 -1.4079 0.0 0 +M V30 5 N 2.4136 0.0236 0.0 0 +M V30 6 C 1.1949 2.4136 0.0 0 +M V30 7 C 2.8159 -2.6147 0.0 0 +M V30 8 C 2.4846 3.1708 0.0 0 +M V30 9 C -0.1183 3.1708 0.0 0 +M V30 10 N 1.5026 -3.3483 0.0 0 +M V30 11 C 3.9162 -1.5972 0.0 0 +M V30 12 C 3.4193 -3.9754 0.0 0 +M V30 13 C 2.4727 4.6853 0.0 0 +M V30 14 C 3.7742 2.4254 0.0 0 +M V30 15 C -0.1301 4.6853 0.0 0 +M V30 16 C 5.2058 -2.3426 0.0 0 +M V30 17 C 4.8982 -3.8097 0.0 0 +M V30 18 N 3.7624 5.4425 0.0 0 +M V30 19 C 1.1594 5.4425 0.0 0 +M V30 20 C 5.0639 3.1826 0.0 0 +M V30 21 C 5.052 4.7089 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 2 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 6 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 1 7 11 +M V30 11 1 7 12 +M V30 12 1 8 13 +M V30 13 2 8 14 +M V30 14 1 9 15 +M V30 15 1 11 16 +M V30 16 1 12 17 +M V30 17 2 13 18 +M V30 18 1 13 19 +M V30 19 1 14 20 +M V30 20 1 18 21 +M V30 21 1 4 5 +M V30 22 2 15 19 +M V30 23 1 16 17 +M V30 24 2 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +410 + +> +Z1021452788 + +> +280.324 + +> +1.893 + +> +1 + +> +77.830 + +> +2 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 411 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2918 -0.7348 0.0 0 +M V30 3 N 2.5836 0.0237 0.0 0 +M V30 4 C 1.2799 -2.2281 0.0 0 +M V30 5 C 2.5718 1.5407 0.0 0 +M V30 6 C -0.0355 -2.9629 0.0 0 +M V30 7 C 2.5718 -2.9629 0.0 0 +M V30 8 C -0.0474 -4.4562 0.0 0 +M V30 9 C 2.5599 -4.4562 0.0 0 +M V30 10 N -1.3629 -5.191 0.0 0 +M V30 11 C 1.2444 -5.191 0.0 0 +M V30 12 C -1.3747 -6.6843 0.0 0 +M V30 13 O -0.0829 -7.4309 0.0 0 +M V30 14 C -2.6903 -7.4309 0.0 0 +M V30 15 C -2.7021 -8.9243 0.0 0 +M V30 16 C -2.714 -10.4176 0.0 0 +M V30 17 C -4.2191 -8.9124 0.0 0 +M V30 18 C -1.2088 -8.9124 0.0 0 +M V30 19 C -4.0295 -11.1642 0.0 0 +M V30 20 O -4.0414 -12.6575 0.0 0 +M V30 21 O -5.345 -10.3939 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 2 8 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 2 19 20 +M V30 20 1 19 21 +M V30 21 1 9 11 +M V30 END BOND +M V30 END CTAB +M END +> +411 + +> +Z169864344 + +> +292.330 + +> +1.129 + +> +3 + +> +95.500 + +> +6 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3414770 + +> +0.88 + +$$$$ +Compound 412 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 S 5.3897 0.1891 0.0 0 +M V30 3 O 6.879 0.2009 0.0 0 +M V30 4 O 3.8768 0.2009 0.0 0 +M V30 5 N 5.3779 -1.3001 0.0 0 +M V30 6 C 5.3779 1.702 0.0 0 +M V30 7 C 6.5835 -2.1748 0.0 0 +M V30 8 C 4.1487 -2.1748 0.0 0 +M V30 9 C 6.6663 2.4584 0.0 0 +M V30 10 C 4.0659 2.4584 0.0 0 +M V30 11 C 6.1107 -3.5931 0.0 0 CFG=2 +M V30 12 C 4.5978 -3.5931 0.0 0 +M V30 13 C 6.6544 3.9714 0.0 0 +M V30 14 C 7.9546 1.7138 0.0 0 +M V30 15 C 4.0541 3.9714 0.0 0 +M V30 16 C 6.9854 -4.7987 0.0 0 +M V30 17 C 7.9428 4.7278 0.0 0 +M V30 18 C 5.3424 4.7278 0.0 0 +M V30 19 C 9.2429 2.4703 0.0 0 +M V30 20 N 6.3589 -6.158 0.0 0 +M V30 21 N 9.2311 3.995 0.0 0 +M V30 22 C 7.2336 -7.3636 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 2 2 4 +M V30 3 1 2 5 +M V30 4 1 2 6 +M V30 5 1 5 7 +M V30 6 1 5 8 +M V30 7 1 6 9 +M V30 8 2 6 10 +M V30 9 1 7 11 +M V30 10 1 8 12 +M V30 11 1 9 13 +M V30 12 2 9 14 +M V30 13 1 10 15 +M V30 14 1 11 16 CFG=1 +M V30 15 2 13 17 +M V30 16 1 13 18 +M V30 17 1 14 19 +M V30 18 1 16 20 +M V30 19 1 17 21 +M V30 20 1 20 22 +M V30 21 1 11 12 +M V30 22 2 15 18 +M V30 23 2 19 21 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 11) +M V30 END COLLECTION +M V30 END CTAB +M END +> +412 + +> +Z1491299765 + +> +305.395 + +> +0.539 + +> +1 + +> +62.300 + +> +3 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL2420922 + +> +0.9 + +$$$$ +Compound 413 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 S 8.1234 -0.0944 0.0 0 +M V30 3 O 9.6111 -0.0826 0.0 0 +M V30 4 O 6.6121 -0.0826 0.0 0 +M V30 5 N 8.1116 -1.5821 0.0 0 +M V30 6 C 8.1116 1.4168 0.0 0 +M V30 7 C 6.801 -2.3142 0.0 0 CFG=2 +M V30 8 C 9.3986 2.1725 0.0 0 +M V30 9 C 6.801 2.1725 0.0 0 +M V30 10 C 5.4904 -1.5585 0.0 0 +M V30 11 C 6.7892 -3.8019 0.0 0 +M V30 12 C 9.3868 3.6838 0.0 0 +M V30 13 C 10.6856 1.4404 0.0 0 +M V30 14 C 6.7892 3.6838 0.0 0 +M V30 15 N 4.1798 -2.2906 0.0 0 +M V30 16 C 5.4786 -4.5458 0.0 0 +M V30 17 C 10.6738 4.4395 0.0 0 +M V30 18 C 8.0762 4.4395 0.0 0 +M V30 19 C 11.9726 2.1961 0.0 0 +M V30 20 C 5.4668 -6.0335 0.0 0 +M V30 21 C 4.1679 -3.7901 0.0 0 +M V30 22 N 11.9608 3.7075 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 2 2 4 +M V30 3 1 2 5 +M V30 4 1 2 6 +M V30 5 1 7 5 CFG=1 +M V30 6 1 6 8 +M V30 7 2 6 9 +M V30 8 1 7 10 +M V30 9 1 7 11 +M V30 10 1 8 12 +M V30 11 2 8 13 +M V30 12 1 9 14 +M V30 13 1 10 15 +M V30 14 1 11 16 +M V30 15 2 12 17 +M V30 16 1 12 18 +M V30 17 1 13 19 +M V30 18 1 16 20 +M V30 19 1 16 21 +M V30 20 1 17 22 +M V30 21 2 14 18 +M V30 22 2 19 22 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 7) +M V30 END COLLECTION +M V30 END CTAB +M END +> +413 + +> +Z1491315233 + +> +307.411 + +> +2.669 + +> +2 + +> +85.080 + +> +5 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 414 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4879 0.0 0 +M V30 3 N -1.3226 -2.2201 0.0 0 +M V30 4 C 1.2753 -2.2201 0.0 0 +M V30 5 C -2.6334 -1.4643 0.0 0 +M V30 6 C 2.5625 -1.4643 0.0 0 +M V30 7 C 1.2635 -3.708 0.0 0 +M V30 8 C -3.9442 -2.1965 0.0 0 +M V30 9 C 3.8497 -2.1965 0.0 0 +M V30 10 C 2.5507 -4.452 0.0 0 +M V30 11 N -5.255 -1.4407 0.0 0 +M V30 12 C 3.8379 -3.6844 0.0 0 +M V30 13 C -5.4203 0.059 0.0 0 +M V30 14 C -6.6367 -2.0429 0.0 0 +M V30 15 C -6.8965 0.3778 0.0 0 +M V30 16 C -4.4284 1.1809 0.0 0 +M V30 17 N -7.6523 -0.9211 0.0 0 +M V30 18 C -6.9555 -3.4955 0.0 0 +M V30 19 C -7.3689 1.8186 0.0 0 +M V30 20 C -4.9007 2.6216 0.0 0 +M V30 21 C -6.3769 2.9404 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 11 13 +M V30 13 1 11 14 +M V30 14 2 13 15 +M V30 15 1 13 16 +M V30 16 2 14 17 +M V30 17 1 14 18 +M V30 18 1 15 19 +M V30 19 2 16 20 +M V30 20 2 19 21 +M V30 21 1 10 12 +M V30 22 1 15 17 +M V30 23 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +414 + +> +Z102413326 + +> +279.336 + +> +2.684 + +> +1 + +> +46.920 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL294060 + +> +0.85 + +$$$$ +Compound 415 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4488 1.4409 0.0 0 +M V30 3 N -0.4488 2.6692 0.0 0 +M V30 4 C 1.8661 1.9133 0.0 0 +M V30 5 C 0.4251 3.8976 0.0 0 +M V30 6 C -1.9606 2.681 0.0 0 +M V30 7 C 1.8543 3.4251 0.0 0 +M V30 8 C 3.1535 1.1692 0.0 0 +M V30 9 O -0.0472 5.3385 0.0 0 +M V30 10 C 3.1417 4.1929 0.0 0 +M V30 11 C 4.4409 1.937 0.0 0 +M V30 12 C 4.4291 3.4606 0.0 0 +M V30 13 N 5.7283 1.1929 0.0 0 +M V30 14 C 7.0157 1.9606 0.0 0 +M V30 15 O 7.0039 3.4724 0.0 0 +M V30 16 C 8.3031 1.2165 0.0 0 +M V30 17 C 9.5905 1.9842 0.0 0 +M V30 18 C 8.2913 -0.2716 0.0 0 +M V30 19 C 10.8779 1.2401 0.0 0 +M V30 20 C 9.5787 -1.0157 0.0 0 +M V30 21 C 10.8661 -0.248 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 2 10 12 +M V30 12 1 11 13 +M V30 13 1 13 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 2 19 21 +M V30 21 1 5 7 +M V30 22 1 11 12 +M V30 23 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +415 + +> +Z27627050 + +> +280.278 + +> +2.491 + +> +1 + +> +66.480 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL589506 + +> +0.87 + +$$$$ +Compound 416 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.0169 1.1233 0.0 0 +M V30 3 N -2.495 0.8277 0.0 0 +M V30 4 C -0.5675 2.5659 0.0 0 +M V30 5 C -2.968 -0.5912 0.0 0 +M V30 6 N -1.4662 3.7957 0.0 0 +M V30 7 C 0.8513 3.0389 0.0 0 +M V30 8 C -1.9747 -1.6909 0.0 0 +M V30 9 C -4.4461 -0.8868 0.0 0 +M V30 10 N -0.5912 5.0255 0.0 0 +M V30 11 C 0.8395 4.5525 0.0 0 +M V30 12 C 2.1402 2.294 0.0 0 +M V30 13 C -2.4477 -3.1099 0.0 0 +M V30 14 C -4.9191 -2.3058 0.0 0 +M V30 15 C 2.1284 5.3211 0.0 0 +M V30 16 C 3.4291 3.0626 0.0 0 +M V30 17 O -1.4544 -4.2096 0.0 0 +M V30 18 C -3.9258 -3.4055 0.0 0 +M V30 19 C 3.4173 4.5761 0.0 0 +M V30 20 O -4.3988 -4.8245 0.0 0 +M V30 21 C -3.4055 -5.9242 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 7 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 11 15 +M V30 15 2 12 16 +M V30 16 1 13 17 +M V30 17 2 13 18 +M V30 18 2 15 19 +M V30 19 1 18 20 +M V30 20 1 20 21 +M V30 21 1 10 11 +M V30 22 1 14 18 +M V30 23 1 16 19 +M V30 END BOND +M V30 END CTAB +M END +> +416 + +> +Z32805840 + +> +283.282 + +> +2.200 + +> +3 + +> +87.240 + +> +3 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL2007002 + +> +0.9 + +$$$$ +Compound 417 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3123 -0.733 0.0 0 +M V30 3 N -2.6247 0.0236 0.0 0 +M V30 4 C -1.3242 -2.2227 0.0 0 +M V30 5 C -2.6366 1.537 0.0 0 +M V30 6 C -3.9371 -0.7094 0.0 0 +M V30 7 C -0.0354 -2.9558 0.0 0 +M V30 8 C -2.6366 -2.9558 0.0 0 +M V30 9 C -0.0472 -4.4455 0.0 0 +M V30 10 C -2.6484 -4.4455 0.0 0 +M V30 11 C -1.3596 -5.1786 0.0 0 +M V30 12 N -1.3715 -6.6683 0.0 0 +M V30 13 C -2.6839 -7.4132 0.0 0 +M V30 14 O -3.9963 -6.6565 0.0 0 +M V30 15 C -2.6957 -8.903 0.0 0 +M V30 16 C -4.0081 -9.6478 0.0 0 +M V30 17 C -5.3205 -10.3809 0.0 0 +M V30 18 C -4.7648 -8.3354 0.0 0 +M V30 19 C -3.2632 -10.9366 0.0 0 +M V30 20 O -5.3323 -11.8706 0.0 0 +M V30 21 O -6.6329 -9.6242 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 1 7 9 +M V30 9 2 8 10 +M V30 10 2 9 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 1 16 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 17 21 +M V30 21 1 10 11 +M V30 END BOND +M V30 END CTAB +M END +> +417 + +> +Z1213002382 + +> +292.330 + +> +0.586 + +> +2 + +> +86.710 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3414766 + +> +0.88 + +$$$$ +Compound 418 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 0.1421 -1.4802 0.0 0 +M V30 3 C -1.4802 0.3197 0.0 0 +M V30 4 N -1.2434 -2.0842 0.0 0 +M V30 5 C 1.4328 -2.2263 0.0 0 +M V30 6 C -2.2381 -0.971 0.0 0 +M V30 7 N 1.421 -3.7184 0.0 0 +M V30 8 C -3.7421 -1.1131 0.0 0 +M V30 9 C 2.7118 -4.4526 0.0 0 +M V30 10 O -4.3697 -2.475 0.0 0 +M V30 11 O -4.6421 0.1184 0.0 0 +M V30 12 O 4.0026 -3.6947 0.0 0 +M V30 13 C 2.7 -5.9447 0.0 0 +M V30 14 C 1.3855 -6.6789 0.0 0 +M V30 15 C 3.9907 -6.6789 0.0 0 +M V30 16 N -0.0592 -6.2052 0.0 0 +M V30 17 C 1.3736 -8.171 0.0 0 +M V30 18 C 3.9789 -8.171 0.0 0 +M V30 19 C -0.9592 -7.4131 0.0 0 +M V30 20 C 2.6644 -8.9052 0.0 0 +M V30 21 C -0.071 -8.621 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 1 7 9 +M V30 9 2 8 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 17 21 +M V30 21 1 4 6 +M V30 22 1 18 20 +M V30 23 2 19 21 +M V30 END BOND +M V30 END CTAB +M END +> +418 + +> +Z1274460559 + +> +301.320 + +> +1.700 + +> +3 + +> +95.080 + +> +4 + +> +ATM + +> + + +> + + +$$$$ +Compound 419 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.4789 -0.1419 0.0 0 +M V30 3 C 2.0823 -1.5025 0.0 0 +M V30 4 C 2.3544 1.0884 0.0 0 +M V30 5 F 1.1831 -2.7093 0.0 0 +M V30 6 C 3.5612 -1.6445 0.0 0 +M V30 7 C 3.8333 0.9465 0.0 0 +M V30 8 C 4.4367 -0.414 0.0 0 +M V30 9 Cl 4.7088 2.1769 0.0 0 +M V30 10 C 5.9156 -0.556 0.0 0 +M V30 11 O 6.519 -1.9166 0.0 0 +M V30 12 N 6.7912 0.6743 0.0 0 +M V30 13 C 8.2701 0.5324 0.0 0 +M V30 14 C 9.1456 1.7628 0.0 0 +M V30 15 S 10.6364 1.7747 0.0 0 +M V30 16 N 8.6724 3.2063 0.0 0 +M V30 17 C 11.086 3.2181 0.0 0 +M V30 18 C 9.8792 4.1054 0.0 0 +M V30 19 C 9.8673 5.6199 0.0 0 +M V30 20 O 8.554 6.3771 0.0 0 +M V30 21 O 11.1569 6.3771 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 2 10 11 +M V30 11 1 10 12 +M V30 12 1 12 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 2 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 18 19 +M V30 19 2 19 20 +M V30 20 1 19 21 +M V30 21 1 7 8 +M V30 22 2 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +419 + +> +Z1274460621 + +> +349.165 + +> +2.553 + +> +2 + +> +79.290 + +> +4 + +> +ATM + +> + + +> + + +$$$$ +Compound 420 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.2249 -0.8716 0.0 0 +M V30 3 C -0.4711 1.437 0.0 0 +M V30 4 N -2.4499 0.0235 0.0 0 +M V30 5 C -1.2367 -2.3557 0.0 0 +M V30 6 C -1.9788 1.4487 0.0 0 +M V30 7 N -2.5442 -3.0978 0.0 0 +M V30 8 C -2.874 2.6737 0.0 0 +M V30 9 C -2.5559 -4.5819 0.0 0 +M V30 10 O -4.3699 2.5324 0.0 0 +M V30 11 O -2.2733 4.0518 0.0 0 +M V30 12 O -1.2721 -5.3122 0.0 0 +M V30 13 C -3.8634 -5.3122 0.0 0 +M V30 14 C -5.1708 -4.5583 0.0 0 +M V30 15 C -3.8752 -6.7963 0.0 0 +M V30 16 C -6.4783 -5.2886 0.0 0 +M V30 17 C -5.1826 -7.5266 0.0 0 +M V30 18 C -6.49 -6.7727 0.0 0 +M V30 19 C -7.9153 -4.8175 0.0 0 +M V30 20 C -7.9271 -7.2203 0.0 0 +M V30 21 C -8.8105 -6.0189 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 1 7 9 +M V30 9 2 8 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 4 6 +M V30 22 1 17 18 +M V30 23 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +420 + +> +Z1274461585 + +> +302.348 + +> +2.531 + +> +2 + +> +79.290 + +> +4 + +> +ATM + +> + + +> + + +$$$$ +Compound 421 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.0136 1.1197 0.0 0 +M V30 3 C -0.5657 2.5576 0.0 0 +M V30 4 C -2.4869 0.825 0.0 0 +M V30 5 C -1.5794 3.6774 0.0 0 +M V30 6 C 0.8839 2.8759 0.0 0 +M V30 7 C -3.5006 1.9447 0.0 0 +M V30 8 C -3.0527 3.3827 0.0 0 +M V30 9 N 1.3318 4.3138 0.0 0 +M V30 10 C 0.4361 5.5396 0.0 0 +M V30 11 C 2.7462 4.7853 0.0 0 +M V30 12 N 1.3083 6.7655 0.0 0 +M V30 13 N -1.0725 5.5514 0.0 0 +M V30 14 C 4.031 4.0545 0.0 0 +M V30 15 C 2.7344 6.294 0.0 0 +M V30 16 O 4.0192 2.5694 0.0 0 +M V30 17 N 5.3157 4.8089 0.0 0 +M V30 18 N 4.0192 7.0483 0.0 0 +M V30 19 C 5.3039 6.3176 0.0 0 +M V30 20 C 4.0074 8.557 0.0 0 +M V30 21 O 6.5887 7.0719 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 1 9 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 1 10 13 +M V30 13 1 11 14 +M V30 14 2 11 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 1 7 8 +M V30 22 1 12 15 +M V30 23 1 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +421 + +> +Z223813986 + +> +305.720 + +> +1.299 + +> +2 + +> +93.250 + +> +2 + +> +parp1 + +> + + +> + + +$$$$ +Compound 422 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3135 0.7573 0.0 0 +M V30 3 C 1.2899 0.7573 0.0 0 +M V30 4 N -1.3254 2.2721 0.0 0 +M V30 5 N -2.6271 0.0236 0.0 0 +M V30 6 C 2.5798 0.0236 0.0 0 +M V30 7 C -2.6389 3.0294 0.0 0 +M V30 8 C -3.9407 0.781 0.0 0 +M V30 9 N 3.8697 0.781 0.0 0 +M V30 10 N 2.5679 -1.4674 0.0 0 +M V30 11 N -2.6508 4.5442 0.0 0 +M V30 12 C -3.9525 2.2957 0.0 0 +M V30 13 C -5.2542 0.0473 0.0 0 +M V30 14 C 5.1596 0.0473 0.0 0 +M V30 15 C 3.8578 -2.2129 0.0 0 +M V30 16 C 5.1477 -1.4437 0.0 0 +M V30 17 C 6.4495 0.8047 0.0 0 +M V30 18 O 3.846 -3.704 0.0 0 +M V30 19 C 6.4376 -2.1892 0.0 0 +M V30 20 C 7.7394 0.071 0.0 0 +M V30 21 C 7.7275 -1.4319 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 7 12 +M V30 12 1 8 13 +M V30 13 1 9 14 +M V30 14 1 10 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 2 19 21 +M V30 21 1 8 12 +M V30 22 1 15 16 +M V30 23 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +422 + +> +Z74373015 + +> +299.351 + +> +1.108 + +> +2 + +> +93.260 + +> +3 + +> +parp10, parp3 + +> + + +> + + +$$$$ +Compound 423 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.508 0.0 0 +M V30 3 C -1.3195 2.2621 0.0 0 +M V30 4 C 1.2724 2.2621 0.0 0 +M V30 5 O -2.6273 1.5198 0.0 0 +M V30 6 C -1.3313 3.7701 0.0 0 +M V30 7 C 1.2606 3.7701 0.0 0 +M V30 8 C 2.427 1.3431 0.0 0 +M V30 9 O -2.6391 4.5242 0.0 0 +M V30 10 C -0.0471 4.5242 0.0 0 +M V30 11 C 2.4152 4.7127 0.0 0 CFG=2 +M V30 12 C 3.8762 1.6847 0.0 0 +M V30 13 C 2.0735 6.1854 0.0 0 +M V30 14 C 3.8644 4.3946 0.0 0 +M V30 15 N 4.5124 3.0396 0.0 0 +M V30 16 C 3.1575 7.2104 0.0 0 +M V30 17 C 0.6362 6.6331 0.0 0 +M V30 18 C 2.8158 8.6831 0.0 0 +M V30 19 C 0.2945 8.1058 0.0 0 +M V30 20 C 1.3784 9.1308 0.0 0 +M V30 21 O 1.0367 10.6035 0.0 0 +M V30 22 S -7.6345 4.6184 0.0 0 +M V30 23 O -7.6463 6.1265 0.0 0 +M V30 24 O -6.15 4.6302 0.0 0 +M V30 25 O -9.1426 4.6302 0.0 0 +M V30 26 C -7.6463 3.1339 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 1 6 9 +M V30 9 2 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 11 13 CFG=3 +M V30 13 1 11 14 +M V30 14 1 12 15 +M V30 15 2 13 16 +M V30 16 1 13 17 +M V30 17 1 16 18 +M V30 18 2 17 19 +M V30 19 2 18 20 +M V30 20 1 20 21 +M V30 21 2 22 23 +M V30 22 2 22 24 +M V30 23 1 22 25 +M V30 24 1 22 26 +M V30 25 1 7 10 +M V30 26 1 14 15 +M V30 27 1 19 20 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 11) +M V30 END COLLECTION +M V30 END CTAB +M END +> +423 + +> +Z2210694608 + +> +305.756 + +> +2.400 + +> +4 + +> +72.720 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL417002 + +> +1.0 + +$$$$ +Compound 424 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.2374 -0.8485 0.0 0 +M V30 3 C 1.1784 -0.8956 0.0 0 +M V30 4 N -2.7105 -0.4949 0.0 0 +M V30 5 C -0.8131 -2.2745 0.0 0 +M V30 6 C 0.6717 -2.298 0.0 0 +M V30 7 C 2.6634 -0.9192 0.0 0 +M V30 8 C -3.7476 -1.5791 0.0 0 +M V30 9 C -1.8502 -3.3587 0.0 0 +M V30 10 C 1.8502 -3.1937 0.0 0 +M V30 11 C 3.0876 -2.3452 0.0 0 +M V30 12 N -3.3233 -3.0051 0.0 0 +M V30 13 C -5.2207 -1.2256 0.0 0 +M V30 14 O -1.4259 -4.7846 0.0 0 +M V30 15 N -5.6685 0.2239 0.0 0 +M V30 16 C -7.1652 0.4713 0.0 0 +M V30 17 C -4.5725 1.2492 0.0 0 +M V30 18 C -7.9195 1.7795 0.0 0 +M V30 19 C -4.6904 2.7576 0.0 0 +M V30 20 C -7.3773 3.1819 0.0 0 +M V30 21 C -5.9396 3.6179 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 5 6 +M V30 22 1 9 12 +M V30 23 1 10 11 +M V30 24 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +424 + +> +Z44490247 + +> +303.422 + +> +2.723 + +> +1 + +> +44.700 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3616846 + +> +0.94 + +$$$$ +Compound 425 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 N -1.3117 0.7681 0.0 0 CHG=1 +M V30 3 O -2.6234 0.0236 0.0 0 CHG=-1 +M V30 4 C -1.3235 2.2807 0.0 0 +M V30 5 C -0.0354 3.0489 0.0 0 +M V30 6 C -2.6352 3.0489 0.0 0 +M V30 7 C -0.0472 4.5615 0.0 0 +M V30 8 C -2.6471 4.5615 0.0 0 +M V30 9 N 1.2408 5.3296 0.0 0 +M V30 10 C -1.359 5.3296 0.0 0 +M V30 11 C 2.5289 4.5851 0.0 0 +M V30 12 N 2.5171 3.0961 0.0 0 +M V30 13 C 3.817 5.3533 0.0 0 +M V30 14 C 3.8052 2.3516 0.0 0 +M V30 15 C 5.1051 4.6088 0.0 0 +M V30 16 O 3.7934 0.8626 0.0 0 +M V30 17 C 5.0933 3.1198 0.0 0 +M V30 18 C 6.3932 5.3769 0.0 0 +M V30 19 C 6.3814 2.3753 0.0 0 +M V30 20 C 7.6813 4.6324 0.0 0 +M V30 21 C 7.6695 3.1434 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 2 7 10 +M V30 10 1 9 11 +M V30 11 1 11 12 +M V30 12 2 11 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 2 19 21 +M V30 21 1 8 10 +M V30 22 2 15 17 +M V30 23 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +425 + +> +Z1787218695 + +> +281.266 + +> +2.870 + +> +2 + +> +84.270 + +> +3 + +> +parp10 + +> + + +> + + +$$$$ +Compound 426 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4909 0.0 0 +M V30 3 N -1.3252 -2.2245 0.0 0 +M V30 4 C 1.2779 -2.2245 0.0 0 +M V30 5 C -1.337 -3.7154 0.0 0 CFG=2 +M V30 6 C 1.266 -3.7154 0.0 0 +M V30 7 C 2.5676 -1.4672 0.0 0 +M V30 8 N -0.0473 -4.4609 0.0 0 +M V30 9 C -2.6505 -4.4609 0.0 0 +M V30 10 C 2.5558 -4.4609 0.0 0 +M V30 11 C 3.8574 -2.2008 0.0 0 +M V30 12 C -3.9639 -3.6917 0.0 0 +M V30 13 C -2.6623 -5.9518 0.0 0 +M V30 14 C 3.8456 -3.6917 0.0 0 +M V30 15 C -5.2773 -4.4372 0.0 0 +M V30 16 C -3.9757 -6.6972 0.0 0 +M V30 17 C -5.2891 -5.9281 0.0 0 +M V30 18 C -6.6026 -6.6736 0.0 0 +M V30 19 C -7.916 -7.4072 0.0 0 +M V30 20 C -5.8689 -7.9633 0.0 0 +M V30 21 C -7.3717 -5.3601 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 CFG=3 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 12 15 +M V30 15 2 13 16 +M V30 16 2 15 17 +M V30 17 1 17 18 +M V30 18 1 18 19 +M V30 19 1 18 20 +M V30 20 1 18 21 +M V30 21 1 6 8 +M V30 22 1 11 14 +M V30 23 1 16 17 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 5) +M V30 END COLLECTION +M V30 END CTAB +M END +> +426 + +> +Z48978226 + +> +280.364 + +> +4.146 + +> +2 + +> +41.130 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3594136 + +> +0.97 + +$$$$ +Compound 427 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4942 0.0 0 +M V30 3 N 1.2807 -2.2294 0.0 0 +M V30 4 C -1.3281 -2.2294 0.0 0 +M V30 5 C 2.5733 -1.4704 0.0 0 +M V30 6 C -1.34 -3.7236 0.0 0 +M V30 7 C -2.6444 -1.4704 0.0 0 +M V30 8 C 3.8659 -2.2057 0.0 0 +M V30 9 C -2.6563 -4.4707 0.0 0 +M V30 10 C -3.9608 -2.2057 0.0 0 +M V30 11 C 5.1585 -1.4467 0.0 0 +M V30 12 C -3.9726 -3.6999 0.0 0 +M V30 13 C -2.6682 -5.9649 0.0 0 +M V30 14 C 6.5223 -2.0515 0.0 0 +M V30 15 C 5.3008 0.0592 0.0 0 +M V30 16 C 7.5184 -0.9249 0.0 0 +M V30 17 C 6.9729 -3.4746 0.0 0 +M V30 18 N 6.7594 0.3794 0.0 0 +M V30 19 C 8.977 -1.2214 0.0 0 +M V30 20 C 8.4315 -3.771 0.0 0 +M V30 21 C 9.4276 -2.6444 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 1 11 14 +M V30 14 2 11 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 2 19 21 +M V30 21 1 10 12 +M V30 22 1 16 18 +M V30 23 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +427 + +> +Z26395676 + +> +278.348 + +> +3.447 + +> +2 + +> +44.890 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL343641 + +> +0.94 + +$$$$ +Compound 428 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4933 0.0 0 +M V30 3 N 1.28 -2.2281 0.0 0 +M V30 4 C -1.3274 -2.2281 0.0 0 +M V30 5 C 2.5719 -1.4696 0.0 0 +M V30 6 C -2.643 -1.4696 0.0 0 +M V30 7 C -1.3392 -3.7215 0.0 0 +M V30 8 C 3.8637 -2.2044 0.0 0 +M V30 9 C -3.9586 -2.2044 0.0 0 +M V30 10 C -2.6548 -4.4682 0.0 0 +M V30 11 C 5.1556 -1.4459 0.0 0 +M V30 12 C -3.9704 -3.6978 0.0 0 +M V30 13 C 6.5186 -2.0504 0.0 0 +M V30 14 C 5.2978 0.0592 0.0 0 +M V30 15 C -5.286 -4.4445 0.0 0 +M V30 16 C 7.5142 -0.9244 0.0 0 +M V30 17 C 6.969 -3.4726 0.0 0 +M V30 18 N 6.7556 0.3792 0.0 0 +M V30 19 C 8.972 -1.2207 0.0 0 +M V30 20 C 8.4268 -3.7689 0.0 0 +M V30 21 C 9.4224 -2.643 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 11 13 +M V30 13 2 11 14 +M V30 14 1 12 15 +M V30 15 2 13 16 +M V30 16 1 13 17 +M V30 17 1 14 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 2 19 21 +M V30 21 1 10 12 +M V30 22 1 16 18 +M V30 23 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +428 + +> +Z26395518 + +> +278.348 + +> +3.447 + +> +2 + +> +44.890 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL343641 + +> +0.92 + +$$$$ +Compound 429 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5074 0.0 0 +M V30 3 C -1.3189 2.2728 0.0 0 +M V30 4 C 1.2718 2.2728 0.0 0 +M V30 5 O -2.6261 1.5309 0.0 0 +M V30 6 C -1.3307 3.7802 0.0 0 +M V30 7 C 1.26 3.7802 0.0 0 +M V30 8 C 2.5555 1.5309 0.0 0 +M V30 9 O -0.0471 4.5339 0.0 0 +M V30 10 C -2.6379 4.5339 0.0 0 +M V30 11 C 2.5437 4.5339 0.0 0 +M V30 12 O 2.5437 0.0471 0.0 0 +M V30 13 C 3.8391 2.2964 0.0 0 +M V30 14 C -2.6497 6.0414 0.0 0 +M V30 15 C -3.9451 3.8038 0.0 0 +M V30 16 C 3.8274 3.8038 0.0 0 +M V30 17 C -3.9569 6.7951 0.0 0 +M V30 18 C -5.2523 4.5575 0.0 0 +M V30 19 O 5.111 4.5575 0.0 0 +M V30 20 C -5.2641 6.0649 0.0 0 +M V30 21 O -6.5713 6.8186 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 1 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 2 8 13 +M V30 13 2 10 14 +M V30 14 1 10 15 +M V30 15 2 11 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 20 21 +M V30 21 1 7 9 +M V30 22 1 13 16 +M V30 23 1 18 20 +M V30 END BOND +M V30 END CTAB +M END +> +429 + +> +Z57183373 + +> +286.236 + +> +2.100 + +> +4 + +> +107.220 + +> +1 + +> +Tankyrase-1 + +> +CHEMBL144625 + +> +0.9 + +$$$$ +Compound 430 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2835 0.7654 0.0 0 +M V30 3 O 2.567 0.0235 0.0 0 +M V30 4 C 1.2717 2.2726 0.0 0 +M V30 5 C -0.0353 3.0263 0.0 0 +M V30 6 C 2.5553 3.0263 0.0 0 +M V30 7 N -1.3424 2.2962 0.0 0 +M V30 8 C -0.0471 4.5336 0.0 0 +M V30 9 C 2.5435 4.5336 0.0 0 +M V30 10 C -2.6495 3.0498 0.0 0 +M V30 11 C 1.2364 5.299 0.0 0 +M V30 12 O 3.827 5.299 0.0 0 +M V30 13 O -2.6612 4.5571 0.0 0 +M V30 14 C -3.9566 2.3197 0.0 0 +M V30 15 O 1.2246 6.8063 0.0 0 +M V30 16 C 5.1106 4.5571 0.0 0 +M V30 17 C -3.9683 0.836 0.0 0 +M V30 18 C -5.2636 3.0734 0.0 0 +M V30 19 C -0.0824 7.5599 0.0 0 +M V30 20 C -5.2754 0.0942 0.0 0 +M V30 21 C -6.5707 2.3433 0.0 0 +M V30 22 C -6.5825 0.8596 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 2 14 17 +M V30 17 1 14 18 +M V30 18 1 15 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 2 20 22 +M V30 22 1 9 11 +M V30 23 1 21 22 +M V30 END BOND +M V30 END CTAB +M END +> +430 + +> +Z57161388 + +> +301.294 + +> +2.967 + +> +2 + +> +84.860 + +> +5 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1997495 + +> +0.98 + +$$$$ +Compound 431 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4467 1.4341 0.0 0 +M V30 3 C 1.8573 1.9043 0.0 0 +M V30 4 C -0.4467 2.6567 0.0 0 +M V30 5 C 1.8456 3.409 0.0 0 +M V30 6 C 3.1386 1.1637 0.0 0 +M V30 7 O 0.4231 3.8792 0.0 0 +M V30 8 C -1.9513 2.6684 0.0 0 +M V30 9 C 3.1269 4.1731 0.0 0 +M V30 10 C 4.42 1.9278 0.0 0 +M V30 11 C -2.7037 3.9733 0.0 0 +M V30 12 C 4.4082 3.4443 0.0 0 +M V30 13 C -4.2084 3.985 0.0 0 +M V30 14 C -1.9749 5.2781 0.0 0 +M V30 15 O 5.6896 4.2084 0.0 0 +M V30 16 O -4.9725 2.7037 0.0 0 +M V30 17 C -4.9725 5.2899 0.0 0 +M V30 18 C -2.7272 6.583 0.0 0 +M V30 19 C -6.4772 2.7155 0.0 0 +M V30 20 O -6.4772 5.3016 0.0 0 +M V30 21 C -4.2319 6.5947 0.0 0 +M V30 22 C -7.2413 6.6065 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 4 8 CFG=2 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 12 15 +M V30 15 1 13 16 +M V30 16 1 13 17 +M V30 17 2 14 18 +M V30 18 1 16 19 +M V30 19 1 17 20 +M V30 20 2 17 21 +M V30 21 1 20 22 +M V30 22 1 5 7 +M V30 23 1 10 12 +M V30 24 1 18 21 +M V30 END BOND +M V30 END CTAB +M END +> +431 + +> +Z196385656 + +> +298.290 + +> +3.435 + +> +1 + +> +64.990 + +> +3 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3290471 + +> +0.86 + +$$$$ +Compound 432 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3034 0.7515 0.0 0 +M V30 3 O -2.6069 0.0234 0.0 0 +M V30 4 C -1.3152 2.2546 0.0 0 +M V30 5 C -0.0352 3.0061 0.0 0 +M V30 6 C -2.6186 3.0061 0.0 0 +M V30 7 C -0.0469 4.5092 0.0 0 +M V30 8 C -2.6304 4.5092 0.0 0 +M V30 9 C -1.3504 5.2608 0.0 0 +M V30 10 N -1.3621 6.7639 0.0 0 +M V30 11 C -2.6656 7.5272 0.0 0 +M V30 12 O -3.9691 6.7874 0.0 0 +M V30 13 C -2.6773 9.0302 0.0 0 +M V30 14 C -3.9808 9.7818 0.0 0 +M V30 15 C -1.3974 9.7818 0.0 0 +M V30 16 C -3.9925 11.2849 0.0 0 +M V30 17 C -1.4091 11.2849 0.0 0 +M V30 18 O -5.296 12.0364 0.0 0 +M V30 19 C -2.7126 12.0364 0.0 0 +M V30 20 C -5.3077 13.5395 0.0 0 +M V30 21 O -2.7243 13.5395 0.0 0 +M V30 22 C -4.0278 14.2911 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 2 7 9 +M V30 9 1 9 10 +M V30 10 1 10 11 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 1 16 18 +M V30 18 2 16 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 21 22 +M V30 22 1 8 9 +M V30 23 1 17 19 +M V30 END BOND +M V30 END CTAB +M END +> +432 + +> +Z56177222 + +> +301.294 + +> +2.459 + +> +2 + +> +84.860 + +> +5 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1997495 + +> +0.89 + +$$$$ +Compound 433 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4489 1.4415 0.0 0 +M V30 3 C 1.8668 1.9141 0.0 0 +M V30 4 C -0.4489 2.6703 0.0 0 +M V30 5 C 1.855 3.4265 0.0 0 +M V30 6 C 3.1547 1.1697 0.0 0 +M V30 7 O 0.4253 3.8991 0.0 0 +M V30 8 C -1.9613 2.6821 0.0 0 +M V30 9 C 3.1429 4.1945 0.0 0 +M V30 10 C 4.4426 1.9377 0.0 0 +M V30 11 C -2.7175 1.3942 0.0 0 +M V30 12 C 4.4308 3.4619 0.0 0 +M V30 13 C -1.985 0.1063 0.0 0 +M V30 14 C -4.2299 1.406 0.0 0 +M V30 15 O 5.7187 4.2299 0.0 0 +M V30 16 C -2.7412 -1.1815 0.0 0 +M V30 17 C -4.998 0.1181 0.0 0 +M V30 18 O -2.0086 -2.4694 0.0 0 +M V30 19 C -4.2536 -1.1697 0.0 0 +M V30 20 C -2.7648 -3.7573 0.0 0 +M V30 21 O -5.0216 -2.4576 0.0 0 +M V30 22 C -4.2772 -3.7455 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 4 8 CFG=2 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 12 15 +M V30 15 1 13 16 +M V30 16 2 14 17 +M V30 17 1 16 18 +M V30 18 2 16 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 21 22 +M V30 22 1 5 7 +M V30 23 1 10 12 +M V30 24 1 17 19 +M V30 END BOND +M V30 END CTAB +M END +> +433 + +> +Z196385662 + +> +298.290 + +> +3.435 + +> +1 + +> +64.990 + +> +3 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3290471 + +> +0.87 + +$$$$ +Compound 434 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4483 1.4393 0.0 0 +M V30 3 C 1.8641 1.9113 0.0 0 +M V30 4 C -0.4483 2.6664 0.0 0 +M V30 5 C 1.8523 3.4215 0.0 0 +M V30 6 C 3.1501 1.168 0.0 0 +M V30 7 O 0.4247 3.8934 0.0 0 +M V30 8 C -1.9585 2.6782 0.0 0 +M V30 9 C 3.1383 4.1884 0.0 0 +M V30 10 C 4.4361 1.9349 0.0 0 +M V30 11 C -2.7136 1.3922 0.0 0 +M V30 12 C 4.4243 3.4569 0.0 0 +M V30 13 C -4.2237 1.404 0.0 0 +M V30 14 C -1.9821 0.1061 0.0 0 +M V30 15 O 5.7103 4.2237 0.0 0 +M V30 16 O -4.9906 2.7136 0.0 0 +M V30 17 C -4.9906 0.1179 0.0 0 +M V30 18 C -2.7372 -1.1798 0.0 0 +M V30 19 C -4.2473 4.0232 0.0 0 +M V30 20 C -4.2473 -1.168 0.0 0 +M V30 21 O -5.0142 -2.454 0.0 0 +M V30 22 C -4.2709 -3.74 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 4 8 CFG=2 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 12 15 +M V30 15 1 13 16 +M V30 16 1 13 17 +M V30 17 2 14 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 20 21 +M V30 21 1 21 22 +M V30 22 1 5 7 +M V30 23 1 10 12 +M V30 24 1 18 20 +M V30 END BOND +M V30 END CTAB +M END +> +434 + +> +Z196385666 + +> +298.290 + +> +3.785 + +> +1 + +> +64.990 + +> +3 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3290477 + +> +0.86 + +$$$$ +Compound 435 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5084 0.0 0 +M V30 3 N -1.3199 2.2744 0.0 0 +M V30 4 C 1.2727 2.2744 0.0 0 +M V30 5 C -1.3316 3.7829 0.0 0 +M V30 6 C 1.2609 3.7829 0.0 0 +M V30 7 C 2.5573 1.532 0.0 0 +M V30 8 C -2.6398 4.5371 0.0 0 +M V30 9 C -0.0471 4.5371 0.0 0 +M V30 10 C 2.5455 4.5371 0.0 0 +M V30 11 C 3.8418 2.298 0.0 0 +M V30 12 O -2.6515 6.0456 0.0 0 +M V30 13 N -3.9479 3.8064 0.0 0 +M V30 14 C 3.83 3.8064 0.0 0 +M V30 15 C -5.256 4.5607 0.0 0 +M V30 16 C -3.9597 2.3216 0.0 0 +M V30 17 C -6.5641 3.83 0.0 0 +M V30 18 C -5.2678 1.5791 0.0 0 +M V30 19 N -6.5759 2.3451 0.0 0 +M V30 20 C -7.884 1.6027 0.0 0 +M V30 21 C -7.8958 0.1178 0.0 0 +M V30 22 O -9.2039 -0.6245 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 1 13 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 1 20 21 +M V30 21 1 21 22 +M V30 22 1 6 9 +M V30 23 1 11 14 +M V30 24 1 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +435 + +> +Z167281862 + +> +301.340 + +> +-0.080 + +> +2 + +> +72.880 + +> +3 + +> +parp2 + +> + + +> + + +$$$$ +Compound 436 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2929 -0.7473 0.0 0 +M V30 3 N 2.5859 0.0118 0.0 0 +M V30 4 C 1.2811 -2.2419 0.0 0 +M V30 5 C -0.0355 -2.9892 0.0 0 +M V30 6 C 2.574 -2.9892 0.0 0 +M V30 7 C -0.0474 -4.4839 0.0 0 +M V30 8 C 2.5622 -4.4839 0.0 0 +M V30 9 N -1.3641 -5.2193 0.0 0 +M V30 10 C 1.2455 -5.2193 0.0 0 +M V30 11 C -1.376 -6.714 0.0 0 +M V30 12 O -2.6927 -7.4613 0.0 0 +M V30 13 C -0.083 -7.4613 0.0 0 +M V30 14 C -0.0948 -8.9559 0.0 0 +M V30 15 C 1.376 -8.6831 0.0 0 +M V30 16 C 0.4033 -10.3557 0.0 0 +M V30 17 C -1.5895 -8.6831 0.0 0 +M V30 18 O 2.3368 -9.8219 0.0 0 +M V30 19 O 1.8742 -7.2596 0.0 0 +M V30 20 C -0.5812 -11.4944 0.0 0 +M V30 21 C -2.574 -9.8219 0.0 0 +M V30 22 C -2.0758 -11.2216 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 2 7 10 +M V30 10 1 9 11 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 1 15 19 +M V30 19 1 16 20 +M V30 20 1 17 21 +M V30 21 1 20 22 +M V30 22 1 8 10 +M V30 23 1 21 22 +M V30 END BOND +M V30 END CTAB +M END +> +436 + +> +Z221346204 + +> +304.341 + +> +1.772 + +> +3 + +> +109.490 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3414766 + +> +0.92 + +$$$$ +Compound 437 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4936 0.0 0 +M V30 3 N -1.3277 -2.2286 0.0 0 +M V30 4 C 1.2802 -2.2286 0.0 0 +M V30 5 N -1.3395 -3.7223 0.0 0 +M V30 6 C 1.2684 -3.7223 0.0 0 +M V30 7 C 2.5724 -1.4699 0.0 0 +M V30 8 C -0.0474 -4.4691 0.0 0 +M V30 9 C 2.5605 -4.4691 0.0 0 +M V30 10 C 3.8645 -2.2049 0.0 0 +M V30 11 C -0.0592 -5.9628 0.0 0 +M V30 12 C 3.8527 -3.7104 0.0 0 +M V30 13 O 1.2328 -6.7096 0.0 0 +M V30 14 N -1.3751 -6.7096 0.0 0 +M V30 15 C -1.3869 -8.2033 0.0 0 CFG=2 +M V30 16 C -2.7028 -8.9383 0.0 0 +M V30 17 C -0.0948 -8.9383 0.0 0 +M V30 18 C -2.7146 -10.432 0.0 0 +M V30 19 C -4.0187 -8.1796 0.0 0 +M V30 20 C -4.0305 -11.1788 0.0 0 +M V30 21 C -5.3345 -8.9146 0.0 0 +M V30 22 N -5.3464 -10.4083 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 15 14 CFG=1 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 2 20 22 +M V30 22 1 6 8 +M V30 23 1 10 12 +M V30 24 1 21 22 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 15) +M V30 END COLLECTION +M V30 END CTAB +M END +> +437 + +> +Z27995136 + +> +294.308 + +> +-0.028 + +> +2 + +> +83.450 + +> +3 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 438 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4517 -1.413 0.0 0 +M V30 3 N 1.9111 -1.7142 0.0 0 +M V30 4 C -0.5675 -2.5134 0.0 0 +M V30 5 C 2.9072 -0.5907 0.0 0 +M V30 6 C -2.0501 -2.189 0.0 0 +M V30 7 C -0.1158 -3.9264 0.0 0 +M V30 8 C -3.0693 -3.2894 0.0 0 +M V30 9 C -1.135 -5.0268 0.0 0 +M V30 10 N -4.5519 -2.9651 0.0 0 +M V30 11 C -2.6176 -4.7024 0.0 0 +M V30 12 C -5.5711 -4.0654 0.0 0 +M V30 13 O -5.1194 -5.4785 0.0 0 +M V30 14 C -7.0537 -3.7411 0.0 0 +M V30 15 C -8.073 -4.8414 0.0 0 +M V30 16 C -9.0922 -3.7179 0.0 0 +M V30 17 C -9.3007 -5.7101 0.0 0 +M V30 18 C -6.8684 -5.7101 0.0 0 +M V30 19 O -10.5748 -4.0191 0.0 0 +M V30 20 O -8.6405 -2.2817 0.0 0 +M V30 21 C -8.849 -7.1232 0.0 0 +M V30 22 C -7.3433 -7.1232 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 2 8 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 1 15 18 +M V30 18 2 16 19 +M V30 19 1 16 20 +M V30 20 1 17 21 +M V30 21 1 18 22 +M V30 22 1 9 11 +M V30 23 1 21 22 +M V30 END BOND +M V30 END CTAB +M END +> +438 + +> +Z90313869 + +> +304.341 + +> +1.419 + +> +3 + +> +95.500 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3414766 + +> +0.89 + +$$$$ +Compound 439 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.5144 0.0 0 +M V30 3 N 1.2777 2.2716 0.0 0 +M V30 4 C -1.3251 2.2716 0.0 0 +M V30 5 C 2.5673 1.538 0.0 0 +M V30 6 N -1.4907 3.7741 0.0 0 +M V30 7 C -2.7093 1.6682 0.0 0 +M V30 8 C 3.8569 2.2952 0.0 0 +M V30 9 C -2.9696 4.0936 0.0 0 +M V30 10 C -3.7268 2.7921 0.0 0 +M V30 11 C 5.1465 1.5617 0.0 0 +M V30 12 C -5.2294 2.6502 0.0 0 +M V30 13 N 6.5071 2.1887 0.0 0 +M V30 14 N 5.2885 0.0828 0.0 0 +M V30 15 O -5.8564 1.2896 0.0 0 +M V30 16 C -6.1285 3.8806 0.0 0 +M V30 17 C 7.501 1.0884 0.0 0 +M V30 18 C 6.7438 -0.2129 0.0 0 +M V30 19 C 8.9917 1.1003 0.0 0 +M V30 20 C 7.4891 -1.5025 0.0 0 +M V30 21 C 9.7371 -0.1893 0.0 0 +M V30 22 C 8.9799 -1.4907 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 1 10 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 2 12 15 +M V30 15 1 12 16 +M V30 16 1 13 17 +M V30 17 1 14 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 2 20 22 +M V30 22 2 9 10 +M V30 23 2 17 18 +M V30 24 1 21 22 +M V30 END BOND +M V30 END CTAB +M END +> +439 + +> +Z198287396 + +> +296.324 + +> +1.170 + +> +3 + +> +90.640 + +> +5 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 440 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5068 0.0 0 +M V30 3 C -1.3185 2.272 0.0 0 +M V30 4 C 1.2714 2.272 0.0 0 +M V30 5 C -1.3302 3.7789 0.0 0 +M V30 6 C 1.2596 3.7789 0.0 0 +M V30 7 C -2.637 4.5323 0.0 0 +M V30 8 C -0.047 4.5323 0.0 0 +M V30 9 O -2.6487 6.0392 0.0 0 +M V30 10 N -3.9437 3.8024 0.0 0 +M V30 11 C -5.2504 4.5559 0.0 0 +M V30 12 C -6.5572 3.826 0.0 0 +M V30 13 O -6.5689 2.3427 0.0 0 +M V30 14 N -7.8639 4.5794 0.0 0 +M V30 15 C -9.1706 3.8495 0.0 0 +M V30 16 C -9.1824 2.3662 0.0 0 +M V30 17 C -10.4774 4.603 0.0 0 +M V30 18 C -10.4891 1.6245 0.0 0 +M V30 19 C -11.7841 3.8731 0.0 0 +M V30 20 C -11.7959 2.3897 0.0 0 +M V30 21 C -10.5009 0.1412 0.0 0 +M V30 22 O -11.8076 -0.5886 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 17 19 +M V30 19 2 18 20 +M V30 20 1 18 21 +M V30 21 1 21 22 +M V30 22 1 6 8 +M V30 23 1 19 20 +M V30 END BOND +M V30 END CTAB +M END +> +440 + +> +Z32803259 + +> +318.755 + +> +1.738 + +> +3 + +> +78.430 + +> +5 + +> +DNA_pk + +> + + +> + + +$$$$ +Compound 441 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 S 5.4927 0.3788 0.0 0 +M V30 3 O 6.9843 0.3906 0.0 0 +M V30 4 O 3.9775 0.3906 0.0 0 +M V30 5 N 5.4809 -1.1127 0.0 0 +M V30 6 C 5.4809 1.894 0.0 0 +M V30 7 C 6.7712 -1.8467 0.0 0 +M V30 8 C 4.1669 -1.8585 0.0 0 +M V30 9 C 6.7712 2.6516 0.0 0 +M V30 10 C 4.1669 2.6516 0.0 0 +M V30 11 C 6.7594 -3.3382 0.0 0 CFG=2 +M V30 12 C 4.155 -3.3501 0.0 0 +M V30 13 C 6.7594 4.1669 0.0 0 +M V30 14 C 8.0615 1.9058 0.0 0 +M V30 15 C 4.155 4.1669 0.0 0 +M V30 16 C 5.4454 -4.0722 0.0 0 +M V30 17 C 8.0497 -4.0722 0.0 0 +M V30 18 C 8.0497 4.9245 0.0 0 +M V30 19 C 5.4454 4.9363 0.0 0 +M V30 20 C 9.3519 2.6635 0.0 0 +M V30 21 N 8.0379 -5.5638 0.0 0 +M V30 22 N 9.34 4.1906 0.0 0 +M V30 23 C 9.3282 -6.2977 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 2 2 4 +M V30 3 1 2 5 +M V30 4 1 2 6 +M V30 5 1 5 7 +M V30 6 1 5 8 +M V30 7 1 6 9 +M V30 8 2 6 10 +M V30 9 1 7 11 +M V30 10 1 8 12 +M V30 11 1 9 13 +M V30 12 2 9 14 +M V30 13 1 10 15 +M V30 14 1 11 16 +M V30 15 1 11 17 CFG=1 +M V30 16 2 13 18 +M V30 17 1 13 19 +M V30 18 1 14 20 +M V30 19 1 17 21 +M V30 20 1 18 22 +M V30 21 1 21 23 +M V30 22 1 12 16 +M V30 23 2 15 19 +M V30 24 2 20 22 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 11) +M V30 END COLLECTION +M V30 END CTAB +M END +> +441 + +> +Z1491218666 + +> +319.422 + +> +1.097 + +> +1 + +> +62.300 + +> +3 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL2420922 + +> +0.95 + +$$$$ +Compound 442 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4974 0.0 0 +M V30 3 N -1.331 -2.2343 0.0 0 +M V30 4 C 1.2835 -2.2343 0.0 0 +M V30 5 N -1.3429 -3.7317 0.0 0 +M V30 6 C 1.2716 -3.7317 0.0 0 +M V30 7 C 2.5789 -1.4736 0.0 0 +M V30 8 C -0.0475 -4.4804 0.0 0 +M V30 9 C 2.567 -4.4804 0.0 0 +M V30 10 C 3.8743 -2.2105 0.0 0 +M V30 11 C -0.0594 -5.9779 0.0 0 +M V30 12 C 3.8624 -3.7198 0.0 0 +M V30 13 O -1.3786 -6.7266 0.0 0 +M V30 14 N 1.2359 -6.7266 0.0 0 +M V30 15 C 1.2241 -8.2241 0.0 0 +M V30 16 C 2.5314 -5.9541 0.0 0 +M V30 17 C 2.5195 -8.9609 0.0 0 +M V30 18 C 3.8268 -6.7029 0.0 0 +M V30 19 N 3.8149 -8.2003 0.0 0 +M V30 20 C 5.1103 -8.9372 0.0 0 +M V30 21 O 5.0984 -10.4346 0.0 0 +M V30 22 C 6.4057 -8.1765 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 2 20 21 +M V30 21 1 20 22 +M V30 22 1 6 8 +M V30 23 1 10 12 +M V30 24 1 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +442 + +> +Z31727123 + +> +300.313 + +> +-1.515 + +> +1 + +> +82.080 + +> +1 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3105422 + +> +0.87 + +$$$$ +Compound 443 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.2856 -0.743 0.0 0 +M V30 3 C -1.3092 -0.743 0.0 0 +M V30 4 N 2.5712 0.0117 0.0 0 +M V30 5 N 1.2738 -2.2291 0.0 0 +M V30 6 C -2.6184 0.0117 0.0 0 +M V30 7 C 3.8568 -0.7312 0.0 0 +M V30 8 C 2.5594 -2.9722 0.0 0 +M V30 9 N -3.9276 -0.7312 0.0 0 +M V30 10 C -2.6302 1.5215 0.0 0 +M V30 11 N 5.1424 0.0235 0.0 0 +M V30 12 C 3.845 -2.2173 0.0 0 +M V30 13 N 2.5476 -4.4583 0.0 0 +M V30 14 C -5.2368 0.0235 0.0 0 +M V30 15 C -3.9394 2.2763 0.0 0 +M V30 16 N -5.2486 1.5333 0.0 0 +M V30 17 C -6.546 -0.7194 0.0 0 +M V30 18 O -3.9512 3.786 0.0 0 +M V30 19 C -6.5578 2.2881 0.0 0 +M V30 20 C -7.8552 0.0353 0.0 0 +M V30 21 C -6.5578 -2.2055 0.0 0 +M V30 22 C -7.867 1.5568 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 6 10 +M V30 10 1 7 11 +M V30 11 2 7 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 10 15 +M V30 15 1 14 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 17 21 +M V30 21 2 19 22 +M V30 22 1 8 12 +M V30 23 1 15 16 +M V30 24 1 20 22 +M V30 END BOND +M V30 END CTAB +M END +> +443 + +> +Z335768980 + +> +314.366 + +> +1.164 + +> +2 + +> +110.490 + +> +3 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 444 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -1.3147 0.758 0.0 0 +M V30 3 C -2.6295 0.0236 0.0 0 +M V30 4 C -1.3266 2.2741 0.0 0 +M V30 5 C -3.9442 0.7817 0.0 0 +M V30 6 C -2.6413 -1.4687 0.0 0 CFG=1 +M V30 7 C -2.6413 3.044 0.0 0 +M V30 8 C -3.9561 2.2978 0.0 0 +M V30 9 N -1.3502 -2.2031 0.0 0 +M V30 10 C -3.9561 -2.2031 0.0 0 +M V30 11 C -1.3621 -3.6955 0.0 0 +M V30 12 C -0.071 -4.4299 0.0 0 +M V30 13 N 1.22 -3.6718 0.0 0 +M V30 14 N -0.0829 -5.9223 0.0 0 +M V30 15 C 2.511 -4.4062 0.0 0 +M V30 16 C 1.2081 -6.6685 0.0 0 +M V30 17 C 2.4992 -5.8986 0.0 0 +M V30 18 C 3.8021 -3.6481 0.0 0 +M V30 19 O 1.1963 -8.1609 0.0 0 +M V30 20 C 3.7903 -6.6448 0.0 0 +M V30 21 C 5.0932 -4.3825 0.0 0 +M V30 22 C 5.0813 -5.8868 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 CFG=1 +M V30 9 1 6 10 +M V30 10 1 9 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 1 15 18 +M V30 18 2 16 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 2 20 22 +M V30 22 1 7 8 +M V30 23 1 16 17 +M V30 24 1 21 22 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 6) +M V30 END COLLECTION +M V30 END CTAB +M END +> +444 + +> +Z44474819 + +> +297.327 + +> +1.283 + +> +2 + +> +53.490 + +> +4 + +> +parp10 + +> + + +> + + +$$$$ +Compound 445 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4981 0.0 0 +M V30 3 C -1.3316 -2.2472 0.0 0 +M V30 4 C 1.2841 -2.2472 0.0 0 +M V30 5 C -1.3435 -3.7453 0.0 0 +M V30 6 C 1.2722 -3.7453 0.0 0 +M V30 7 C -0.0475 -4.4825 0.0 0 +M V30 8 C -0.0594 -5.9806 0.0 0 CFG=1 +M V30 9 N 1.2365 -6.7178 0.0 0 +M V30 10 C -1.3792 -6.7178 0.0 0 +M V30 11 C 1.2246 -8.216 0.0 0 +M V30 12 C 2.5206 -8.9531 0.0 0 +M V30 13 N 3.8166 -8.1922 0.0 0 +M V30 14 N 2.5087 -10.4513 0.0 0 +M V30 15 C 5.1127 -8.9294 0.0 0 +M V30 16 C 3.8048 -11.2004 0.0 0 +M V30 17 C 5.1008 -10.4275 0.0 0 +M V30 18 C 6.4087 -8.1684 0.0 0 +M V30 19 O 3.7929 -12.6985 0.0 0 +M V30 20 C 6.3968 -11.1766 0.0 0 +M V30 21 C 7.7047 -8.9056 0.0 0 +M V30 22 C 7.6928 -10.4156 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 CFG=1 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 1 15 18 +M V30 18 2 16 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 2 20 22 +M V30 22 1 6 7 +M V30 23 1 16 17 +M V30 24 1 21 22 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 8) +M V30 END COLLECTION +M V30 END CTAB +M END +> +445 + +> +Z44470753 + +> +313.781 + +> +1.853 + +> +2 + +> +53.490 + +> +4 + +> +parp1, parp2 + +> + + +> + + +$$$$ +Compound 446 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4981 0.0 0 +M V30 3 C -1.3316 -2.2472 0.0 0 +M V30 4 C 1.2841 -2.2472 0.0 0 +M V30 5 C -1.3435 -3.7453 0.0 0 +M V30 6 C 1.2722 -3.7453 0.0 0 +M V30 7 C -0.0475 -4.4825 0.0 0 +M V30 8 C -0.0594 -5.9806 0.0 0 CFG=1 +M V30 9 N 1.2365 -6.7178 0.0 0 +M V30 10 C -1.3792 -6.7178 0.0 0 +M V30 11 C 1.2246 -8.216 0.0 0 +M V30 12 C 2.5206 -8.9531 0.0 0 +M V30 13 N 3.8166 -8.1922 0.0 0 +M V30 14 N 2.5087 -10.4513 0.0 0 +M V30 15 C 5.1127 -8.9294 0.0 0 +M V30 16 C 3.8048 -11.2004 0.0 0 +M V30 17 C 5.1008 -10.4275 0.0 0 +M V30 18 C 6.4087 -8.1684 0.0 0 +M V30 19 O 3.7929 -12.6985 0.0 0 +M V30 20 C 6.3968 -11.1766 0.0 0 +M V30 21 C 7.7047 -8.9056 0.0 0 +M V30 22 C 7.6928 -10.4156 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 CFG=1 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 1 15 18 +M V30 18 2 16 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 2 20 22 +M V30 22 1 6 7 +M V30 23 1 16 17 +M V30 24 1 21 22 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 8) +M V30 END COLLECTION +M V30 END CTAB +M END +> +446 + +> +Z44471395 + +> +358.232 + +> +2.003 + +> +2 + +> +53.490 + +> +4 + +> +parp1 + +> + + +> + + +$$$$ +Compound 447 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4965 0.0 0 +M V30 3 N -1.3302 -2.2328 0.0 0 +M V30 4 C 1.2827 -2.2328 0.0 0 +M V30 5 N -1.3421 -3.7294 0.0 0 +M V30 6 C 1.2708 -3.7294 0.0 0 +M V30 7 C 2.5773 -1.4727 0.0 0 +M V30 8 C -0.0475 -4.4776 0.0 0 +M V30 9 C 2.5654 -4.4776 0.0 0 +M V30 10 C 3.8719 -2.2091 0.0 0 +M V30 11 C -0.0593 -5.9741 0.0 0 +M V30 12 C 3.86 -3.7175 0.0 0 +M V30 13 O -1.3777 -6.7224 0.0 0 +M V30 14 N 1.2352 -6.7224 0.0 0 +M V30 15 C 2.5298 -5.9504 0.0 0 +M V30 16 C 1.2233 -8.2189 0.0 0 +M V30 17 C 3.8244 -6.6986 0.0 0 CFG=1 +M V30 18 C 2.5179 -8.9553 0.0 0 +M V30 19 O 5.119 -5.9266 0.0 0 +M V30 20 C 3.8125 -8.1951 0.0 0 +M V30 21 C 6.4136 -6.6749 0.0 0 +M V30 22 C 7.7082 -5.9029 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 CFG=1 +M V30 19 1 17 20 +M V30 20 1 19 21 +M V30 21 1 21 22 +M V30 22 1 6 8 +M V30 23 1 10 12 +M V30 24 1 18 20 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 17) +M V30 END COLLECTION +M V30 END CTAB +M END +> +447 + +> +Z827213900 + +> +301.340 + +> +0.018 + +> +1 + +> +71.000 + +> +3 + +> +parp2 + +> + + +> + + +$$$$ +Compound 448 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 S 5.4936 0.1894 0.0 0 +M V30 3 O 6.9854 0.2012 0.0 0 +M V30 4 O 3.9781 0.2012 0.0 0 +M V30 5 N 5.4818 -1.3023 0.0 0 +M V30 6 C 5.4818 1.7049 0.0 0 +M V30 7 C 6.7723 -2.0364 0.0 0 +M V30 8 C 4.1676 -2.0364 0.0 0 +M V30 9 C 6.7723 2.4626 0.0 0 +M V30 10 C 4.1676 2.4626 0.0 0 +M V30 11 C 6.7605 -3.5282 0.0 0 +M V30 12 C 4.1557 -3.5282 0.0 0 +M V30 13 C 6.7605 3.9781 0.0 0 +M V30 14 C 8.0629 1.7286 0.0 0 +M V30 15 C 4.1557 3.9781 0.0 0 +M V30 16 C 8.051 -4.2623 0.0 0 +M V30 17 C 5.4463 -4.2623 0.0 0 +M V30 18 C 8.051 4.7359 0.0 0 +M V30 19 C 5.4463 4.7477 0.0 0 +M V30 20 C 9.3534 2.4863 0.0 0 +M V30 21 N 9.3416 -3.5045 0.0 0 +M V30 22 C 8.0392 -5.7541 0.0 0 +M V30 23 N 9.3416 4.0018 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 2 2 4 +M V30 3 1 2 5 +M V30 4 1 2 6 +M V30 5 1 5 7 +M V30 6 1 5 8 +M V30 7 1 6 9 +M V30 8 2 6 10 +M V30 9 1 7 11 +M V30 10 1 8 12 +M V30 11 1 9 13 +M V30 12 2 9 14 +M V30 13 1 10 15 +M V30 14 1 11 16 +M V30 15 1 11 17 +M V30 16 2 13 18 +M V30 17 1 13 19 +M V30 18 1 14 20 +M V30 19 1 16 21 +M V30 20 1 16 22 +M V30 21 1 18 23 +M V30 22 1 12 17 +M V30 23 2 15 19 +M V30 24 2 20 23 +M V30 END BOND +M V30 END CTAB +M END +> +448 + +> +Z1491321611 + +> +319.422 + +> +1.260 + +> +1 + +> +76.290 + +> +2 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL2420922 + +> +0.93 + +$$$$ +Compound 449 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.017 1.1234 0.0 0 +M V30 3 N -2.4953 0.8278 0.0 0 +M V30 4 C -0.5676 2.5662 0.0 0 +M V30 5 C -2.9683 -0.5913 0.0 0 +M V30 6 N -1.4664 3.7962 0.0 0 +M V30 7 C 0.8514 3.0393 0.0 0 +M V30 8 C -1.9749 -1.6911 0.0 0 +M V30 9 C -4.4466 -0.8869 0.0 0 +M V30 10 N -0.5913 5.0261 0.0 0 +M V30 11 C 0.8396 4.553 0.0 0 +M V30 12 C 2.1405 2.2942 0.0 0 +M V30 13 C -2.448 -3.1102 0.0 0 +M V30 14 C -4.9197 -2.3061 0.0 0 +M V30 15 C 2.1287 5.3217 0.0 0 +M V30 16 C 3.4295 3.0629 0.0 0 +M V30 17 O -1.4546 -4.2101 0.0 0 +M V30 18 C -3.9262 -3.4059 0.0 0 +M V30 19 C 3.4177 4.5767 0.0 0 +M V30 20 C -1.9276 -5.6292 0.0 0 +M V30 21 O -4.3993 -4.825 0.0 0 +M V30 22 C -3.4059 -5.9249 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 7 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 11 15 +M V30 15 2 12 16 +M V30 16 1 13 17 +M V30 17 2 13 18 +M V30 18 2 15 19 +M V30 19 1 17 20 +M V30 20 1 18 21 +M V30 21 1 21 22 +M V30 22 1 10 11 +M V30 23 1 14 18 +M V30 24 1 16 19 +M V30 END BOND +M V30 END CTAB +M END +> +449 + +> +Z32374120 + +> +297.309 + +> +2.614 + +> +2 + +> +76.240 + +> +4 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL2007002 + +> +0.9 + +$$$$ +Compound 450 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.0163 1.1226 0.0 0 +M V30 3 N -2.4935 0.8272 0.0 0 +M V30 4 C -0.5672 2.5644 0.0 0 +M V30 5 C -2.9662 -0.5908 0.0 0 +M V30 6 N -1.4654 3.7935 0.0 0 +M V30 7 C 0.8508 3.0371 0.0 0 +M V30 8 C -4.4435 -0.8863 0.0 0 +M V30 9 C -1.9735 -1.6899 0.0 0 +M V30 10 N -0.5908 5.0225 0.0 0 +M V30 11 C 0.839 4.5498 0.0 0 +M V30 12 C 2.139 2.2926 0.0 0 +M V30 13 C -4.9162 -2.3044 0.0 0 +M V30 14 C -2.4462 -3.108 0.0 0 +M V30 15 C 2.1272 5.318 0.0 0 +M V30 16 C 3.4271 3.0608 0.0 0 +M V30 17 O -6.3934 -2.5999 0.0 0 +M V30 18 C -3.9235 -3.4035 0.0 0 +M V30 19 O -1.4535 -4.2071 0.0 0 +M V30 20 C 3.4153 4.5735 0.0 0 +M V30 21 C -6.8661 -4.018 0.0 0 +M V30 22 C -1.9263 -5.6252 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 7 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 11 15 +M V30 15 2 12 16 +M V30 16 1 13 17 +M V30 17 2 13 18 +M V30 18 1 14 19 +M V30 19 2 15 20 +M V30 20 1 17 21 +M V30 21 1 19 22 +M V30 22 1 10 11 +M V30 23 1 14 18 +M V30 24 1 16 20 +M V30 END BOND +M V30 END CTAB +M END +> +450 + +> +Z32396208 + +> +297.309 + +> +2.964 + +> +2 + +> +76.240 + +> +4 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL2007002 + +> +0.86 + +$$$$ +Compound 451 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 S 8.1319 0.0945 0.0 0 +M V30 3 O 9.6211 0.1063 0.0 0 +M V30 4 O 6.6189 0.1063 0.0 0 +M V30 5 N 8.12 -1.3947 0.0 0 +M V30 6 C 8.12 1.6074 0.0 0 +M V30 7 C 6.8081 -2.1275 0.0 0 +M V30 8 C 9.4084 2.3639 0.0 0 +M V30 9 C 6.8081 2.3639 0.0 0 +M V30 10 C 6.7962 -3.6168 0.0 0 +M V30 11 C 5.4961 -1.371 0.0 0 +M V30 12 C 9.3966 3.8768 0.0 0 +M V30 13 C 10.6967 1.6311 0.0 0 +M V30 14 C 6.7962 3.8768 0.0 0 +M V30 15 C 5.4843 -4.3496 0.0 0 +M V30 16 C 8.0846 -4.3496 0.0 0 +M V30 17 C 4.1841 -2.1038 0.0 0 +M V30 18 C 10.6849 4.6332 0.0 0 +M V30 19 C 8.0846 4.6332 0.0 0 +M V30 20 C 11.9851 2.3875 0.0 0 +M V30 21 C 4.1723 -3.5931 0.0 0 +M V30 22 N 8.0728 -5.8389 0.0 0 +M V30 23 N 11.9732 3.9004 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 2 2 4 +M V30 3 1 2 5 +M V30 4 1 2 6 +M V30 5 1 5 7 +M V30 6 1 6 8 +M V30 7 2 6 9 +M V30 8 1 7 10 +M V30 9 1 7 11 +M V30 10 1 8 12 +M V30 11 2 8 13 +M V30 12 1 9 14 +M V30 13 1 10 15 +M V30 14 1 10 16 +M V30 15 1 11 17 +M V30 16 2 12 18 +M V30 17 1 12 19 +M V30 18 1 13 20 +M V30 19 1 15 21 +M V30 20 1 16 22 +M V30 21 1 18 23 +M V30 22 2 14 19 +M V30 23 1 17 21 +M V30 24 2 20 23 +M V30 END BOND +M V30 END CTAB +M END +> +451 + +> +Z1491326294 + +> +319.422 + +> +2.727 + +> +2 + +> +85.080 + +> +3 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 452 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.2072 0.8995 0.0 0 +M V30 3 C 0.4497 -1.4203 0.0 0 +M V30 4 N 2.4145 0.0236 0.0 0 +M V30 5 C 1.1954 2.4145 0.0 0 +M V30 6 C 1.941 -1.4084 0.0 0 +M V30 7 N 2.4855 3.1838 0.0 0 +M V30 8 C 2.8169 -2.6157 0.0 0 +M V30 9 C 2.4736 4.6988 0.0 0 +M V30 10 O 4.2964 -2.45 0.0 0 +M V30 11 O 2.1896 -3.9768 0.0 0 +M V30 12 O 1.1599 5.4563 0.0 0 +M V30 13 C 3.7638 5.4563 0.0 0 +M V30 14 C 5.0539 4.7106 0.0 0 +M V30 15 C 3.7519 6.9713 0.0 0 +M V30 16 C 6.344 5.4681 0.0 0 +M V30 17 C 5.042 7.7288 0.0 0 +M V30 18 O 7.6341 4.7225 0.0 0 +M V30 19 C 6.3321 6.9831 0.0 0 +M V30 20 C 8.9242 5.48 0.0 0 +M V30 21 O 7.6222 7.7406 0.0 0 +M V30 22 C 8.9124 6.9949 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 1 7 9 +M V30 9 2 8 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 1 16 18 +M V30 18 2 16 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 4 6 +M V30 23 1 17 19 +M V30 24 1 21 22 +M V30 END BOND +M V30 END CTAB +M END +> +452 + +> +Z1274461220 + +> +320.320 + +> +1.663 + +> +2 + +> +97.750 + +> +4 + +> +ATM + +> + + +> + + +$$$$ +Compound 453 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.2404 -0.8505 0.0 0 +M V30 3 C 1.1813 -0.8978 0.0 0 +M V30 4 N -2.7171 -0.4961 0.0 0 +M V30 5 C -0.8151 -2.28 0.0 0 +M V30 6 C 0.6733 -2.3036 0.0 0 +M V30 7 C 2.6698 -0.9214 0.0 0 +M V30 8 C -3.7567 -1.583 0.0 0 +M V30 9 C -1.8547 -3.3669 0.0 0 +M V30 10 C 1.8547 -3.2015 0.0 0 +M V30 11 C 3.0951 -2.3509 0.0 0 +M V30 12 N -3.3314 -3.0124 0.0 0 +M V30 13 C -5.2334 -1.2286 0.0 0 +M V30 14 O -1.4294 -4.7963 0.0 0 +M V30 15 N -6.273 -2.3154 0.0 0 +M V30 16 C -7.7497 -1.961 0.0 0 +M V30 17 C -5.8477 -3.7449 0.0 0 +M V30 18 C -8.7894 -3.0479 0.0 0 +M V30 19 C -10.2897 -2.8471 0.0 0 +M V30 20 C -8.5176 -4.5128 0.0 0 +M V30 21 S -10.9394 -4.1938 0.0 0 +M V30 22 C -9.8408 -5.2216 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 1 5 6 +M V30 23 1 9 12 +M V30 24 1 10 11 +M V30 25 1 21 22 +M V30 END BOND +M V30 END CTAB +M END +> +453 + +> +Z90265831 + +> +331.456 + +> +2.385 + +> +1 + +> +44.700 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3616846 + +> +0.93 + +$$$$ +Compound 454 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 S 5.4927 0.3788 0.0 0 +M V30 3 O 6.9843 0.3906 0.0 0 +M V30 4 O 3.9775 0.3906 0.0 0 +M V30 5 N 5.4809 -1.1127 0.0 0 +M V30 6 C 5.4809 1.894 0.0 0 +M V30 7 C 6.7712 -1.8467 0.0 0 +M V30 8 C 4.1669 -1.8585 0.0 0 +M V30 9 C 6.7712 2.6516 0.0 0 +M V30 10 C 4.1669 2.6516 0.0 0 +M V30 11 C 6.7594 -3.3382 0.0 0 CFG=2 +M V30 12 C 4.155 -3.3501 0.0 0 +M V30 13 C 6.7594 4.1669 0.0 0 +M V30 14 C 8.0615 1.9058 0.0 0 +M V30 15 C 4.155 4.1669 0.0 0 +M V30 16 C 5.4454 -4.0722 0.0 0 +M V30 17 C 8.0497 -4.0722 0.0 0 +M V30 18 N 8.0497 4.9245 0.0 0 +M V30 19 C 5.4454 4.9363 0.0 0 +M V30 20 C 9.3519 2.6635 0.0 0 +M V30 21 N 8.0379 -5.5638 0.0 0 +M V30 22 C 9.34 4.1906 0.0 0 +M V30 23 C 9.3282 -6.2977 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 2 2 4 +M V30 3 1 2 5 +M V30 4 1 2 6 +M V30 5 1 5 7 +M V30 6 1 5 8 +M V30 7 1 6 9 +M V30 8 2 6 10 +M V30 9 1 7 11 +M V30 10 1 8 12 +M V30 11 1 9 13 +M V30 12 2 9 14 +M V30 13 1 10 15 +M V30 14 1 11 16 +M V30 15 1 11 17 CFG=1 +M V30 16 2 13 18 +M V30 17 1 13 19 +M V30 18 1 14 20 +M V30 19 1 17 21 +M V30 20 1 18 22 +M V30 21 1 21 23 +M V30 22 1 12 16 +M V30 23 2 15 19 +M V30 24 2 20 22 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 11) +M V30 END COLLECTION +M V30 END CTAB +M END +> +454 + +> +Z1491218468 + +> +319.422 + +> +1.307 + +> +1 + +> +62.300 + +> +3 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL2420922 + +> +0.86 + +$$$$ +Compound 455 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4923 0.0 0 +M V30 3 N 1.2791 -2.2266 0.0 0 +M V30 4 C -1.3265 -2.2266 0.0 0 +M V30 5 C 2.5701 -1.4686 0.0 0 +M V30 6 C -2.6412 -1.4686 0.0 0 +M V30 7 C -1.3383 -3.719 0.0 0 +M V30 8 C 3.8611 -2.2029 0.0 0 +M V30 9 C -3.9559 -2.2029 0.0 0 +M V30 10 C -2.653 -4.4652 0.0 0 +M V30 11 C 5.1521 -1.4449 0.0 0 +M V30 12 C -5.2706 -1.4449 0.0 0 +M V30 13 C -3.9677 -3.6953 0.0 0 +M V30 14 C 6.5142 -2.049 0.0 0 +M V30 15 C 5.2942 0.0592 0.0 0 +M V30 16 N -6.5852 -0.6869 0.0 0 +M V30 17 C 7.5091 -0.9238 0.0 0 +M V30 18 C 6.9643 -3.4703 0.0 0 +M V30 19 N 6.7511 0.379 0.0 0 +M V30 20 C 8.9659 -1.2199 0.0 0 +M V30 21 C 8.4211 -3.7664 0.0 0 +M V30 22 C 9.416 -2.6412 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 1 9 12 +M V30 12 2 9 13 +M V30 13 1 11 14 +M V30 14 2 11 15 +M V30 15 3 12 16 +M V30 16 2 14 17 +M V30 17 1 14 18 +M V30 18 1 15 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 2 20 22 +M V30 22 1 10 13 +M V30 23 1 17 19 +M V30 24 1 21 22 +M V30 END BOND +M V30 END CTAB +M END +> +455 + +> +Z26394148 + +> +289.331 + +> +2.849 + +> +2 + +> +68.680 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL343641 + +> +0.9 + +$$$$ +Compound 456 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4936 0.0 0 +M V30 3 N 1.2803 -2.2286 0.0 0 +M V30 4 C -1.3277 -2.2286 0.0 0 +M V30 5 C 2.5724 -1.4699 0.0 0 +M V30 6 C -1.3395 -3.7223 0.0 0 +M V30 7 C -2.6435 -1.4699 0.0 0 +M V30 8 C 3.8646 -2.2049 0.0 0 +M V30 9 C -2.6554 -4.4692 0.0 0 +M V30 10 C -0.0474 -4.4692 0.0 0 +M V30 11 C -3.9594 -2.2049 0.0 0 +M V30 12 C 5.1567 -1.4462 0.0 0 +M V30 13 C -3.9713 -3.6986 0.0 0 +M V30 14 C 6.52 -2.0508 0.0 0 +M V30 15 C 5.299 0.0592 0.0 0 +M V30 16 C -5.2871 -4.4455 0.0 0 +M V30 17 C 7.5158 -0.9246 0.0 0 +M V30 18 C 6.9705 -3.4734 0.0 0 +M V30 19 N 6.7571 0.3793 0.0 0 +M V30 20 C 8.974 -1.221 0.0 0 +M V30 21 C 8.4286 -3.7697 0.0 0 +M V30 22 C 9.4244 -2.6435 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 12 14 +M V30 14 2 12 15 +M V30 15 1 13 16 +M V30 16 2 14 17 +M V30 17 1 14 18 +M V30 18 1 15 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 2 20 22 +M V30 22 1 11 13 +M V30 23 1 17 19 +M V30 24 1 21 22 +M V30 END BOND +M V30 END CTAB +M END +> +456 + +> +Z26395629 + +> +292.375 + +> +3.606 + +> +2 + +> +44.890 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL551001 + +> +0.92 + +$$$$ +Compound 457 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4944 0.0 0 +M V30 3 N 1.2809 -2.2298 0.0 0 +M V30 4 C -1.3284 -2.2298 0.0 0 +M V30 5 C 2.5738 -1.4707 0.0 0 +M V30 6 C -1.3402 -3.7243 0.0 0 +M V30 7 C -2.645 -1.4707 0.0 0 +M V30 8 C 3.8667 -2.2061 0.0 0 +M V30 9 C -2.6568 -4.4716 0.0 0 +M V30 10 C -0.0474 -4.4716 0.0 0 +M V30 11 C -3.9615 -2.2061 0.0 0 +M V30 12 C 5.1595 -1.447 0.0 0 +M V30 13 C -3.9734 -3.7006 0.0 0 +M V30 14 C -2.6687 -5.9661 0.0 0 +M V30 15 C 6.5235 -2.0519 0.0 0 +M V30 16 C 5.3018 0.0593 0.0 0 +M V30 17 C 7.5199 -0.9251 0.0 0 +M V30 18 C 6.9742 -3.4752 0.0 0 +M V30 19 N 6.7607 0.3795 0.0 0 +M V30 20 C 8.9788 -1.2216 0.0 0 +M V30 21 C 8.4332 -3.7718 0.0 0 +M V30 22 C 9.4295 -2.645 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 9 14 +M V30 14 1 12 15 +M V30 15 2 12 16 +M V30 16 2 15 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 2 20 22 +M V30 22 1 11 13 +M V30 23 1 17 19 +M V30 24 1 21 22 +M V30 END BOND +M V30 END CTAB +M END +> +457 + +> +Z26395598 + +> +292.375 + +> +3.556 + +> +2 + +> +44.890 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL551001 + +> +0.91 + +$$$$ +Compound 458 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4933 0.0 0 +M V30 3 N 1.28 -2.2282 0.0 0 +M V30 4 C -1.3274 -2.2282 0.0 0 +M V30 5 C 2.5719 -1.4696 0.0 0 +M V30 6 C -1.3393 -3.7216 0.0 0 +M V30 7 C -2.643 -1.4696 0.0 0 +M V30 8 C 3.8638 -2.2045 0.0 0 +M V30 9 C -2.6549 -4.4683 0.0 0 +M V30 10 C -3.9586 -2.2045 0.0 0 +M V30 11 C 5.1557 -1.4459 0.0 0 +M V30 12 C -3.9705 -3.6979 0.0 0 +M V30 13 C -2.6667 -5.9617 0.0 0 +M V30 14 C -5.2742 -1.4459 0.0 0 +M V30 15 C 6.5187 -2.0504 0.0 0 +M V30 16 C 5.2979 0.0592 0.0 0 +M V30 17 C 7.5143 -0.9244 0.0 0 +M V30 18 C 6.9691 -3.4727 0.0 0 +M V30 19 N 6.7558 0.3792 0.0 0 +M V30 20 C 8.9722 -1.2207 0.0 0 +M V30 21 C 8.427 -3.769 0.0 0 +M V30 22 C 9.4226 -2.643 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 2 11 16 +M V30 16 2 15 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 2 20 22 +M V30 22 1 10 12 +M V30 23 1 17 19 +M V30 24 1 21 22 +M V30 END BOND +M V30 END CTAB +M END +> +458 + +> +Z26395572 + +> +292.375 + +> +3.946 + +> +2 + +> +44.890 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL343641 + +> +0.94 + +$$$$ +Compound 459 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4936 0.0 0 +M V30 3 N 1.2802 -2.2286 0.0 0 +M V30 4 C -1.3276 -2.2286 0.0 0 +M V30 5 C 2.5723 -1.4699 0.0 0 +M V30 6 C -1.3395 -3.7222 0.0 0 +M V30 7 C -2.6435 -1.4699 0.0 0 +M V30 8 C 3.8644 -2.2048 0.0 0 +M V30 9 C -2.6553 -4.469 0.0 0 +M V30 10 C -3.9593 -2.2048 0.0 0 +M V30 11 C 5.1566 -1.4462 0.0 0 +M V30 12 C -3.9711 -3.6985 0.0 0 +M V30 13 C -2.6672 -5.9626 0.0 0 +M V30 14 C 6.5198 -2.0507 0.0 0 +M V30 15 C 5.2988 0.0592 0.0 0 +M V30 16 C -5.287 -4.4453 0.0 0 +M V30 17 C 7.5156 -0.9246 0.0 0 +M V30 18 C 6.9703 -3.4733 0.0 0 +M V30 19 N 6.7569 0.3793 0.0 0 +M V30 20 C 8.9736 -1.2209 0.0 0 +M V30 21 C 8.4283 -3.7696 0.0 0 +M V30 22 C 9.4241 -2.6435 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 1 11 14 +M V30 14 2 11 15 +M V30 15 1 12 16 +M V30 16 2 14 17 +M V30 17 1 14 18 +M V30 18 1 15 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 2 20 22 +M V30 22 1 10 12 +M V30 23 1 17 19 +M V30 24 1 21 22 +M V30 END BOND +M V30 END CTAB +M END +> +459 + +> +Z26395677 + +> +292.375 + +> +3.896 + +> +2 + +> +44.890 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL343641 + +> +0.92 + +$$$$ +Compound 460 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3144 -0.7342 0.0 0 +M V30 3 O -2.6289 0.0236 0.0 0 +M V30 4 C -1.3263 -2.2263 0.0 0 +M V30 5 C -2.6408 -2.9605 0.0 0 +M V30 6 C -0.0355 -2.9605 0.0 0 +M V30 7 C -2.6526 -4.4526 0.0 0 +M V30 8 C -3.9553 -2.2026 0.0 0 +M V30 9 C -0.0473 -4.4526 0.0 0 +M V30 10 C 1.3855 -2.4868 0.0 0 +M V30 11 N -1.3618 -5.1869 0.0 0 +M V30 12 C -3.9671 -5.1869 0.0 0 +M V30 13 C -5.2698 -2.9368 0.0 0 +M V30 14 C 1.3737 -4.9027 0.0 0 +M V30 15 C 2.2618 -3.6947 0.0 0 +M V30 16 C -5.2816 -4.429 0.0 0 +M V30 17 C 1.8237 -6.3237 0.0 0 +M V30 18 C 0.8052 -7.4251 0.0 0 +M V30 19 C 1.2552 -8.8461 0.0 0 +M V30 20 C -0.675 -7.1053 0.0 0 +M V30 21 C 0.2368 -9.9475 0.0 0 +M V30 22 C -1.6934 -8.2066 0.0 0 +M V30 23 C -1.2434 -9.6277 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 4 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 7 12 +M V30 12 1 8 13 +M V30 13 1 9 14 +M V30 14 1 10 15 +M V30 15 1 12 16 +M V30 16 2 14 17 CFG=2 +M V30 17 1 17 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 2 21 23 +M V30 23 2 9 11 +M V30 24 2 13 16 +M V30 25 1 14 15 +M V30 26 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +460 + +> +Z46108263 + +> +301.339 + +> +4.979 + +> +1 + +> +50.190 + +> +2 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1996831 + +> +0.93 + +$$$$ +Compound 461 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2832 0.7652 0.0 0 +M V30 3 O 2.5664 0.0235 0.0 0 +M V30 4 C 1.2714 2.2721 0.0 0 +M V30 5 C -0.0353 3.0255 0.0 0 +M V30 6 C 2.5546 3.0255 0.0 0 +M V30 7 N -1.342 2.2956 0.0 0 +M V30 8 C -0.047 4.5325 0.0 0 +M V30 9 C 2.5429 4.5325 0.0 0 +M V30 10 C -2.6488 3.0491 0.0 0 +M V30 11 C 1.2361 5.2977 0.0 0 +M V30 12 O 3.8261 5.2977 0.0 0 +M V30 13 O -2.6606 4.556 0.0 0 +M V30 14 C -3.9556 2.3192 0.0 0 +M V30 15 O 1.2243 6.8046 0.0 0 +M V30 16 C 5.1093 4.556 0.0 0 +M V30 17 C -5.2624 3.0726 0.0 0 +M V30 18 C -3.9674 0.8358 0.0 0 +M V30 19 C -0.0824 7.558 0.0 0 +M V30 20 C -6.5691 2.3427 0.0 0 +M V30 21 C -5.2741 0.0941 0.0 0 +M V30 22 C -6.5809 0.8594 0.0 0 +M V30 23 C -7.8759 3.0962 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 2 14 17 +M V30 17 1 14 18 +M V30 18 1 15 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 2 20 22 +M V30 22 1 20 23 +M V30 23 1 9 11 +M V30 24 1 21 22 +M V30 END BOND +M V30 END CTAB +M END +> +461 + +> +Z57161389 + +> +315.321 + +> +3.466 + +> +2 + +> +84.860 + +> +5 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1997495 + +> +0.94 + +$$$$ +Compound 462 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2834 0.7653 0.0 0 +M V30 3 O 2.5668 0.0235 0.0 0 +M V30 4 C 1.2716 2.2725 0.0 0 +M V30 5 C -0.0353 3.026 0.0 0 +M V30 6 C 2.555 3.026 0.0 0 +M V30 7 N -1.3423 2.296 0.0 0 +M V30 8 C -0.047 4.5332 0.0 0 +M V30 9 C 2.5433 4.5332 0.0 0 +M V30 10 C -2.6492 3.0496 0.0 0 +M V30 11 C 1.2363 5.2985 0.0 0 +M V30 12 O 3.8267 5.2985 0.0 0 +M V30 13 O -2.661 4.5567 0.0 0 +M V30 14 C -3.9562 2.3196 0.0 0 +M V30 15 O 1.2245 6.8057 0.0 0 +M V30 16 C 5.1101 4.5567 0.0 0 +M V30 17 C -3.968 0.8359 0.0 0 +M V30 18 C -5.2632 3.0731 0.0 0 +M V30 19 C -0.0824 7.5593 0.0 0 +M V30 20 C -5.275 0.0941 0.0 0 +M V30 21 C -6.5702 2.3431 0.0 0 +M V30 22 C -6.582 0.8595 0.0 0 +M V30 23 C -7.889 0.1177 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 2 14 17 +M V30 17 1 14 18 +M V30 18 1 15 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 2 20 22 +M V30 22 1 22 23 +M V30 23 1 9 11 +M V30 24 1 21 22 +M V30 END BOND +M V30 END CTAB +M END +> +462 + +> +Z85891688 + +> +315.321 + +> +3.466 + +> +2 + +> +84.860 + +> +5 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1997495 + +> +0.96 + +$$$$ +Compound 463 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2938 -0.7359 0.0 0 +M V30 3 N 2.5876 0.0237 0.0 0 +M V30 4 C 1.2819 -2.2315 0.0 0 +M V30 5 C -0.0356 -2.9794 0.0 0 +M V30 6 C 2.5758 -2.9794 0.0 0 +M V30 7 C -0.0474 -4.475 0.0 0 +M V30 8 C 2.5639 -4.475 0.0 0 +M V30 9 N -1.365 -5.2109 0.0 0 +M V30 10 C 1.2463 -5.2109 0.0 0 +M V30 11 C -1.3769 -6.7066 0.0 0 +M V30 12 O -2.6945 -7.4425 0.0 0 +M V30 13 C -0.083 -7.4425 0.0 0 +M V30 14 C -0.0949 -8.9382 0.0 0 +M V30 15 C 1.365 -8.5939 0.0 0 +M V30 16 C 0.546 -10.2795 0.0 0 +M V30 17 C -1.5787 -8.5939 0.0 0 +M V30 18 O 2.374 -9.686 0.0 0 +M V30 19 O 1.7923 -7.1458 0.0 0 +M V30 20 C -0.1187 -11.6208 0.0 0 +M V30 21 C -2.7657 -9.5198 0.0 0 +M V30 22 C -1.6024 -11.9413 0.0 0 +M V30 23 C -2.7776 -11.0154 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 2 7 10 +M V30 10 1 9 11 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 1 15 19 +M V30 19 1 16 20 +M V30 20 1 17 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 8 10 +M V30 24 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +463 + +> +Z221346210 + +> +318.368 + +> +2.331 + +> +3 + +> +109.490 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3414766 + +> +0.93 + +$$$$ +Compound 464 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2923 -0.7469 0.0 0 +M V30 3 N 2.5846 0.0118 0.0 0 +M V30 4 C 1.2804 -2.2408 0.0 0 +M V30 5 C 2.5727 1.5294 0.0 0 +M V30 6 C -0.0355 -2.9877 0.0 0 +M V30 7 C 2.5727 -2.9877 0.0 0 +M V30 8 C -0.0474 -4.4816 0.0 0 +M V30 9 C 2.5609 -4.4816 0.0 0 +M V30 10 N -1.3634 -5.2167 0.0 0 +M V30 11 C 1.2448 -5.2167 0.0 0 +M V30 12 C -1.3753 -6.7105 0.0 0 +M V30 13 O -2.6913 -7.4575 0.0 0 +M V30 14 C -0.0829 -7.4575 0.0 0 +M V30 15 C -0.0948 -8.9513 0.0 0 +M V30 16 C 1.3753 -8.6787 0.0 0 +M V30 17 C 0.4031 -10.3504 0.0 0 +M V30 18 C -1.5887 -8.6787 0.0 0 +M V30 19 O 2.3356 -9.8168 0.0 0 +M V30 20 O 1.8732 -7.2559 0.0 0 +M V30 21 C -0.5809 -11.4886 0.0 0 +M V30 22 C -2.5727 -9.8168 0.0 0 +M V30 23 C -2.0748 -11.2159 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 2 8 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 1 15 18 +M V30 18 2 16 19 +M V30 19 1 16 20 +M V30 20 1 17 21 +M V30 21 1 18 22 +M V30 22 1 21 23 +M V30 23 1 9 11 +M V30 24 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +464 + +> +Z169864340 + +> +318.368 + +> +1.978 + +> +3 + +> +95.500 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3414766 + +> +0.89 + +$$$$ +Compound 465 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2864 -0.7435 0.0 0 +M V30 3 O 2.5728 0.0118 0.0 0 +M V30 4 C 1.2745 -2.2305 0.0 0 +M V30 5 C 3.8592 -0.7317 0.0 0 +M V30 6 C -0.0354 -2.974 0.0 0 +M V30 7 C 2.5609 -2.974 0.0 0 +M V30 8 N -1.3454 -2.2069 0.0 0 +M V30 9 C -0.0472 -4.461 0.0 0 +M V30 10 C 2.5491 -4.461 0.0 0 +M V30 11 C -2.6554 -2.9504 0.0 0 +M V30 12 C 1.2391 -5.1928 0.0 0 +M V30 13 O -2.6672 -4.4374 0.0 0 +M V30 14 C -3.9654 -2.1833 0.0 0 +M V30 15 C -3.9772 -0.6727 0.0 0 +M V30 16 C -5.2754 -2.9268 0.0 0 +M V30 17 C -5.2872 0.0826 0.0 0 +M V30 18 C -6.5854 -2.1597 0.0 0 +M V30 19 O -5.299 1.5932 0.0 0 +M V30 20 C -6.5972 -0.6609 0.0 0 +M V30 21 C -4.0126 2.3485 0.0 0 +M V30 22 O -7.9072 0.0944 0.0 0 +M V30 23 C -9.2172 -0.6491 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 6 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 17 19 +M V30 19 2 17 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 22 23 +M V30 23 1 10 12 +M V30 24 1 18 20 +M V30 END BOND +M V30 END CTAB +M END +> +465 + +> +Z27656157 + +> +315.321 + +> +2.860 + +> +1 + +> +73.860 + +> +6 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1997495 + +> +0.95 + +$$$$ +Compound 466 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2932 -0.7474 0.0 0 +M V30 3 N 2.5865 0.0118 0.0 0 +M V30 4 C 1.2814 -2.2424 0.0 0 +M V30 5 C -0.0355 -2.9899 0.0 0 +M V30 6 C 2.5746 -2.9899 0.0 0 +M V30 7 C -0.0474 -4.4849 0.0 0 +M V30 8 C 2.5628 -4.4849 0.0 0 +M V30 9 N -1.3644 -5.2323 0.0 0 +M V30 10 C 1.2458 -5.2323 0.0 0 +M V30 11 C -1.3763 -6.7273 0.0 0 +M V30 12 O -0.083 -7.4748 0.0 0 +M V30 13 C -2.6933 -7.4748 0.0 0 +M V30 14 C -2.7051 -8.9698 0.0 0 +M V30 15 C -1.2339 -8.6969 0.0 0 +M V30 16 C -3.2272 -10.3698 0.0 0 +M V30 17 C -4.2001 -8.6969 0.0 0 +M V30 18 C -0.2728 -9.8359 0.0 0 +M V30 19 C -2.2661 -11.5088 0.0 0 +M V30 20 C -5.1849 -9.8359 0.0 0 +M V30 21 C -0.7949 -11.236 0.0 0 +M V30 22 O -4.6866 -11.236 0.0 0 +M V30 23 O -6.6799 -9.563 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 2 7 10 +M V30 10 1 9 11 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 1 17 20 +M V30 20 1 18 21 +M V30 21 2 20 22 +M V30 22 1 20 23 +M V30 23 1 8 10 +M V30 24 1 19 21 +M V30 END BOND +M V30 END CTAB +M END +> +466 + +> +Z221346194 + +> +318.368 + +> +2.236 + +> +3 + +> +109.490 + +> +6 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3414770 + +> +0.9 + +$$$$ +Compound 467 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3087 -0.7428 0.0 0 +M V30 3 O -1.3205 -2.2284 0.0 0 +M V30 4 C -2.6175 0.0235 0.0 0 +M V30 5 C -2.6293 1.5327 0.0 0 +M V30 6 C -3.9263 -0.7192 0.0 0 +M V30 7 C -3.9381 2.2874 0.0 0 +M V30 8 C -5.235 0.0471 0.0 0 +M V30 9 C -5.2468 1.5563 0.0 0 +M V30 10 C -3.9499 3.7966 0.0 0 +M V30 11 N -2.6647 4.563 0.0 0 +M V30 12 C -2.6765 6.0722 0.0 0 +M V30 13 O -3.9852 6.8268 0.0 0 +M V30 14 C -1.3913 6.8268 0.0 0 +M V30 15 C -0.1061 6.084 0.0 0 +M V30 16 C -1.4031 8.336 0.0 0 +M V30 17 C 1.179 6.8386 0.0 0 +M V30 18 C -0.1179 9.0906 0.0 0 +M V30 19 O 2.4642 6.0958 0.0 0 +M V30 20 C 1.1672 8.3478 0.0 0 +M V30 21 C 3.7494 6.8504 0.0 0 +M V30 22 O 2.4524 9.1024 0.0 0 +M V30 23 C 3.7376 8.3596 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 17 19 +M V30 19 2 17 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 8 9 +M V30 24 1 18 20 +M V30 25 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +467 + +> +Z600504866 + +> +313.305 + +> +2.717 + +> +2 + +> +84.860 + +> +4 + +> +ATM + +> + + +> + + +$$$$ +Compound 468 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2969 -0.7376 0.0 0 +M V30 3 C 2.5938 0.0237 0.0 0 +M V30 4 C 1.285 -2.2368 0.0 0 +M V30 5 C 3.8907 -0.7139 0.0 0 +M V30 6 C 2.5819 -2.9745 0.0 0 +M V30 7 C 3.8788 -2.213 0.0 0 +M V30 8 C 5.1757 -2.9507 0.0 0 +M V30 9 C 6.4727 -2.1892 0.0 0 +M V30 10 N 7.7696 -2.9269 0.0 0 +M V30 11 C 9.0665 -2.1655 0.0 0 +M V30 12 O 9.0546 -0.6425 0.0 0 +M V30 13 C 10.3634 -2.9032 0.0 0 +M V30 14 N 11.6603 -2.1417 0.0 0 +M V30 15 C 10.3515 -4.4023 0.0 0 +M V30 16 N 12.9573 -2.8794 0.0 0 +M V30 17 C 11.6484 -5.14 0.0 0 +M V30 18 C 9.0308 -5.14 0.0 0 +M V30 19 C 12.9454 -4.3785 0.0 0 +M V30 20 C 11.6366 -6.6392 0.0 0 +M V30 21 C 9.0189 -6.6392 0.0 0 +M V30 22 O 14.2423 -5.1162 0.0 0 +M V30 23 C 10.3158 -7.3888 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 1 9 10 +M V30 10 1 10 11 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 2 19 22 +M V30 22 2 20 23 +M V30 23 1 6 7 +M V30 24 1 17 19 +M V30 25 1 21 23 +M V30 END BOND +M V30 END CTAB +M END +> +468 + +> +Z26420358 + +> +327.765 + +> +2.002 + +> +2 + +> +70.560 + +> +4 + +> +parp10 + +> + + +> + + +$$$$ +Compound 469 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.3057 -0.7293 0.0 0 +M V30 3 C -2.6115 0.0235 0.0 0 +M V30 4 C -1.3175 -2.2116 0.0 0 +M V30 5 C -3.9173 -0.7058 0.0 0 +M V30 6 C -2.6233 -2.9527 0.0 0 +M V30 7 N -5.2231 0.047 0.0 0 +M V30 8 C -3.9291 -2.188 0.0 0 +M V30 9 C -6.5289 -0.6823 0.0 0 +M V30 10 C -5.2349 -2.9292 0.0 0 +M V30 11 N -6.5407 -2.1645 0.0 0 +M V30 12 C -7.8347 0.0705 0.0 0 +M V30 13 O -5.2467 -4.4114 0.0 0 +M V30 14 N -7.8465 1.5763 0.0 0 +M V30 15 C -9.1523 2.3292 0.0 0 CFG=2 +M V30 16 C -9.164 3.835 0.0 0 +M V30 17 C -10.4581 1.5998 0.0 0 +M V30 18 C -10.4698 4.5879 0.0 0 +M V30 19 C -7.8818 4.5879 0.0 0 +M V30 20 C -10.4816 6.0937 0.0 0 +M V30 21 C -7.8935 6.0937 0.0 0 +M V30 22 C -9.1993 6.8583 0.0 0 +M V30 23 Cl -9.2111 8.3641 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 1 15 14 CFG=1 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 2 20 22 +M V30 22 1 22 23 +M V30 23 1 6 8 +M V30 24 1 10 11 +M V30 25 1 21 22 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 15) +M V30 END COLLECTION +M V30 END CTAB +M END +> +469 + +> +Z44470832 + +> +348.227 + +> +2.624 + +> +2 + +> +53.490 + +> +4 + +> +parp10 + +> + + +> + + +$$$$ +Compound 470 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4898 0.0 0 +M V30 3 C -1.3243 -2.2229 0.0 0 +M V30 4 C 1.277 -2.2229 0.0 0 +M V30 5 C -1.3361 -3.7128 0.0 0 +M V30 6 C 1.2652 -3.7128 0.0 0 +M V30 7 N -2.6486 -4.4459 0.0 0 +M V30 8 C -0.0472 -4.4459 0.0 0 +M V30 9 C -2.6604 -5.9358 0.0 0 +M V30 10 C -0.0591 -5.9358 0.0 0 +M V30 11 N -1.3716 -6.6807 0.0 0 +M V30 12 C -3.9729 -6.6807 0.0 0 +M V30 13 O 1.2297 -6.6807 0.0 0 +M V30 14 N -5.2854 -5.9121 0.0 0 +M V30 15 C -6.5979 -6.657 0.0 0 CFG=2 +M V30 16 C -7.9104 -5.8885 0.0 0 +M V30 17 C -6.6097 -8.1469 0.0 0 +M V30 18 S -8.076 -4.3868 0.0 0 +M V30 19 C -9.2939 -6.4915 0.0 0 +M V30 20 C -5.3209 -8.88 0.0 0 +M V30 21 C -7.9222 -8.88 0.0 0 +M V30 22 C -9.554 -4.0675 0.0 0 +M V30 23 C -10.3107 -5.3682 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 1 15 14 CFG=1 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 16 19 +M V30 19 1 17 20 +M V30 20 1 17 21 +M V30 21 1 18 22 +M V30 22 1 19 23 +M V30 23 1 6 8 +M V30 24 1 10 11 +M V30 25 2 22 23 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 15) +M V30 END COLLECTION +M V30 END CTAB +M END +> +470 + +> +Z106901900 + +> +347.862 + +> +2.485 + +> +2 + +> +53.490 + +> +5 + +> +parp10 + +> + + +> + + +$$$$ +Compound 471 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3113 -0.7324 0.0 0 +M V30 3 O -2.6227 0.0236 0.0 0 +M V30 4 C -1.3231 -2.221 0.0 0 +M V30 5 C -2.6345 1.5358 0.0 0 +M V30 6 C -0.0354 -2.9653 0.0 0 +M V30 7 C -2.6345 -2.9653 0.0 0 +M V30 8 C -0.0472 -4.4539 0.0 0 +M V30 9 C -2.6463 -4.4539 0.0 0 +M V30 10 N -1.3586 -5.1863 0.0 0 +M V30 11 C -1.3704 -6.6749 0.0 0 +M V30 12 C -2.6817 -7.4192 0.0 0 +M V30 13 O -2.6936 -8.9078 0.0 0 +M V30 14 N -3.9931 -6.6513 0.0 0 +M V30 15 C -5.3045 -7.3956 0.0 0 +M V30 16 C -6.6158 -6.6277 0.0 0 +M V30 17 C -5.3163 -8.8841 0.0 0 +M V30 18 O -6.6277 -5.1154 0.0 0 +M V30 19 C -7.9272 -7.3719 0.0 0 +M V30 20 C -6.6277 -9.6166 0.0 0 +M V30 21 C -5.3399 -4.3593 0.0 0 +M V30 22 C -7.939 -8.8605 0.0 0 +M V30 23 C -6.6395 -11.1052 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 1 4 7 +M V30 7 1 6 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 1 20 23 +M V30 23 1 9 10 +M V30 24 1 20 22 +M V30 END BOND +M V30 END CTAB +M END +> +471 + +> +Z46245854 + +> +320.383 + +> +1.234 + +> +1 + +> +67.870 + +> +6 + +> +ATM + +> + + +> + + +$$$$ +Compound 472 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.2921 -0.735 0.0 0 +M V30 3 F 2.0271 0.5808 0.0 0 +M V30 4 F 0.5216 -2.0271 0.0 0 +M V30 5 C 2.5843 -1.4818 0.0 0 +M V30 6 C 2.5725 -2.9755 0.0 0 +M V30 7 C 3.8765 -0.7112 0.0 0 +M V30 8 C 3.8647 -3.7105 0.0 0 +M V30 9 C 5.1687 -1.4581 0.0 0 +M V30 10 C 5.1568 -2.9518 0.0 0 +M V30 11 C 6.449 -3.6868 0.0 0 +M V30 12 C 7.7412 -2.9281 0.0 0 +M V30 13 O 7.7294 -1.4107 0.0 0 +M V30 14 N 9.0334 -3.6631 0.0 0 +M V30 15 C 10.3256 -2.9044 0.0 0 +M V30 16 C 10.3137 -1.387 0.0 0 +M V30 17 C 11.6178 -3.6394 0.0 0 +M V30 18 C 11.6059 -0.6164 0.0 0 +M V30 19 C 12.91 -2.8807 0.0 0 +M V30 20 C 11.5941 0.9009 0.0 0 +M V30 21 C 12.8981 -1.3633 0.0 0 +M V30 22 O 10.2782 1.6596 0.0 0 +M V30 23 C 12.8863 1.6596 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 2 5 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 2 8 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 17 19 +M V30 19 1 18 20 +M V30 20 2 18 21 +M V30 21 2 20 22 +M V30 22 1 20 23 +M V30 23 1 9 10 +M V30 24 1 19 21 +M V30 END BOND +M V30 END CTAB +M END +> +472 + +> +Z169621246 + +> +321.294 + +> +3.490 + +> +1 + +> +46.170 + +> +5 + +> +parp14 + +> + + +> + + +$$$$ +Compound 473 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2948 0.7602 0.0 0 +M V30 3 C 2.5896 0.0237 0.0 0 +M V30 4 C 1.2829 2.2808 0.0 0 +M V30 5 C 3.8845 0.784 0.0 0 +M V30 6 C 2.5777 3.041 0.0 0 +M V30 7 C 3.8726 2.3045 0.0 0 +M V30 8 O 5.1674 3.0648 0.0 0 +M V30 9 C 6.4622 2.3283 0.0 0 CFG=2 +M V30 10 C 7.7571 3.0885 0.0 0 +M V30 11 C 6.4504 0.8315 0.0 0 +M V30 12 O 7.7452 4.6091 0.0 0 +M V30 13 N 9.0519 2.352 0.0 0 +M V30 14 C 10.3467 3.1123 0.0 0 +M V30 15 C 11.6416 2.3758 0.0 0 +M V30 16 N 13.0077 3.0054 0.0 0 +M V30 17 N 11.7841 0.8909 0.0 0 +M V30 18 C 14.0055 1.9006 0.0 0 +M V30 19 C 13.2453 0.5939 0.0 0 +M V30 20 C 15.5023 1.9125 0.0 0 +M V30 21 C 13.9937 -0.7008 0.0 0 +M V30 22 C 16.2507 0.6177 0.0 0 +M V30 23 C 15.4904 -0.6889 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 9 8 CFG=3 +M V30 9 1 9 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 1 10 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 2 21 23 +M V30 23 1 6 7 +M V30 24 2 18 19 +M V30 25 1 22 23 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 9) +M V30 END COLLECTION +M V30 END CTAB +M END +> +473 + +> +Z26782332 + +> +329.781 + +> +3.288 + +> +2 + +> +67.010 + +> +5 + +> +ATM + +> + + +> + + +$$$$ +Compound 474 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.7551 -1.2861 0.0 0 +M V30 3 C -1.0147 1.1209 0.0 0 +M V30 4 N -2.23 -0.9675 0.0 0 +M V30 5 C -0.1533 -2.643 0.0 0 +M V30 6 C -2.3952 0.5191 0.0 0 +M V30 7 C -0.9085 -3.9291 0.0 0 +M V30 8 C 1.2979 -2.938 0.0 0 +M V30 9 C -3.705 1.2861 0.0 0 +M V30 10 S 0.0825 -5.0265 0.0 0 +M V30 11 C 1.4395 -4.4129 0.0 0 +M V30 12 O -5.0147 0.5427 0.0 0 +M V30 13 N -3.7168 2.7964 0.0 0 +M V30 14 C -2.4306 3.5634 0.0 0 +M V30 15 C -5.0265 3.5634 0.0 0 +M V30 16 C -2.4424 5.0737 0.0 0 +M V30 17 C -1.1445 2.82 0.0 0 +M V30 18 C -5.0383 5.0737 0.0 0 +M V30 19 N -3.7522 5.8406 0.0 0 +M V30 20 C -1.1563 5.8406 0.0 0 +M V30 21 C 0.1415 3.587 0.0 0 +M V30 22 O -6.348 5.8406 0.0 0 +M V30 23 C 0.1297 5.0973 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 1 13 14 +M V30 14 1 13 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 1 16 20 +M V30 20 2 17 21 +M V30 21 2 18 22 +M V30 22 2 20 23 +M V30 23 1 4 6 +M V30 24 1 10 11 +M V30 25 1 18 19 +M V30 26 1 21 23 +M V30 END BOND +M V30 END CTAB +M END +> +474 + +> +Z116807288 + +> +341.407 + +> +2.128 + +> +1 + +> +62.300 + +> +2 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL2037101 + +> +0.92 + +$$$$ +Compound 475 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -0.7549 1.3093 0.0 0 +M V30 3 F 0.5308 2.0642 0.0 0 +M V30 4 F -2.0642 0.5779 0.0 0 +M V30 5 C -1.5216 2.6186 0.0 0 +M V30 6 C -0.7785 3.9279 0.0 0 +M V30 7 C -3.0314 2.6304 0.0 0 +M V30 8 C -1.5452 5.2372 0.0 0 +M V30 9 C -3.7981 3.9397 0.0 0 +M V30 10 C -3.055 5.249 0.0 0 +M V30 11 C -0.8021 6.5465 0.0 0 +M V30 12 C 0.6841 6.5583 0.0 0 +M V30 13 O 1.4154 5.2726 0.0 0 +M V30 14 N 1.4154 7.8676 0.0 0 +M V30 15 C 2.9017 7.8794 0.0 0 +M V30 16 N 3.7745 9.1061 0.0 0 +M V30 17 N 3.7745 6.6763 0.0 0 +M V30 18 C 5.19 8.6579 0.0 0 +M V30 19 C 5.19 7.1481 0.0 0 +M V30 20 C 6.4757 9.4246 0.0 0 +M V30 21 C 6.4757 6.405 0.0 0 +M V30 22 C 7.7615 8.6933 0.0 0 +M V30 23 C 7.7615 7.1717 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 2 5 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 2 8 10 +M V30 10 1 8 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 2 21 23 +M V30 23 1 9 10 +M V30 24 2 18 19 +M V30 25 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +475 + +> +Z27666322 + +> +319.281 + +> +3.833 + +> +2 + +> +57.780 + +> +4 + +> +DNA_pk + +> + + +> + + +$$$$ +Compound 476 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.0166 1.123 0.0 0 +M V30 3 N -2.4943 0.8274 0.0 0 +M V30 4 C -0.5674 2.5652 0.0 0 +M V30 5 C -2.9671 -0.591 0.0 0 +M V30 6 N -1.4658 3.7946 0.0 0 +M V30 7 C 0.8511 3.038 0.0 0 +M V30 8 C -4.4448 -0.8865 0.0 0 +M V30 9 C -1.9741 -1.6904 0.0 0 +M V30 10 N -0.591 5.024 0.0 0 +M V30 11 C 0.8393 4.5512 0.0 0 +M V30 12 C 2.1396 2.2933 0.0 0 +M V30 13 C -4.9176 -2.3051 0.0 0 +M V30 14 C -5.4614 0.2364 0.0 0 +M V30 15 C -2.447 -3.109 0.0 0 +M V30 16 C 2.1278 5.3195 0.0 0 +M V30 17 C 3.4281 3.0617 0.0 0 +M V30 18 C -3.9246 -3.4045 0.0 0 +M V30 19 O -1.454 -4.2083 0.0 0 +M V30 20 C 3.4163 4.5748 0.0 0 +M V30 21 O -4.3975 -4.8231 0.0 0 +M V30 22 C -1.9268 -5.6269 0.0 0 +M V30 23 C -3.4045 -5.9224 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 7 12 +M V30 12 1 8 13 +M V30 13 1 8 14 +M V30 14 2 9 15 +M V30 15 1 11 16 +M V30 16 2 12 17 +M V30 17 2 13 18 +M V30 18 1 15 19 +M V30 19 2 16 20 +M V30 20 1 18 21 +M V30 21 1 19 22 +M V30 22 1 21 23 +M V30 23 1 10 11 +M V30 24 1 15 18 +M V30 25 1 17 20 +M V30 END BOND +M V30 END CTAB +M END +> +476 + +> +Z33594984 + +> +311.335 + +> +2.463 + +> +2 + +> +76.240 + +> +4 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL2007002 + +> +0.9 + +$$$$ +Compound 477 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 F 9.3658 -1.7974 0.0 0 +M V30 3 C 8.0532 -1.0406 0.0 0 +M V30 4 C 8.0414 0.473 0.0 0 +M V30 5 C 6.7406 -1.7856 0.0 0 +M V30 6 C 9.3304 1.2298 0.0 0 +M V30 7 C 6.7287 1.2298 0.0 0 +M V30 8 C 5.4279 -1.0288 0.0 0 +M V30 9 O 9.3185 2.7435 0.0 0 +M V30 10 N 10.6194 0.4966 0.0 0 +M V30 11 C 5.4161 0.4966 0.0 0 +M V30 12 F 4.1153 -1.7738 0.0 0 +M V30 13 C 11.9084 1.2535 0.0 0 +M V30 14 C 4.1034 1.2535 0.0 0 +M V30 15 C 13.1974 0.5203 0.0 0 +M V30 16 C 14.4863 1.2771 0.0 0 +M V30 17 N 15.7753 0.5439 0.0 0 +M V30 18 C 17.0643 1.3008 0.0 0 +M V30 19 C 18.3533 0.5676 0.0 0 +M V30 20 C 18.3415 -0.9223 0.0 0 +M V30 21 C 19.6423 1.3244 0.0 0 +M V30 22 N 19.6305 -1.6674 0.0 0 +M V30 23 C 20.9313 0.5912 0.0 0 +M V30 24 C 20.9195 -0.9105 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 2 3 +M V30 2 2 3 4 +M V30 3 1 3 5 +M V30 4 1 4 6 +M V30 5 1 4 7 +M V30 6 2 5 8 +M V30 7 2 6 9 +M V30 8 1 6 10 +M V30 9 2 7 11 +M V30 10 1 8 12 +M V30 11 1 10 13 +M V30 12 1 11 14 +M V30 13 1 13 15 +M V30 14 1 15 16 +M V30 15 1 16 17 +M V30 16 1 17 18 +M V30 17 1 18 19 +M V30 18 2 19 20 +M V30 19 1 19 21 +M V30 20 1 20 22 +M V30 21 2 21 23 +M V30 22 2 22 24 +M V30 23 1 8 11 +M V30 24 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +477 + +> +Z1456122167 + +> +319.349 + +> +2.056 + +> +2 + +> +54.020 + +> +7 + +> +ATM + +> + + +> + + +$$$$ +Compound 478 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4897 0.0 0 +M V30 3 C -1.3242 -2.2228 0.0 0 +M V30 4 C 1.2769 -2.2228 0.0 0 +M V30 5 C -1.336 -3.7125 0.0 0 +M V30 6 C -2.6366 -1.4661 0.0 0 +M V30 7 C 1.2651 -3.7125 0.0 0 +M V30 8 O -0.0472 -4.4574 0.0 0 +M V30 9 C -2.6484 -4.4574 0.0 0 +M V30 10 O -2.6484 0.0472 0.0 0 +M V30 11 C -3.949 -2.1991 0.0 0 +M V30 12 C 2.5538 -4.4574 0.0 0 +M V30 13 C -3.9608 -3.6889 0.0 0 +M V30 14 C 3.8426 -3.6889 0.0 0 +M V30 15 C 2.542 -5.9471 0.0 0 +M V30 16 O -5.2732 -4.4337 0.0 0 +M V30 17 C 5.1313 -4.4337 0.0 0 +M V30 18 C 3.8307 -6.692 0.0 0 +M V30 19 C -6.5856 -3.6652 0.0 0 +M V30 20 C 5.1195 -5.9235 0.0 0 +M V30 21 C -7.898 -4.4101 0.0 0 +M V30 22 O -7.9098 -5.8998 0.0 0 +M V30 23 O -9.2104 -3.6416 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 6 11 +M V30 11 1 7 12 +M V30 12 2 9 13 +M V30 13 2 12 14 +M V30 14 1 12 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 19 21 +M V30 21 2 21 22 +M V30 22 1 21 23 +M V30 23 1 7 8 +M V30 24 1 11 13 +M V30 25 1 18 20 +M V30 END BOND +M V30 END CTAB +M END +> +478 + +> +Z1824566549 + +> +312.274 + +> +3.437 + +> +2 + +> +93.060 + +> +4 + +> +Tankyrase-1 + +> +CHEMBL327209 + +> +0.85 + +$$$$ +Compound 479 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4926 0.0 0 +M V30 3 N 1.2794 -2.2271 0.0 0 +M V30 4 C -1.3268 -2.2271 0.0 0 +M V30 5 C 2.5706 -1.4689 0.0 0 +M V30 6 C -2.6417 -1.4689 0.0 0 +M V30 7 C -1.3386 -3.7197 0.0 0 +M V30 8 C 3.8619 -2.2034 0.0 0 +M V30 9 C -3.9567 -2.2034 0.0 0 +M V30 10 C -2.6536 -4.4661 0.0 0 +M V30 11 C 5.1531 -1.4452 0.0 0 +M V30 12 C -3.9685 -3.696 0.0 0 +M V30 13 C 6.5155 -2.0494 0.0 0 +M V30 14 C 5.2953 0.0592 0.0 0 +M V30 15 C -5.2835 -4.4424 0.0 0 +M V30 16 C 7.5106 -0.924 0.0 0 +M V30 17 C 6.9657 -3.471 0.0 0 +M V30 18 N 6.7524 0.379 0.0 0 +M V30 19 C -5.2953 -5.935 0.0 0 +M V30 20 C -6.5984 -3.6723 0.0 0 +M V30 21 C 8.9677 -1.2201 0.0 0 +M V30 22 C 8.4228 -3.7671 0.0 0 +M V30 23 C 9.4179 -2.6417 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 11 13 +M V30 13 2 11 14 +M V30 14 1 12 15 +M V30 15 2 13 16 +M V30 16 1 13 17 +M V30 17 1 14 18 +M V30 18 1 15 19 +M V30 19 1 15 20 +M V30 20 1 16 21 +M V30 21 2 17 22 +M V30 22 2 21 23 +M V30 23 1 10 12 +M V30 24 1 16 18 +M V30 25 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +479 + +> +Z26395512 + +> +306.401 + +> +4.375 + +> +2 + +> +44.890 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL343641 + +> +0.9 + +$$$$ +Compound 480 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3117 0.7563 0.0 0 +M V30 3 N -1.3236 2.269 0.0 0 +M V30 4 C -2.6235 0.0236 0.0 0 +M V30 5 C -0.0354 3.0372 0.0 0 +M V30 6 C -3.9353 0.7799 0.0 0 +M V30 7 C -2.6353 -1.4654 0.0 0 +M V30 8 C -0.0472 4.5499 0.0 0 +M V30 9 C -5.2471 0.0472 0.0 0 +M V30 10 C -3.9471 -2.1981 0.0 0 +M V30 11 C 1.2408 5.3062 0.0 0 +M V30 12 C -5.2589 -1.4417 0.0 0 +M V30 13 C -6.6889 0.5199 0.0 0 +M V30 14 C 2.5999 4.7035 0.0 0 +M V30 15 C 1.3826 6.8071 0.0 0 +M V30 16 C -6.7007 -1.8908 0.0 0 +M V30 17 C -7.5871 -0.6854 0.0 0 +M V30 18 C 3.5926 5.8262 0.0 0 +M V30 19 C 3.049 3.2853 0.0 0 +M V30 20 N 2.8363 7.1262 0.0 0 +M V30 21 C 5.0462 5.5307 0.0 0 +M V30 22 C 4.5026 2.9899 0.0 0 +M V30 23 C 5.4953 4.1126 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 1 11 14 +M V30 14 2 11 15 +M V30 15 1 12 16 +M V30 16 1 13 17 +M V30 17 2 14 18 +M V30 18 1 14 19 +M V30 19 1 15 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 2 21 23 +M V30 23 1 10 12 +M V30 24 1 16 17 +M V30 25 1 18 20 +M V30 26 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +480 + +> +Z49503975 + +> +304.386 + +> +3.961 + +> +2 + +> +44.890 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL551001 + +> +0.88 + +$$$$ +Compound 481 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.2443 0.8451 0.0 0 +M V30 3 N -2.5942 0.1995 0.0 0 +M V30 4 C -1.1503 2.3477 0.0 0 CFG=1 +M V30 5 O 0.2817 1.9955 0.0 0 +M V30 6 C -2.6412 2.2068 0.0 0 +M V30 7 C -0.5517 3.7211 0.0 0 +M V30 8 N -3.5333 3.4277 0.0 0 +M V30 9 C -1.4438 4.942 0.0 0 +M V30 10 C -2.9346 4.8011 0.0 0 +M V30 11 C -5.0241 3.2868 0.0 0 +M V30 12 C -5.9163 4.5076 0.0 0 +M V30 13 O -7.4188 4.5194 0.0 0 +M V30 14 C -5.4702 5.9397 0.0 0 +M V30 15 N -7.8884 5.9515 0.0 0 +M V30 16 C -6.691 6.8319 0.0 0 +M V30 17 C -6.7028 8.3344 0.0 0 +M V30 18 C -5.4232 9.0857 0.0 0 +M V30 19 C -8.0058 9.0857 0.0 0 +M V30 20 C -5.435 10.5883 0.0 0 +M V30 21 C -8.0175 10.5883 0.0 0 +M V30 22 C -6.738 11.3396 0.0 0 +M V30 23 C -6.7497 12.8421 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 4 5 CFG=3 +M V30 5 1 4 6 +M V30 6 1 4 7 +M V30 7 1 6 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 1 8 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 2 12 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 2 20 22 +M V30 22 1 22 23 +M V30 23 1 9 10 +M V30 24 2 15 16 +M V30 25 1 21 22 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 4) +M V30 END COLLECTION +M V30 END CTAB +M END +> +481 + +> +Z2058556880 + +> +315.367 + +> +1.618 + +> +2 + +> +92.590 + +> +4 + +> +ATM + +> + + +> + + +$$$$ +Compound 482 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5085 0.0 0 +M V30 3 N -1.32 2.2746 0.0 0 +M V30 4 C 1.2728 2.2746 0.0 0 +M V30 5 C -1.3317 3.7832 0.0 0 +M V30 6 C 1.261 3.7832 0.0 0 +M V30 7 C 2.5575 1.5321 0.0 0 +M V30 8 N -0.0471 4.5375 0.0 0 +M V30 9 C -2.64 4.5375 0.0 0 +M V30 10 C 2.5457 4.5375 0.0 0 +M V30 11 C 3.8421 2.2982 0.0 0 +M V30 12 O -2.6517 6.046 0.0 0 +M V30 13 N -3.9482 3.8067 0.0 0 +M V30 14 C 3.8303 3.8067 0.0 0 +M V30 15 C -5.2564 4.561 0.0 0 +M V30 16 C -6.5646 3.8303 0.0 0 +M V30 17 C -5.6925 2.6282 0.0 0 +M V30 18 C -7.6843 2.8403 0.0 0 +M V30 19 C -7.1893 5.2093 0.0 0 +M V30 20 O -4.2192 2.7932 0.0 0 +M V30 21 N -6.3171 1.2728 0.0 0 +M V30 22 C -8.9925 3.6064 0.0 0 +M V30 23 C -8.6861 5.0678 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 1 16 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 17 21 +M V30 21 1 18 22 +M V30 22 1 19 23 +M V30 23 1 6 8 +M V30 24 1 11 14 +M V30 25 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +482 + +> +Z1213176656 + +> +314.339 + +> +0.878 + +> +3 + +> +113.650 + +> +4 + +> +parp15 + +> + + +> + + +$$$$ +Compound 483 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 0.9879 1.1173 0.0 0 +M V30 3 C 2.4346 0.8233 0.0 0 +M V30 4 C 0.5175 2.5523 0.0 0 +M V30 5 C 3.4226 1.9406 0.0 0 +M V30 6 C 1.5055 3.6696 0.0 0 +M V30 7 C 2.9522 3.3756 0.0 0 +M V30 8 C 3.9402 4.493 0.0 0 +M V30 9 C 3.4697 5.9279 0.0 0 +M V30 10 C 2.0347 6.3984 0.0 0 +M V30 11 C 4.3401 7.1511 0.0 0 +M V30 12 N 0.7292 5.6691 0.0 0 +M V30 13 C 2.023 7.9039 0.0 0 +M V30 14 C 3.4462 8.3744 0.0 0 +M V30 15 C -0.5763 6.4219 0.0 0 +M V30 16 C 0.7174 8.6566 0.0 0 +M V30 17 C -0.588 7.9274 0.0 0 +M V30 18 C -1.8818 5.6927 0.0 0 +M V30 19 C 0.7057 10.1621 0.0 0 +M V30 20 C -1.8936 8.6802 0.0 0 +M V30 21 C -3.1874 6.4454 0.0 0 +M V30 22 O 1.9877 10.9149 0.0 0 +M V30 23 O -0.5998 10.9149 0.0 0 +M V30 24 C -3.1992 7.9509 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 2 8 9 CFG=2 +M V30 9 1 9 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 1 10 13 +M V30 13 1 11 14 +M V30 14 1 12 15 +M V30 15 2 13 16 +M V30 16 1 15 17 +M V30 17 2 15 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 1 19 23 +M V30 23 1 20 24 +M V30 24 1 6 7 +M V30 25 1 13 14 +M V30 26 1 16 17 +M V30 27 2 21 24 +M V30 END BOND +M V30 END CTAB +M END +> +483 + +> +Z46108276 + +> +335.784 + +> +5.692 + +> +1 + +> +50.190 + +> +2 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1996831 + +> +0.87 + +$$$$ +Compound 484 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.314 -0.7339 0.0 0 +M V30 3 O -2.628 0.0236 0.0 0 +M V30 4 C -1.3258 -2.2255 0.0 0 +M V30 5 C -2.6398 -2.9595 0.0 0 +M V30 6 C -0.0355 -2.9595 0.0 0 +M V30 7 C -2.6517 -4.451 0.0 0 +M V30 8 C -3.9538 -2.2018 0.0 0 +M V30 9 C -0.0473 -4.451 0.0 0 +M V30 10 C 1.2548 -2.2018 0.0 0 +M V30 11 N -1.3613 -5.185 0.0 0 +M V30 12 C -3.9657 -5.185 0.0 0 +M V30 13 C -5.2679 -2.9358 0.0 0 +M V30 14 C 1.2429 -5.185 0.0 0 +M V30 15 C 2.5451 -2.9358 0.0 0 +M V30 16 C -5.2797 -4.4274 0.0 0 +M V30 17 C 1.2311 -6.6766 0.0 0 +M V30 18 C 2.5333 -4.4274 0.0 0 +M V30 19 C -0.0828 -7.4224 0.0 0 +M V30 20 C -0.0947 -8.914 0.0 0 +M V30 21 C -1.3968 -6.6648 0.0 0 +M V30 22 C -1.4087 -9.6598 0.0 0 +M V30 23 C -2.7109 -7.4105 0.0 0 +M V30 24 C -2.7227 -8.8903 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 4 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 7 12 +M V30 12 1 8 13 +M V30 13 1 9 14 +M V30 14 1 10 15 +M V30 15 1 12 16 +M V30 16 2 14 17 CFG=2 +M V30 17 1 14 18 +M V30 18 1 17 19 +M V30 19 2 19 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 2 21 23 +M V30 23 2 22 24 +M V30 24 2 9 11 +M V30 25 2 13 16 +M V30 26 1 15 18 +M V30 27 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +484 + +> +Z46035726 + +> +315.365 + +> +5.538 + +> +1 + +> +50.190 + +> +2 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1996831 + +> +0.9 + +$$$$ +Compound 485 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 0.8917 -1.2009 0.0 0 +M V30 3 C 2.3781 -1.0107 0.0 0 +M V30 4 C 0.2853 -2.5802 0.0 0 +M V30 5 C 3.2699 -2.2116 0.0 0 +M V30 6 C 1.1771 -3.7812 0.0 0 +M V30 7 C 2.6635 -3.5909 0.0 0 +M V30 8 C 3.5553 -4.7919 0.0 0 +M V30 9 N 3.1034 -6.2188 0.0 0 +M V30 10 N 5.0535 -4.7562 0.0 0 +M V30 11 N 4.3282 -7.0749 0.0 0 +M V30 12 C 5.5291 -6.1712 0.0 0 +M V30 13 C 4.4946 -8.5612 0.0 0 +M V30 14 C 6.9084 -6.7538 0.0 0 +M V30 15 O 3.2699 -9.453 0.0 0 +M V30 16 N 5.8739 -9.1439 0.0 0 +M V30 17 C 7.0749 -8.2402 0.0 0 +M V30 18 C 8.264 -6.1118 0.0 0 +M V30 19 S 8.5493 -8.5256 0.0 0 +M V30 20 C 9.2747 -7.2057 0.0 0 +M V30 21 C 8.692 -4.6611 0.0 0 +M V30 22 C 10.7372 -6.8609 0.0 0 +M V30 23 C 10.1546 -4.3163 0.0 0 +M V30 24 C 11.1653 -5.4102 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 2 8 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 2 13 15 +M V30 15 1 13 16 +M V30 16 2 14 17 +M V30 17 1 14 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 1 18 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 6 7 +M V30 25 1 11 12 +M V30 26 1 16 17 +M V30 27 1 19 20 +M V30 28 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +485 + +> +Z55410776 + +> +356.829 + +> +3.109 + +> +1 + +> +59.810 + +> +1 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL2007592 + +> +0.96 + +$$$$ +Compound 486 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4488 1.441 0.0 0 +M V30 3 C 1.8662 1.9135 0.0 0 +M V30 4 C -0.4488 2.6695 0.0 0 +M V30 5 C 1.8544 3.4254 0.0 0 +M V30 6 C 3.1538 1.1693 0.0 0 +M V30 7 O 0.4252 3.8979 0.0 0 +M V30 8 C -1.9607 2.6813 0.0 0 +M V30 9 C 3.1419 4.1932 0.0 0 +M V30 10 C 4.4413 1.9371 0.0 0 +M V30 11 C -2.7167 1.3938 0.0 0 +M V30 12 C 4.4294 3.4609 0.0 0 +M V30 13 C -4.2286 1.4056 0.0 0 +M V30 14 C -1.9844 0.1063 0.0 0 +M V30 15 O 5.717 4.2286 0.0 0 +M V30 16 C -4.9964 0.1181 0.0 0 +M V30 17 C -2.7403 -1.1811 0.0 0 +M V30 18 O -6.5084 0.1299 0.0 0 +M V30 19 C -4.2523 -1.1693 0.0 0 +M V30 20 O -2.008 -2.4687 0.0 0 +M V30 21 C -7.2761 -1.1575 0.0 0 +M V30 22 O -5.02 -2.4568 0.0 0 +M V30 23 C -2.764 -3.7562 0.0 0 +M V30 24 C -4.2759 -3.7443 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 4 8 CFG=2 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 12 15 +M V30 15 1 13 16 +M V30 16 2 14 17 +M V30 17 1 16 18 +M V30 18 2 16 19 +M V30 19 1 17 20 +M V30 20 1 18 21 +M V30 21 1 19 22 +M V30 22 1 20 23 +M V30 23 1 22 24 +M V30 24 1 5 7 +M V30 25 1 10 12 +M V30 26 1 17 19 +M V30 END BOND +M V30 END CTAB +M END +> +486 + +> +Z119968738 + +> +328.316 + +> +3.077 + +> +1 + +> +74.220 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3290471 + +> +0.87 + +$$$$ +Compound 487 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4486 1.4404 0.0 0 +M V30 3 C 1.8655 1.9127 0.0 0 +M V30 4 C -0.4486 2.6684 0.0 0 +M V30 5 C 1.8537 3.424 0.0 0 +M V30 6 C 3.1525 1.1689 0.0 0 +M V30 7 O 0.425 3.8963 0.0 0 +M V30 8 C -1.9599 2.6802 0.0 0 +M V30 9 C 3.1407 4.1915 0.0 0 +M V30 10 C 4.4394 1.9363 0.0 0 +M V30 11 C -2.7156 1.3932 0.0 0 +M V30 12 C 4.4276 3.4594 0.0 0 +M V30 13 C -4.2269 1.405 0.0 0 +M V30 14 C -1.9836 0.1062 0.0 0 +M V30 15 O 5.7146 4.2269 0.0 0 +M V30 16 O -4.9944 2.7156 0.0 0 +M V30 17 C -4.9944 0.118 0.0 0 +M V30 18 C -2.7392 -1.1807 0.0 0 +M V30 19 C -4.2505 4.0262 0.0 0 +M V30 20 C -4.2505 -1.1689 0.0 0 +M V30 21 O -2.0072 -2.4676 0.0 0 +M V30 22 O -5.018 -2.4558 0.0 0 +M V30 23 C -2.7628 -3.7546 0.0 0 +M V30 24 C -4.2741 -3.7428 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 4 8 CFG=2 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 12 15 +M V30 15 1 13 16 +M V30 16 1 13 17 +M V30 17 2 14 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 18 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 5 7 +M V30 25 1 10 12 +M V30 26 1 18 20 +M V30 END BOND +M V30 END CTAB +M END +> +487 + +> +Z119968798 + +> +328.316 + +> +3.427 + +> +1 + +> +74.220 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3290471 + +> +0.86 + +$$$$ +Compound 488 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O -1.5109 0.0118 0.0 0 +M V30 3 O 1.4873 0.0118 0.0 0 +M V30 4 N -0.0118 -1.4873 0.0 0 +M V30 5 C -0.0118 1.5109 0.0 0 +M V30 6 C 1.2748 -2.2191 0.0 0 +M V30 7 C 1.2748 2.2664 0.0 0 +M V30 8 C -1.322 2.2664 0.0 0 +M V30 9 C 1.263 -3.7065 0.0 0 +M V30 10 C 2.5615 -1.4637 0.0 0 +M V30 11 C 1.263 3.7773 0.0 0 +M V30 12 C -1.3338 3.7773 0.0 0 +M V30 13 O -0.0472 -4.4383 0.0 0 +M V30 14 C 2.5497 -4.4383 0.0 0 +M V30 15 C 3.8481 -2.1955 0.0 0 +M V30 16 C -0.0472 4.5328 0.0 0 +M V30 17 C -1.3574 -3.6829 0.0 0 +M V30 18 C 3.8363 -3.6829 0.0 0 +M V30 19 F -0.059 6.0437 0.0 0 +M V30 20 C -2.6677 -4.4147 0.0 0 +M V30 21 C -1.3692 -2.1719 0.0 0 +M V30 22 C -3.978 -3.6592 0.0 0 +M V30 23 C -2.6795 -1.4165 0.0 0 +M V30 24 C -3.9898 -2.1483 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 9 13 +M V30 13 1 9 14 +M V30 14 2 10 15 +M V30 15 2 11 16 +M V30 16 1 13 17 +M V30 17 2 14 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 17 21 +M V30 21 1 20 22 +M V30 22 2 21 23 +M V30 23 2 22 24 +M V30 24 1 12 16 +M V30 25 1 15 18 +M V30 26 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +488 + +> +Z45708389 + +> +343.372 + +> +5.123 + +> +1 + +> +55.400 + +> +4 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1968025 + +> +0.87 + +$$$$ +Compound 489 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2931 -0.7355 0.0 0 +M V30 3 N 2.5863 0.0237 0.0 0 +M V30 4 C 1.2813 -2.2304 0.0 0 +M V30 5 C 2.5744 1.5423 0.0 0 +M V30 6 C -0.0355 -2.9778 0.0 0 +M V30 7 C 2.5744 -2.9778 0.0 0 +M V30 8 C -0.0474 -4.4727 0.0 0 +M V30 9 C 2.5626 -4.4727 0.0 0 +M V30 10 N -1.3643 -5.2082 0.0 0 +M V30 11 C 1.2457 -5.2082 0.0 0 +M V30 12 C -1.3762 -6.7031 0.0 0 +M V30 13 O -2.6931 -7.4387 0.0 0 +M V30 14 C -0.083 -7.4387 0.0 0 +M V30 15 C -0.0949 -8.9335 0.0 0 +M V30 16 C 1.3643 -8.5895 0.0 0 +M V30 17 C 0.5457 -10.2742 0.0 0 +M V30 18 C -1.5779 -8.5895 0.0 0 +M V30 19 O 2.3727 -9.681 0.0 0 +M V30 20 O 1.7914 -7.1421 0.0 0 +M V30 21 C -0.1186 -11.6148 0.0 0 +M V30 22 C -2.7643 -9.5149 0.0 0 +M V30 23 C -1.6016 -11.9351 0.0 0 +M V30 24 C -2.7761 -11.0097 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 2 8 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 1 15 18 +M V30 18 2 16 19 +M V30 19 1 16 20 +M V30 20 1 17 21 +M V30 21 1 18 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 9 11 +M V30 25 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +489 + +> +Z169864346 + +> +332.394 + +> +2.537 + +> +3 + +> +95.500 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3414766 + +> +0.9 + +$$$$ +Compound 490 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2926 -0.7471 0.0 0 +M V30 3 N 2.5852 0.0118 0.0 0 +M V30 4 C 1.2807 -2.2413 0.0 0 +M V30 5 C 2.5733 1.5298 0.0 0 +M V30 6 C -0.0355 -2.9884 0.0 0 +M V30 7 C 2.5733 -2.9884 0.0 0 +M V30 8 C -0.0474 -4.4826 0.0 0 +M V30 9 C 2.5615 -4.4826 0.0 0 +M V30 10 N -1.3637 -5.2297 0.0 0 +M V30 11 C 1.2451 -5.2297 0.0 0 +M V30 12 C -1.3756 -6.724 0.0 0 +M V30 13 O -0.083 -7.4711 0.0 0 +M V30 14 C -2.6919 -7.4711 0.0 0 +M V30 15 C -2.7038 -8.9653 0.0 0 +M V30 16 C -1.2333 -8.6925 0.0 0 +M V30 17 C -3.2256 -10.3646 0.0 0 +M V30 18 C -4.198 -8.6925 0.0 0 +M V30 19 C -0.2727 -9.831 0.0 0 +M V30 20 C -2.265 -11.5031 0.0 0 +M V30 21 C -5.1823 -9.831 0.0 0 +M V30 22 C -0.7945 -11.2303 0.0 0 +M V30 23 O -4.6842 -11.2303 0.0 0 +M V30 24 O -6.6765 -9.5582 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 2 8 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 1 17 20 +M V30 20 1 18 21 +M V30 21 1 19 22 +M V30 22 2 21 23 +M V30 23 1 21 24 +M V30 24 1 9 11 +M V30 25 1 20 22 +M V30 END BOND +M V30 END CTAB +M END +> +490 + +> +Z90313870 + +> +332.394 + +> +2.442 + +> +3 + +> +95.500 + +> +6 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3414770 + +> +0.86 + +$$$$ +Compound 491 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O 1.2857 0.7549 0.0 0 +M V30 3 O 0.7431 -1.2857 0.0 0 +M V30 4 N -0.7549 1.3093 0.0 0 +M V30 5 C -1.3093 -0.7313 0.0 0 +M V30 6 C -1.3211 -2.2176 0.0 0 +M V30 7 C -2.6186 0.0235 0.0 0 +M V30 8 C -2.6304 -2.9489 0.0 0 +M V30 9 C -3.9279 -0.7077 0.0 0 +M V30 10 C -3.9397 -2.194 0.0 0 +M V30 11 C -2.6422 -4.4352 0.0 0 +M V30 12 N -3.9515 -5.1783 0.0 0 +M V30 13 C -3.9633 -6.6646 0.0 0 +M V30 14 O -2.6776 -7.4077 0.0 0 +M V30 15 C -5.2727 -7.4077 0.0 0 +M V30 16 C -6.582 -6.641 0.0 0 +M V30 17 C -5.2845 -8.894 0.0 0 +M V30 18 C -7.8913 -7.3841 0.0 0 +M V30 19 C -6.5938 -5.1311 0.0 0 +M V30 20 C -6.5938 -9.6253 0.0 0 +M V30 21 N -9.2007 -6.6174 0.0 0 CHG=1 +M V30 22 C -7.9031 -8.8704 0.0 0 +M V30 23 O -10.51 -7.3605 0.0 0 +M V30 24 O -9.2125 -5.1075 0.0 0 CHG=-1 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 2 5 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 2 8 10 +M V30 10 1 8 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 18 21 +M V30 21 2 18 22 +M V30 22 2 21 23 +M V30 23 1 21 24 +M V30 24 1 9 10 +M V30 25 1 20 22 +M V30 END BOND +M V30 END CTAB +M END +> +491 + +> +Z367584692 + +> +349.362 + +> +1.246 + +> +2 + +> +132.400 + +> +5 + +> +ATM + +> + + +> + + +$$$$ +Compound 492 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.2895 -0.7335 0.0 0 +M V30 3 C 1.2777 -2.2242 0.0 0 +M V30 4 C 2.5791 0.0236 0.0 0 +M V30 5 F -0.0354 -2.9695 0.0 0 +M V30 6 C 2.5673 -2.9695 0.0 0 +M V30 7 C 3.8687 -0.7098 0.0 0 +M V30 8 C 3.8569 -2.2005 0.0 0 +M V30 9 C 5.1583 0.0473 0.0 0 CFG=1 +M V30 10 N 5.1464 1.5616 0.0 0 +M V30 11 C 6.4478 -0.6861 0.0 0 +M V30 12 C 6.436 2.3307 0.0 0 +M V30 13 O 7.7256 1.5853 0.0 0 +M V30 14 C 6.4242 3.845 0.0 0 +M V30 15 N 5.1109 4.6022 0.0 0 +M V30 16 C 7.7138 4.6022 0.0 0 +M V30 17 N 5.0991 6.1166 0.0 0 +M V30 18 C 7.7019 6.1166 0.0 0 +M V30 19 C 9.0033 3.8569 0.0 0 +M V30 20 C 6.3887 6.8738 0.0 0 +M V30 21 C 8.9915 6.8738 0.0 0 +M V30 22 C 10.2929 4.614 0.0 0 +M V30 23 O 6.3769 8.3881 0.0 0 +M V30 24 C 10.2811 6.1402 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 1 9 10 CFG=1 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 1 17 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 2 20 23 +M V30 23 2 21 24 +M V30 24 1 7 8 +M V30 25 1 18 20 +M V30 26 1 22 24 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 9) +M V30 END COLLECTION +M V30 END CTAB +M END +> +492 + +> +Z28547774 + +> +329.301 + +> +1.685 + +> +2 + +> +70.560 + +> +3 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 493 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.6282 -1.3631 0.0 0 +M V30 3 C -1.126 1.0193 0.0 0 +M V30 4 C -2.1335 -1.1971 0.0 0 +M V30 5 C 0.1185 -2.6551 0.0 0 CFG=1 +M V30 6 C -2.4417 0.2844 0.0 0 +M V30 7 N -0.64 -3.9471 0.0 0 +M V30 8 C 1.612 -2.6432 0.0 0 +M V30 9 C 0.1066 -5.2391 0.0 0 +M V30 10 C -2.1573 -3.9352 0.0 0 +M V30 11 N 2.3588 -3.9352 0.0 0 +M V30 12 C 3.8523 -3.9234 0.0 0 +M V30 13 O 4.5872 -2.6077 0.0 0 +M V30 14 C 4.5872 -5.2154 0.0 0 +M V30 15 N 6.0807 -5.2036 0.0 0 +M V30 16 C 3.8286 -6.5074 0.0 0 +M V30 17 N 6.8156 -6.4956 0.0 0 +M V30 18 C 4.5635 -7.7994 0.0 0 +M V30 19 C 2.3113 -6.4956 0.0 0 +M V30 20 C 6.057 -7.7876 0.0 0 +M V30 21 C 3.8049 -9.0914 0.0 0 +M V30 22 C 1.5409 -7.7876 0.0 0 +M V30 23 O 6.7919 -9.0796 0.0 0 +M V30 24 C 2.2876 -9.0796 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 5 7 CFG=3 +M V30 7 1 5 8 +M V30 8 1 7 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 1 17 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 2 20 23 +M V30 23 2 21 24 +M V30 24 1 4 6 +M V30 25 1 18 20 +M V30 26 1 22 24 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 5) +M V30 END COLLECTION +M V30 END CTAB +M END +> +493 + +> +Z32203019 + +> +342.415 + +> +0.886 + +> +2 + +> +73.800 + +> +5 + +> +parp2 + +> + + +> + + +$$$$ +Compound 494 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4869 0.0 0 +M V30 3 C -1.3217 -2.2304 0.0 0 +M V30 4 C 1.2745 -2.2304 0.0 0 +M V30 5 C -1.3335 -3.7173 0.0 0 +M V30 6 C 1.2627 -3.7173 0.0 0 +M V30 7 C -0.0472 -4.449 0.0 0 +M V30 8 C -2.4546 -4.7086 0.0 0 +M V30 9 O -0.3658 -5.9005 0.0 0 +M V30 10 C -1.8527 -6.0657 0.0 0 CFG=2 +M V30 11 C -2.6198 -7.352 0.0 0 +M V30 12 O -1.8763 -8.6383 0.0 0 +M V30 13 N -4.1303 -7.3402 0.0 0 +M V30 14 C -4.8974 -8.6265 0.0 0 +M V30 15 C -4.8974 -6.0303 0.0 0 +M V30 16 C -6.4079 -8.6147 0.0 0 +M V30 17 C -6.4079 -6.0185 0.0 0 +M V30 18 C -7.1632 -7.3048 0.0 0 +M V30 19 N -8.0601 -8.5085 0.0 0 +M V30 20 C -8.0601 -6.0775 0.0 0 +M V30 21 C -9.4998 -8.0365 0.0 0 +M V30 22 O -7.6116 -4.6378 0.0 0 +M V30 23 N -9.4998 -6.5259 0.0 0 +M V30 24 O -10.7271 -8.9098 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 1 10 11 CFG=3 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 1 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 1 20 23 +M V30 23 2 21 24 +M V30 24 1 6 7 +M V30 25 1 9 10 +M V30 26 1 17 18 +M V30 27 1 21 23 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 10) +M V30 END COLLECTION +M V30 END CTAB +M END +> +494 + +> +Z225729450 + +> +349.769 + +> +-0.150 + +> +2 + +> +87.740 + +> +1 + +> +ATM + +> + + +> + + +$$$$ +Compound 495 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2878 0.7561 0.0 0 +M V30 3 N 1.276 2.2684 0.0 0 +M V30 4 C 2.5756 0.0118 0.0 0 CFG=1 +M V30 5 C -0.0354 3.0246 0.0 0 +M V30 6 C 2.5638 -1.4768 0.0 0 +M V30 7 C 3.8634 0.7679 0.0 0 +M V30 8 C -1.3468 2.292 0.0 0 +M V30 9 C -0.0472 4.5369 0.0 0 +M V30 10 C 3.8516 -2.2093 0.0 0 +M V30 11 C 1.2523 -2.2093 0.0 0 +M V30 12 N 5.1513 0.0236 0.0 0 +M V30 13 C -2.6583 3.0482 0.0 0 +M V30 14 C -1.3587 5.293 0.0 0 +M V30 15 C 3.8398 -3.698 0.0 0 +M V30 16 C 1.2405 -3.698 0.0 0 +M V30 17 C -3.9698 2.3157 0.0 0 +M V30 18 C -2.6701 4.5605 0.0 0 +M V30 19 C 2.5283 -4.4305 0.0 0 +M V30 20 N -4.1352 0.8388 0.0 0 +M V30 21 N -5.3521 2.9419 0.0 0 +M V30 22 N -5.612 0.5434 0.0 0 +M V30 23 C -6.3682 1.8431 0.0 0 +M V30 24 C -7.8687 2.0085 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 1 4 7 CFG=3 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 1 7 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 10 15 +M V30 15 2 11 16 +M V30 16 1 13 17 +M V30 17 2 13 18 +M V30 18 2 15 19 +M V30 19 2 17 20 +M V30 20 1 17 21 +M V30 21 1 20 22 +M V30 22 2 21 23 +M V30 23 1 23 24 +M V30 24 1 14 18 +M V30 25 1 16 19 +M V30 26 1 22 23 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 4) +M V30 END COLLECTION +M V30 END CTAB +M END +> +495 + +> +Z1033096106 + +> +321.376 + +> +2.048 + +> +3 + +> +96.690 + +> +5 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 496 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.493 0.0 0 +M V30 3 N 1.2797 -2.2276 0.0 0 +M V30 4 C -1.3271 -2.2276 0.0 0 +M V30 5 C 2.5713 -1.4693 0.0 0 +M V30 6 N -1.493 -3.7088 0.0 0 +M V30 7 C -2.7135 -1.5996 0.0 0 +M V30 8 C 3.8629 -2.2039 0.0 0 +M V30 9 N -2.9742 -4.0051 0.0 0 +M V30 10 C -3.7325 -2.7016 0.0 0 +M V30 11 C -3.1874 -0.154 0.0 0 +M V30 12 C 5.1544 -1.4456 0.0 0 +M V30 13 C -5.2137 -2.3817 0.0 0 +M V30 14 C -4.6686 0.1658 0.0 0 +M V30 15 N 6.446 -2.1802 0.0 0 +M V30 16 C -5.6877 -0.9361 0.0 0 +M V30 17 C 6.5882 -3.6614 0.0 0 +M V30 18 C 7.8087 -1.5522 0.0 0 +M V30 19 C 8.0457 -3.9577 0.0 0 +M V30 20 C 5.5692 -4.7634 0.0 0 +M V30 21 N 8.8041 -2.6542 0.0 0 +M V30 22 C 8.496 -5.3796 0.0 0 +M V30 23 C 6.0195 -6.1853 0.0 0 +M V30 24 C 7.4769 -6.4816 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 10 13 +M V30 13 2 11 14 +M V30 14 1 12 15 +M V30 15 2 13 16 +M V30 16 1 15 17 +M V30 17 1 15 18 +M V30 18 2 17 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 1 19 22 +M V30 22 2 20 23 +M V30 23 2 22 24 +M V30 24 1 9 10 +M V30 25 1 14 16 +M V30 26 1 19 21 +M V30 27 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +496 + +> +Z102447710 + +> +319.360 + +> +3.063 + +> +2 + +> +75.600 + +> +5 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 497 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.2264 -0.8677 0.0 0 +M V30 3 C 1.2032 -0.8677 0.0 0 +M V30 4 N -2.7073 -0.5437 0.0 0 +M V30 5 C -0.7751 -2.2792 0.0 0 +M V30 6 C 0.7288 -2.2792 0.0 0 +M V30 7 C 2.661 -0.5437 0.0 0 +M V30 8 C -3.7254 -1.6429 0.0 0 +M V30 9 C -1.7933 -3.3783 0.0 0 +M V30 10 C 1.7239 -3.3783 0.0 0 +M V30 11 C 3.656 -1.6429 0.0 0 +M V30 12 S -5.2064 -1.3189 0.0 0 +M V30 13 N -3.2742 -3.0544 0.0 0 +M V30 14 O -1.342 -4.7899 0.0 0 +M V30 15 C 3.1816 -3.0544 0.0 0 +M V30 16 C -5.6807 0.1156 0.0 0 +M V30 17 C -7.1617 0.4396 0.0 0 +M V30 18 O -8.1798 -0.6594 0.0 0 +M V30 19 N -7.636 1.8743 0.0 0 +M V30 20 C -9.117 2.1982 0.0 0 +M V30 21 C -6.641 2.9965 0.0 0 +M V30 22 C -9.5913 3.6329 0.0 0 +M V30 23 C -7.1154 4.4312 0.0 0 +M V30 24 O -8.5963 4.7551 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 10 15 +M V30 15 1 12 16 +M V30 16 1 16 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 5 6 +M V30 25 1 9 13 +M V30 26 1 11 15 +M V30 27 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +497 + +> +Z13685974 + +> +365.470 + +> +1.981 + +> +1 + +> +71.000 + +> +3 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL2440673 + +> +0.94 + +$$$$ +Compound 498 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3106 0.7674 0.0 0 +M V30 3 N -2.6212 0.0236 0.0 0 +M V30 4 C -1.3224 2.2788 0.0 0 +M V30 5 C -2.633 -1.4641 0.0 0 +M V30 6 C -0.0354 3.0344 0.0 0 +M V30 7 C -2.633 3.0344 0.0 0 +M V30 8 C -3.9436 -2.1961 0.0 0 +M V30 9 N 1.2515 2.2906 0.0 0 +M V30 10 C -0.0472 4.5458 0.0 0 +M V30 11 C -2.6448 4.5458 0.0 0 +M V30 12 C -3.9554 -3.6838 0.0 0 +M V30 13 C 2.5385 3.0462 0.0 0 +M V30 14 C 1.2397 5.3014 0.0 0 +M V30 15 C -1.3578 5.3014 0.0 0 +M V30 16 C -5.1834 -4.5576 0.0 0 +M V30 17 C -2.7511 -4.5576 0.0 0 +M V30 18 C 2.5267 4.5576 0.0 0 +M V30 19 C -4.7347 -5.9745 0.0 0 +M V30 20 C -6.6593 -4.2388 0.0 0 +M V30 21 N -3.2233 -5.9745 0.0 0 +M V30 22 C -5.7501 -7.0725 0.0 0 +M V30 23 C -7.6747 -5.3369 0.0 0 +M V30 24 C -7.226 -6.7537 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 10 15 +M V30 15 1 12 16 +M V30 16 2 12 17 +M V30 17 2 13 18 +M V30 18 2 16 19 +M V30 19 1 16 20 +M V30 20 1 17 21 +M V30 21 1 19 22 +M V30 22 2 20 23 +M V30 23 2 22 24 +M V30 24 2 11 15 +M V30 25 1 14 18 +M V30 26 1 19 21 +M V30 27 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +498 + +> +Z62694752 + +> +315.368 + +> +3.393 + +> +2 + +> +57.780 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL559994 + +> +0.87 + +$$$$ +Compound 499 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3104 0.7673 0.0 0 +M V30 3 N -2.6208 0.0236 0.0 0 +M V30 4 C -1.3222 2.2784 0.0 0 +M V30 5 C -2.6326 -1.4638 0.0 0 +M V30 6 C -0.0354 3.034 0.0 0 +M V30 7 C -2.6326 3.034 0.0 0 +M V30 8 C -3.943 -2.1958 0.0 0 +M V30 9 C -0.0472 4.5451 0.0 0 +M V30 10 C 1.2513 2.2902 0.0 0 +M V30 11 C -2.6444 4.5451 0.0 0 +M V30 12 C -3.9548 -3.6833 0.0 0 +M V30 13 N 1.2395 5.3007 0.0 0 +M V30 14 C -1.3576 5.3007 0.0 0 +M V30 15 C 2.5382 3.0458 0.0 0 +M V30 16 C -5.1826 -4.5569 0.0 0 +M V30 17 C -2.7507 -4.5569 0.0 0 +M V30 18 C 2.5263 4.5687 0.0 0 +M V30 19 C -4.734 -5.9736 0.0 0 +M V30 20 C -6.6583 -4.2382 0.0 0 +M V30 21 N -3.2229 -5.9736 0.0 0 +M V30 22 C -5.7493 -7.0715 0.0 0 +M V30 23 C -7.6736 -5.3361 0.0 0 +M V30 24 C -7.225 -6.7527 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 9 14 +M V30 14 1 10 15 +M V30 15 1 12 16 +M V30 16 2 12 17 +M V30 17 1 13 18 +M V30 18 2 16 19 +M V30 19 1 16 20 +M V30 20 1 17 21 +M V30 21 1 19 22 +M V30 22 2 20 23 +M V30 23 2 22 24 +M V30 24 2 11 14 +M V30 25 2 15 18 +M V30 26 1 19 21 +M V30 27 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +499 + +> +Z228827278 + +> +315.368 + +> +3.393 + +> +2 + +> +57.780 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL343641 + +> +0.93 + +$$$$ +Compound 500 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2864 0.7553 0.0 0 +M V30 3 O 2.5728 0.0236 0.0 0 +M V30 4 C 1.2746 2.266 0.0 0 +M V30 5 C 2.561 3.0331 0.0 0 +M V30 6 C -0.0354 3.0331 0.0 0 +M V30 7 C 2.5492 4.5438 0.0 0 +M V30 8 C 3.8475 2.2896 0.0 0 +M V30 9 C -0.0472 4.5438 0.0 0 +M V30 10 C -1.3454 2.2896 0.0 0 +M V30 11 N 1.2392 5.2991 0.0 0 +M V30 12 C 3.8357 5.2991 0.0 0 +M V30 13 C 5.1339 3.0567 0.0 0 +M V30 14 C -1.3572 5.2991 0.0 0 +M V30 15 C -2.6554 3.0567 0.0 0 CFG=2 +M V30 16 C 5.1221 4.5556 0.0 0 +M V30 17 C -1.369 6.8098 0.0 0 +M V30 18 C -2.6672 4.5556 0.0 0 +M V30 19 C -3.9655 2.3132 0.0 0 +M V30 20 C -0.0826 7.5652 0.0 0 +M V30 21 C -0.0944 9.0758 0.0 0 +M V30 22 C 1.2038 6.8334 0.0 0 +M V30 23 C 1.192 9.8312 0.0 0 +M V30 24 C 2.4902 7.5888 0.0 0 +M V30 25 C 2.4784 9.0994 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 4 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 7 12 +M V30 12 1 8 13 +M V30 13 1 9 14 +M V30 14 1 10 15 +M V30 15 1 12 16 +M V30 16 2 14 17 CFG=2 +M V30 17 1 14 18 +M V30 18 1 15 19 CFG=1 +M V30 19 1 17 20 +M V30 20 2 20 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 2 22 24 +M V30 24 2 23 25 +M V30 25 2 9 11 +M V30 26 2 13 16 +M V30 27 1 15 18 +M V30 28 1 24 25 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 15) +M V30 END COLLECTION +M V30 END CTAB +M END +> +500 + +> +Z46107792 + +> +329.392 + +> +6.057 + +> +1 + +> +50.190 + +> +2 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1996831 + +> +0.88 + +$$$$ +Compound 501 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2847 -0.7425 0.0 0 +M V30 3 O 2.5695 0.0117 0.0 0 +M V30 4 C 1.2729 -2.2277 0.0 0 +M V30 5 C -0.0353 -2.9702 0.0 0 +M V30 6 C 2.5577 -2.9702 0.0 0 +M V30 7 N -1.3437 -2.2041 0.0 0 +M V30 8 C -0.0471 -4.4554 0.0 0 +M V30 9 C 2.5459 -4.4554 0.0 0 +M V30 10 C -2.652 -2.9467 0.0 0 +M V30 11 C 1.2376 -5.1862 0.0 0 +M V30 12 O -2.6638 -4.4318 0.0 0 +M V30 13 C -3.9603 -2.1805 0.0 0 +M V30 14 C -5.2687 -2.9231 0.0 0 +M V30 15 C -3.9721 -0.6718 0.0 0 +M V30 16 C -6.577 -2.157 0.0 0 +M V30 17 C -5.2805 0.0825 0.0 0 +M V30 18 C -6.5888 -0.6482 0.0 0 +M V30 19 O -7.8972 0.106 0.0 0 +M V30 20 C -7.909 1.6148 0.0 0 +M V30 21 C -9.2173 2.3691 0.0 0 +M V30 22 C -6.6242 2.3691 0.0 0 +M V30 23 C -9.2291 3.8778 0.0 0 +M V30 24 C -6.636 3.8778 0.0 0 +M V30 25 C -7.9443 4.6322 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 2 10 12 +M V30 12 1 10 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 2 16 18 +M V30 18 1 18 19 +M V30 19 1 19 20 +M V30 20 2 20 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 2 22 24 +M V30 24 2 23 25 +M V30 25 1 9 11 +M V30 26 1 17 18 +M V30 27 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +501 + +> +Z85878902 + +> +333.337 + +> +5.415 + +> +2 + +> +75.630 + +> +5 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1997495 + +> +0.87 + +$$$$ +Compound 502 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2935 -0.7357 0.0 0 +M V30 3 N 2.5871 0.0237 0.0 0 +M V30 4 C 1.2817 -2.2311 0.0 0 +M V30 5 C 2.5752 1.5428 0.0 0 +M V30 6 C 3.8807 -0.712 0.0 0 +M V30 7 C -0.0356 -2.9787 0.0 0 +M V30 8 C 2.5752 -2.9787 0.0 0 +M V30 9 C -0.0474 -4.4741 0.0 0 +M V30 10 C 2.5634 -4.4741 0.0 0 +M V30 11 N -1.3647 -5.2099 0.0 0 +M V30 12 C 1.2461 -5.2099 0.0 0 +M V30 13 C -1.3766 -6.7052 0.0 0 +M V30 14 O -2.6939 -7.441 0.0 0 +M V30 15 C -0.083 -7.441 0.0 0 +M V30 16 C -0.0949 -8.9363 0.0 0 +M V30 17 C 1.3647 -8.5922 0.0 0 +M V30 18 C 0.5459 -10.2774 0.0 0 +M V30 19 C -1.5784 -8.5922 0.0 0 +M V30 20 O 2.3735 -9.684 0.0 0 +M V30 21 O 1.792 -7.1443 0.0 0 +M V30 22 C -0.1186 -11.6185 0.0 0 +M V30 23 C -2.7651 -9.5179 0.0 0 +M V30 24 C -1.6021 -11.9389 0.0 0 +M V30 25 C -2.777 -11.0132 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 1 7 9 +M V30 9 2 8 10 +M V30 10 1 9 11 +M V30 11 2 9 12 +M V30 12 1 11 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 1 16 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 17 21 +M V30 21 1 18 22 +M V30 22 1 19 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 1 10 12 +M V30 26 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +502 + +> +Z240928614 + +> +346.421 + +> +2.458 + +> +2 + +> +86.710 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3414766 + +> +0.88 + +$$$$ +Compound 503 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2931 -0.7355 0.0 0 +M V30 3 N 2.5863 0.0237 0.0 0 +M V30 4 C 1.2813 -2.2304 0.0 0 +M V30 5 C 2.5745 1.5423 0.0 0 +M V30 6 C -0.0355 -2.9778 0.0 0 +M V30 7 C 2.5745 -2.9778 0.0 0 +M V30 8 C 3.8677 2.3016 0.0 0 +M V30 9 C -0.0474 -4.4727 0.0 0 +M V30 10 C 2.5626 -4.4727 0.0 0 +M V30 11 N -1.3643 -5.2083 0.0 0 +M V30 12 C 1.2457 -5.2083 0.0 0 +M V30 13 C -1.3762 -6.7032 0.0 0 +M V30 14 O -2.6931 -7.4387 0.0 0 +M V30 15 C -0.083 -7.4387 0.0 0 +M V30 16 C -0.0949 -8.9336 0.0 0 +M V30 17 C 1.3643 -8.5896 0.0 0 +M V30 18 C 0.5457 -10.2743 0.0 0 +M V30 19 C -1.5779 -8.5896 0.0 0 +M V30 20 O 2.3728 -9.6811 0.0 0 +M V30 21 O 1.7914 -7.1421 0.0 0 +M V30 22 C -0.1186 -11.6149 0.0 0 +M V30 23 C -2.7643 -9.515 0.0 0 +M V30 24 C -1.6016 -11.9352 0.0 0 +M V30 25 C -2.7762 -11.0098 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 9 11 +M V30 11 2 9 12 +M V30 12 1 11 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 1 16 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 17 21 +M V30 21 1 18 22 +M V30 22 1 19 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 1 10 12 +M V30 26 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +503 + +> +Z169894146 + +> +346.421 + +> +3.066 + +> +3 + +> +95.500 + +> +6 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3414766 + +> +0.88 + +$$$$ +Compound 504 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2853 -0.7429 0.0 0 +M V30 3 O 2.5707 0.0117 0.0 0 +M V30 4 C 1.2736 -2.2288 0.0 0 +M V30 5 C 3.8561 -0.7311 0.0 0 +M V30 6 C -0.0353 -2.9717 0.0 0 +M V30 7 C 2.559 -2.9717 0.0 0 +M V30 8 N -1.3443 -2.2052 0.0 0 +M V30 9 C -0.0471 -4.4576 0.0 0 +M V30 10 C 2.5472 -4.4576 0.0 0 +M V30 11 C -2.6533 -2.9481 0.0 0 +M V30 12 C 1.2382 -5.1887 0.0 0 +M V30 13 O -2.6651 -4.434 0.0 0 +M V30 14 C -3.9623 -2.1816 0.0 0 +M V30 15 C -5.2713 -2.9245 0.0 0 +M V30 16 C -3.9741 -0.6721 0.0 0 +M V30 17 C -6.5802 -2.158 0.0 0 +M V30 18 C -5.2831 0.0825 0.0 0 +M V30 19 O -7.8892 -2.9009 0.0 0 +M V30 20 C -6.592 -0.6485 0.0 0 +M V30 21 O -5.2948 1.592 0.0 0 +M V30 22 C -9.1982 -2.1344 0.0 0 +M V30 23 O -7.901 0.1061 0.0 0 +M V30 24 C -4.0095 2.3467 0.0 0 +M V30 25 C -9.21 -0.6368 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 6 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 17 19 +M V30 19 2 17 20 +M V30 20 1 18 21 +M V30 21 1 19 22 +M V30 22 1 20 23 +M V30 23 1 21 24 +M V30 24 1 23 25 +M V30 25 1 10 12 +M V30 26 1 18 20 +M V30 END BOND +M V30 END CTAB +M END +> +504 + +> +Z27656096 + +> +345.347 + +> +2.456 + +> +1 + +> +83.090 + +> +7 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1997495 + +> +0.92 + +$$$$ +Compound 505 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 0.734 -1.2905 0.0 0 +M V30 3 C -1.48 -0.296 0.0 0 +M V30 4 N -0.2841 -2.3917 0.0 0 +M V30 5 N 2.2141 -1.4326 0.0 0 +M V30 6 C -1.6458 -1.776 0.0 0 +M V30 7 C 3.0903 -0.2012 0.0 0 +M V30 8 C -2.96 -2.5101 0.0 0 +M V30 9 O 2.4627 1.184 0.0 0 +M V30 10 C 4.5703 -0.3433 0.0 0 +M V30 11 C -4.2743 -1.7523 0.0 0 +M V30 12 N 5.1742 -1.705 0.0 0 +M V30 13 C 5.4465 0.888 0.0 0 +M V30 14 O -4.2861 -0.2368 0.0 0 +M V30 15 O -5.5886 -2.4864 0.0 0 +M V30 16 C 6.6542 -1.847 0.0 0 +M V30 17 C 6.9265 0.7459 0.0 0 +M V30 18 C -6.9028 -1.7286 0.0 0 +M V30 19 O 7.2581 -3.2087 0.0 0 +M V30 20 C 7.5304 -0.6156 0.0 0 +M V30 21 C 7.8027 1.9773 0.0 0 +M V30 22 C -8.2171 -2.4627 0.0 0 +M V30 23 C 9.0104 -0.7577 0.0 0 +M V30 24 C 9.2827 1.8352 0.0 0 +M V30 25 C 9.8866 0.4736 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 1 10 12 +M V30 12 2 10 13 +M V30 13 2 11 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 1 13 17 +M V30 17 1 15 18 +M V30 18 2 16 19 +M V30 19 1 16 20 +M V30 20 1 17 21 +M V30 21 1 18 22 +M V30 22 1 20 23 +M V30 23 2 21 24 +M V30 24 2 23 25 +M V30 25 1 4 6 +M V30 26 2 17 20 +M V30 27 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +505 + +> +Z225998254 + +> +357.384 + +> +1.072 + +> +2 + +> +97.390 + +> +6 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 506 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5047 0.0 0 +M V30 3 N -1.3166 2.2689 0.0 0 +M V30 4 C 1.2696 2.2689 0.0 0 +M V30 5 C -1.3284 3.7737 0.0 0 +M V30 6 C 1.2579 3.7737 0.0 0 +M V30 7 C 2.551 1.5283 0.0 0 +M V30 8 C -2.6333 4.5261 0.0 0 +M V30 9 C -0.047 4.5261 0.0 0 +M V30 10 C 2.5393 4.5261 0.0 0 +M V30 11 C 3.8325 2.2924 0.0 0 +M V30 12 O -2.6451 6.0309 0.0 0 +M V30 13 N -3.9383 3.7972 0.0 0 +M V30 14 C 3.8207 3.7972 0.0 0 +M V30 15 C -5.2432 4.5496 0.0 0 +M V30 16 C -6.5481 3.8207 0.0 0 +M V30 17 C -7.8531 4.5731 0.0 0 +M V30 18 N -9.2285 3.9735 0.0 0 +M V30 19 N -8.0177 6.0661 0.0 0 +M V30 20 C -10.2396 5.0904 0.0 0 +M V30 21 C -9.4872 6.3835 0.0 0 +M V30 22 C -11.7444 5.1021 0.0 0 +M V30 23 C -10.2396 7.6885 0.0 0 +M V30 24 C -12.5085 6.4071 0.0 0 +M V30 25 C -11.7444 7.7002 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 2 22 24 +M V30 24 2 23 25 +M V30 25 1 6 9 +M V30 26 1 11 14 +M V30 27 2 20 21 +M V30 28 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +506 + +> +Z167201228 + +> +332.356 + +> +1.405 + +> +3 + +> +86.880 + +> +4 + +> +parp15, Tankyrase1, parp1 + +> + + +> + + +$$$$ +Compound 507 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3149 0.77 0.0 0 +M V30 3 C 1.2912 0.77 0.0 0 +M V30 4 N -1.4808 2.2745 0.0 0 +M V30 5 N -2.701 0.1658 0.0 0 +M V30 6 C 2.5825 0.0236 0.0 0 +M V30 7 N -2.9616 2.5944 0.0 0 +M V30 8 N -3.7198 1.2912 0.0 0 +M V30 9 C -3.0209 -1.2912 0.0 0 +M V30 10 N 3.8738 0.7937 0.0 0 +M V30 11 N 2.5707 -1.4689 0.0 0 +M V30 12 C -4.4662 -1.7414 0.0 0 +M V30 13 C -1.9191 -2.2864 0.0 0 +M V30 14 C 5.1651 0.0473 0.0 0 +M V30 15 C 3.862 -2.2153 0.0 0 +M V30 16 C -4.786 -3.1986 0.0 0 +M V30 17 C -2.239 -3.7435 0.0 0 +M V30 18 C 5.1533 -1.4452 0.0 0 +M V30 19 C 6.4564 0.8174 0.0 0 +M V30 20 O 3.8501 -3.708 0.0 0 +M V30 21 C -3.6843 -4.1937 0.0 0 +M V30 22 C 6.4446 -2.1916 0.0 0 +M V30 23 C 7.7477 0.071 0.0 0 +M V30 24 O -4.0041 -5.6508 0.0 0 +M V30 25 C 7.7359 -1.4216 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 2 13 17 +M V30 17 2 14 18 +M V30 18 1 14 19 +M V30 19 2 15 20 +M V30 20 2 16 21 +M V30 21 1 18 22 +M V30 22 2 19 23 +M V30 23 1 21 24 +M V30 24 2 22 25 +M V30 25 2 7 8 +M V30 26 1 15 18 +M V30 27 1 17 21 +M V30 28 1 23 25 +M V30 END BOND +M V30 END CTAB +M END +> +507 + +> +Z25096582 + +> +352.370 + +> +1.586 + +> +2 + +> +105.290 + +> +4 + +> +parp1, parp2 + +> + + +> + + +$$$$ +Compound 508 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3046 0.7639 0.0 0 +M V30 3 N -1.3164 2.2684 0.0 0 +M V30 4 C -2.6093 0.0235 0.0 0 +M V30 5 N -2.621 3.0207 0.0 0 +M V30 6 C -3.914 0.7875 0.0 0 +M V30 7 C -2.621 -1.4574 0.0 0 +M V30 8 C -3.9257 2.2802 0.0 0 +M V30 9 C -5.2186 0.047 0.0 0 +M V30 10 C -3.9257 -2.1862 0.0 0 +M V30 11 C -5.2304 3.0324 0.0 0 +M V30 12 C -5.2304 -1.4339 0.0 0 +M V30 13 O -6.5351 2.2919 0.0 0 +M V30 14 N -5.2421 4.5369 0.0 0 +M V30 15 C -3.961 5.2892 0.0 0 +M V30 16 C -6.5468 5.2892 0.0 0 +M V30 17 C -3.9727 6.7936 0.0 0 +M V30 18 C -7.8515 4.5604 0.0 0 +M V30 19 C -5.2774 7.5459 0.0 0 +M V30 20 C -2.6916 7.5459 0.0 0 +M V30 21 N -9.1562 5.3127 0.0 0 +M V30 22 C -10.4608 4.5839 0.0 0 +M V30 23 C -9.1679 6.8172 0.0 0 +M V30 24 C -11.7655 5.3362 0.0 0 +M V30 25 C -10.4726 7.5694 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 17 20 +M V30 20 1 18 21 +M V30 21 1 21 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 1 6 8 +M V30 26 1 10 12 +M V30 END BOND +M V30 END CTAB +M END +> +508 + +> +Z608784116 + +> +344.451 + +> +1.928 + +> +1 + +> +65.010 + +> +8 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3105422 + +> +0.85 + +$$$$ +Compound 509 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.2952 0.7605 0.0 0 +M V30 3 C 2.5905 0.0237 0.0 0 +M V30 4 C 1.2834 2.2816 0.0 0 +M V30 5 C 3.8858 0.7843 0.0 0 +M V30 6 C 2.5787 3.0421 0.0 0 +M V30 7 C 3.8739 2.3053 0.0 0 +M V30 8 O 5.1692 3.0659 0.0 0 +M V30 9 C 6.4645 2.3291 0.0 0 +M V30 10 C 7.7598 3.0896 0.0 0 +M V30 11 N 9.0551 2.3529 0.0 0 +M V30 12 C 10.3504 3.1134 0.0 0 +M V30 13 C 9.0432 0.8556 0.0 0 +M V30 14 O 10.3385 4.6345 0.0 0 +M V30 15 C 11.6457 2.3766 0.0 0 +M V30 16 N 12.941 3.1372 0.0 0 +M V30 17 C 11.6338 0.8793 0.0 0 +M V30 18 N 14.2363 2.4004 0.0 0 +M V30 19 C 12.9291 0.1426 0.0 0 +M V30 20 C 10.3148 0.1426 0.0 0 +M V30 21 C 14.2244 0.9031 0.0 0 +M V30 22 C 12.9172 -1.3547 0.0 0 +M V30 23 C 10.3029 -1.3547 0.0 0 +M V30 24 O 15.5197 0.1663 0.0 0 +M V30 25 C 11.5982 -2.0914 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 1 9 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 1 11 13 +M V30 13 2 12 14 +M V30 14 1 12 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 17 19 +M V30 19 1 17 20 +M V30 20 1 18 21 +M V30 21 1 19 22 +M V30 22 2 20 23 +M V30 23 2 21 24 +M V30 24 2 22 25 +M V30 25 1 6 7 +M V30 26 1 19 21 +M V30 27 1 23 25 +M V30 END BOND +M V30 END CTAB +M END +> +509 + +> +Z119834214 + +> +341.336 + +> +1.281 + +> +1 + +> +71.000 + +> +5 + +> +parp10 + +> + + +> + + +$$$$ +Compound 510 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.0146 1.1208 0.0 0 +M V30 3 N -2.4893 0.8258 0.0 0 +M V30 4 C -0.5663 2.5601 0.0 0 +M V30 5 C -2.9613 -0.5899 0.0 0 +M V30 6 C 0.8494 3.032 0.0 0 +M V30 7 C -1.4629 3.7871 0.0 0 +M V30 8 C -4.436 -0.8848 0.0 0 +M V30 9 C 0.8376 4.5422 0.0 0 +M V30 10 C 2.1354 2.2888 0.0 0 +M V30 11 N -0.5899 5.0141 0.0 0 +M V30 12 C -5.4506 0.2359 0.0 0 +M V30 13 C -4.9079 -2.3006 0.0 0 +M V30 14 C 2.1236 5.3091 0.0 0 +M V30 15 C 3.4214 3.0556 0.0 0 +M V30 16 C -6.9254 -0.0589 0.0 0 +M V30 17 C -6.3827 -2.5955 0.0 0 +M V30 18 C 3.4096 4.5658 0.0 0 +M V30 19 C -7.3973 -1.4747 0.0 0 +M V30 20 C -8.8721 -1.7697 0.0 0 +M V30 21 N -9.344 -3.1854 0.0 0 +M V30 22 C -10.8188 -3.4804 0.0 0 +M V30 23 C -8.353 -4.2826 0.0 0 +M V30 24 C -11.2907 -4.8961 0.0 0 +M V30 25 C -6.9018 -3.9641 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 1 9 14 +M V30 14 2 10 15 +M V30 15 1 12 16 +M V30 16 2 13 17 +M V30 17 2 14 18 +M V30 18 2 16 19 +M V30 19 1 19 20 +M V30 20 1 20 21 +M V30 21 1 21 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 1 9 11 +M V30 26 1 15 18 +M V30 27 1 17 19 +M V30 END BOND +M V30 END CTAB +M END +> +510 + +> +Z109836774 + +> +335.443 + +> +3.967 + +> +2 + +> +48.130 + +> +7 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL356104 + +> +0.87 + +$$$$ +Compound 511 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3065 0.7533 0.0 0 +M V30 3 N -1.3183 2.2599 0.0 0 +M V30 4 C -2.613 0.0235 0.0 0 +M V30 5 N -2.6248 3.0133 0.0 0 +M V30 6 C -3.9196 0.7768 0.0 0 +M V30 7 C -2.6248 -1.4595 0.0 0 +M V30 8 C -3.9314 2.2835 0.0 0 +M V30 9 C -5.2261 0.047 0.0 0 +M V30 10 C -3.9314 -2.2011 0.0 0 +M V30 11 C -5.2379 3.0368 0.0 0 +M V30 12 C -5.2379 -1.436 0.0 0 +M V30 13 O -5.8618 1.6832 0.0 0 +M V30 14 N -5.2497 4.5434 0.0 0 +M V30 15 C -6.5562 5.2968 0.0 0 CFG=2 +M V30 16 C -6.568 6.8034 0.0 0 +M V30 17 C -7.8628 4.567 0.0 0 +M V30 18 C -7.8746 7.5685 0.0 0 +M V30 19 C -5.285 7.5685 0.0 0 +M V30 20 N -7.8746 3.0839 0.0 0 +M V30 21 C -7.8863 9.0752 0.0 0 +M V30 22 C -5.2968 9.0752 0.0 0 +M V30 23 C -6.5915 2.3541 0.0 0 +M V30 24 C -9.1811 2.3541 0.0 0 +M V30 25 C -6.6033 9.8285 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 15 14 CFG=1 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 1 17 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 1 20 23 +M V30 23 1 20 24 +M V30 24 2 21 25 +M V30 25 1 6 8 +M V30 26 1 10 12 +M V30 27 1 22 25 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 15) +M V30 END COLLECTION +M V30 END CTAB +M END +> +511 + +> +Z117592368 + +> +336.388 + +> +1.247 + +> +2 + +> +73.800 + +> +5 + +> +parp2 + +> + + +> + + +$$$$ +Compound 512 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2905 0.7695 0.0 0 +M V30 3 N 2.581 0.0236 0.0 0 +M V30 4 C 1.2787 2.285 0.0 0 CFG=1 +M V30 5 C 3.8716 0.7932 0.0 0 +M V30 6 O 2.5692 3.0546 0.0 0 +M V30 7 C -0.0355 3.0546 0.0 0 +M V30 8 C 3.8597 2.3087 0.0 0 +M V30 9 C 5.1621 0.0473 0.0 0 +M V30 10 C 5.1503 3.0783 0.0 0 +M V30 11 C 6.4527 0.8169 0.0 0 +M V30 12 C 6.4408 2.3324 0.0 0 +M V30 13 N 7.7432 0.071 0.0 0 +M V30 14 C 9.0337 0.8406 0.0 0 +M V30 15 O 9.0219 2.3561 0.0 0 +M V30 16 C 10.3243 0.0947 0.0 0 +M V30 17 C 11.6148 0.8643 0.0 0 +M V30 18 C 12.9764 0.2604 0.0 0 +M V30 19 C 11.7569 2.3679 0.0 0 +M V30 20 C 13.9709 1.3852 0.0 0 +M V30 21 C 13.4263 -1.1603 0.0 0 +M V30 22 N 13.2132 2.6876 0.0 0 +M V30 23 C 15.4272 1.0892 0.0 0 +M V30 24 C 14.8826 -1.4562 0.0 0 +M V30 25 C 15.8772 -0.3315 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 1 4 7 CFG=3 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 2 10 12 +M V30 12 1 11 13 +M V30 13 1 13 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 2 17 19 +M V30 19 2 18 20 +M V30 20 1 18 21 +M V30 21 1 19 22 +M V30 22 1 20 23 +M V30 23 2 21 24 +M V30 24 2 23 25 +M V30 25 1 6 8 +M V30 26 1 11 12 +M V30 27 1 20 22 +M V30 28 1 24 25 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 4) +M V30 END COLLECTION +M V30 END CTAB +M END +> +512 + +> +Z110096110 + +> +335.357 + +> +1.985 + +> +3 + +> +83.220 + +> +3 + +> +parp3 + +> + + +> + + +$$$$ +Compound 513 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5049 0.0 0 +M V30 3 N -1.3167 2.2573 0.0 0 +M V30 4 C 1.2697 2.2573 0.0 0 +M V30 5 C -1.3285 3.7622 0.0 0 +M V30 6 C 1.258 3.7622 0.0 0 +M V30 7 C 2.5512 1.5166 0.0 0 +M V30 8 C -2.6335 4.5147 0.0 0 +M V30 9 C -0.047 4.5147 0.0 0 +M V30 10 C 2.5395 4.5147 0.0 0 +M V30 11 C 3.8328 2.2691 0.0 0 +M V30 12 O -2.6453 6.0196 0.0 0 +M V30 13 N -3.9386 3.7857 0.0 0 +M V30 14 C 3.821 3.7857 0.0 0 +M V30 15 C -5.2436 4.5382 0.0 0 +M V30 16 C -5.2554 6.0431 0.0 0 +M V30 17 C -6.5487 3.8093 0.0 0 +M V30 18 C -6.5604 6.7956 0.0 0 +M V30 19 C -7.8537 4.5617 0.0 0 +M V30 20 C -6.5722 8.3005 0.0 0 +M V30 21 C -7.8655 6.0666 0.0 0 +M V30 22 N -5.3729 9.194 0.0 0 +M V30 23 C -7.7949 9.194 0.0 0 +M V30 24 N -5.8432 10.6284 0.0 0 +M V30 25 C -7.3481 10.6284 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 17 19 +M V30 19 1 18 20 +M V30 20 2 18 21 +M V30 21 1 20 22 +M V30 22 2 20 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 1 6 9 +M V30 26 1 11 14 +M V30 27 1 19 21 +M V30 28 2 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +513 + +> +Z395890110 + +> +330.340 + +> +2.156 + +> +3 + +> +86.880 + +> +3 + +> +parp15, Tankyrase1 + +> + + +> + + +$$$$ +Compound 514 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 N 0.7427 1.3086 0.0 0 CHG=1 +M V30 3 O 2.2281 1.3204 0.0 0 CHG=-1 +M V30 4 C -0.0235 2.6172 0.0 0 +M V30 5 C -1.5326 2.629 0.0 0 +M V30 6 C 0.7191 3.9258 0.0 0 +M V30 7 O -2.2989 1.3439 0.0 0 +M V30 8 C -2.2989 3.9376 0.0 0 +M V30 9 C -0.0471 5.2344 0.0 0 +M V30 10 C -1.5562 0.0589 0.0 0 +M V30 11 C -1.5562 5.2462 0.0 0 +M V30 12 C 0.6955 6.5431 0.0 0 +M V30 13 O -0.0707 7.8517 0.0 0 +M V30 14 N 2.181 6.5549 0.0 0 +M V30 15 C 2.9119 7.8635 0.0 0 CFG=2 +M V30 16 C 4.3974 7.8753 0.0 0 +M V30 17 C 2.1574 9.1721 0.0 0 +M V30 18 N 5.2698 9.1014 0.0 0 +M V30 19 N 5.2698 6.6728 0.0 0 +M V30 20 C 6.6845 8.6534 0.0 0 +M V30 21 C 6.6845 7.1443 0.0 0 +M V30 22 C 7.9696 9.4197 0.0 0 +M V30 23 C 7.9696 6.4016 0.0 0 +M V30 24 C 9.2546 8.6887 0.0 0 +M V30 25 C 9.2546 7.1679 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 9 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 15 14 CFG=1 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 2 22 24 +M V30 24 2 23 25 +M V30 25 1 9 11 +M V30 26 2 20 21 +M V30 27 1 24 25 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 15) +M V30 END COLLECTION +M V30 END CTAB +M END +> +514 + +> +Z88504748 + +> +340.333 + +> +2.564 + +> +2 + +> +110.150 + +> +5 + +> +parp14 + +> + + +> + + +$$$$ +Compound 515 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.4941 0.0118 0.0 0 +M V30 3 N 2.2293 1.3281 0.0 0 +M V30 4 C 2.2293 -1.2806 0.0 0 +M V30 5 C 3.7234 1.3399 0.0 0 +M V30 6 C 3.7234 -1.2688 0.0 0 +M V30 7 C 1.4704 -2.5732 0.0 0 +M V30 8 C 4.4586 2.6562 0.0 0 +M V30 9 C 4.4586 0.0474 0.0 0 +M V30 10 C 4.4586 -2.5613 0.0 0 +M V30 11 C 2.2056 -3.8657 0.0 0 +M V30 12 O 3.6997 3.9724 0.0 0 +M V30 13 N 5.9528 2.668 0.0 0 +M V30 14 C 3.6997 -3.8539 0.0 0 +M V30 15 C 6.6998 3.9843 0.0 0 CFG=2 +M V30 16 C 8.194 3.9962 0.0 0 +M V30 17 C 5.929 5.3006 0.0 0 +M V30 18 N 9.0715 5.2294 0.0 0 +M V30 19 N 9.0715 2.7866 0.0 0 +M V30 20 C 10.4944 4.7788 0.0 0 +M V30 21 C 10.4944 3.261 0.0 0 +M V30 22 C 11.787 5.5377 0.0 0 +M V30 23 C 11.787 2.5257 0.0 0 +M V30 24 C 13.0795 4.8025 0.0 0 +M V30 25 C 13.0795 3.2847 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 2 10 14 +M V30 14 1 15 13 CFG=1 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 2 22 24 +M V30 24 2 23 25 +M V30 25 1 6 9 +M V30 26 1 11 14 +M V30 27 2 20 21 +M V30 28 1 24 25 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 15) +M V30 END COLLECTION +M V30 END CTAB +M END +> +515 + +> +Z298373262 + +> +332.356 + +> +1.815 + +> +3 + +> +86.880 + +> +3 + +> +parp15, parp3 + +> + + +> + + +$$$$ +Compound 516 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O -0.7609 -1.2959 0.0 0 +M V30 3 O 0.7371 1.3196 0.0 0 +M V30 4 N -1.3196 0.7609 0.0 0 +M V30 5 C 1.2959 -0.7371 0.0 0 +M V30 6 C 2.5918 0.0237 0.0 0 +M V30 7 C 1.284 -2.2351 0.0 0 +M V30 8 C 3.8877 -0.7133 0.0 0 +M V30 9 C 2.5799 -2.9841 0.0 0 +M V30 10 C 3.8758 -2.2113 0.0 0 +M V30 11 C 5.1717 -2.9603 0.0 0 +M V30 12 C 6.4676 -2.1994 0.0 0 +M V30 13 N 7.7635 -2.9484 0.0 0 +M V30 14 C 9.0594 -2.1875 0.0 0 +M V30 15 O 9.0475 -0.6657 0.0 0 +M V30 16 C 10.3553 -2.9366 0.0 0 +M V30 17 C 10.3434 -4.4346 0.0 0 +M V30 18 C 11.6512 -2.1757 0.0 0 +M V30 19 C 11.6394 -5.1717 0.0 0 +M V30 20 C 12.9471 -2.9247 0.0 0 +M V30 21 N 11.6275 -6.6697 0.0 0 +M V30 22 C 12.9353 -4.4108 0.0 0 +M V30 23 C 12.9234 -7.4068 0.0 0 +M V30 24 N 14.2312 -5.1479 0.0 0 +M V30 25 C 14.2193 -6.6459 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 2 5 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 2 8 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 1 13 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 2 19 21 +M V30 21 1 19 22 +M V30 22 1 21 23 +M V30 23 2 22 24 +M V30 24 2 23 25 +M V30 25 1 9 10 +M V30 26 1 20 22 +M V30 27 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +516 + +> +Z30857069 + +> +356.399 + +> +0.950 + +> +2 + +> +115.040 + +> +5 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 517 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.4762 0.3188 0.0 0 +M V30 3 N -2.4918 -0.7794 0.0 0 +M V30 4 C -1.9485 1.7596 0.0 0 +M V30 5 C -3.968 -0.4605 0.0 0 +M V30 6 C -3.4248 2.0785 0.0 0 +M V30 7 C -0.9565 2.8815 0.0 0 +M V30 8 C -4.9836 -1.5588 0.0 0 +M V30 9 C -4.4404 0.9802 0.0 0 +M V30 10 C -3.8971 3.5192 0.0 0 +M V30 11 C -1.4289 4.3223 0.0 0 +M V30 12 O -6.4598 -1.24 0.0 0 +M V30 13 N -4.5349 -2.976 0.0 0 +M V30 14 C -2.9051 4.6412 0.0 0 +M V30 15 C -3.1177 -3.4248 0.0 0 +M V30 16 C -5.4324 -4.1806 0.0 0 +M V30 17 C -3.1295 -4.9128 0.0 0 +M V30 18 C -1.8305 -2.6571 0.0 0 +M V30 19 C -4.5585 -5.3852 0.0 0 CFG=2 +M V30 20 C -1.8423 -5.6568 0.0 0 +M V30 21 C -0.5432 -3.4011 0.0 0 +M V30 22 C -5.0309 -6.8023 0.0 0 +M V30 23 C -0.555 -4.901 0.0 0 +M V30 24 O -6.5071 -7.0976 0.0 0 +M V30 25 O -4.0389 -7.9006 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 1 13 16 +M V30 16 2 15 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 1 19 22 CFG=3 +M V30 22 2 20 23 +M V30 23 2 22 24 +M V30 24 1 22 25 +M V30 25 1 6 9 +M V30 26 1 11 14 +M V30 27 1 17 19 +M V30 28 1 21 23 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 19) +M V30 END COLLECTION +M V30 END CTAB +M END +> +517 + +> +Z1413700738 + +> +334.325 + +> +1.034 + +> +2 + +> +86.710 + +> +2 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 518 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 O 10.6594 -1.0376 0.0 0 +M V30 3 C 10.6476 0.4716 0.0 0 +M V30 4 N 11.9329 1.2381 0.0 0 +M V30 5 C 9.3388 1.2381 0.0 0 +M V30 6 C 13.2182 0.4952 0.0 0 +M V30 7 C 11.9211 2.7474 0.0 0 +M V30 8 C 9.327 2.7474 0.0 0 +M V30 9 C 8.0299 0.4952 0.0 0 +M V30 10 C 14.5034 1.2616 0.0 0 +M V30 11 C 13.2064 3.5138 0.0 0 +M V30 12 C 8.0181 3.5138 0.0 0 +M V30 13 C 6.7211 1.2616 0.0 0 +M V30 14 C 14.4916 2.7709 0.0 0 +M V30 15 C 15.7887 0.5188 0.0 0 +M V30 16 C 6.7093 2.7709 0.0 0 +M V30 17 N 15.7769 -0.9668 0.0 0 +M V30 18 C 5.4004 3.5374 0.0 0 +M V30 19 C 17.0622 -1.7097 0.0 0 +M V30 20 C 4.0916 2.7945 0.0 0 +M V30 21 O 18.3474 -0.9433 0.0 0 +M V30 22 C 17.0504 -3.1954 0.0 0 +M V30 23 N 15.8241 -4.068 0.0 0 +M V30 24 C 18.2531 -4.068 0.0 0 +M V30 25 C 16.2721 -5.483 0.0 0 +M V30 26 C 17.7814 -5.483 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 2 3 +M V30 2 1 3 4 +M V30 3 1 3 5 +M V30 4 1 4 6 +M V30 5 1 4 7 +M V30 6 2 5 8 +M V30 7 1 5 9 +M V30 8 1 6 10 +M V30 9 1 7 11 +M V30 10 1 8 12 +M V30 11 2 9 13 +M V30 12 1 10 14 +M V30 13 1 10 15 +M V30 14 2 12 16 +M V30 15 1 15 17 +M V30 16 1 16 18 +M V30 17 1 17 19 +M V30 18 1 18 20 +M V30 19 2 19 21 +M V30 20 1 19 22 +M V30 21 1 22 23 +M V30 22 1 22 24 +M V30 23 1 23 25 +M V30 24 1 24 26 +M V30 25 1 11 14 +M V30 26 1 13 16 +M V30 27 1 25 26 +M V30 END BOND +M V30 END CTAB +M END +> +518 + +> +Z1508613390 + +> +343.463 + +> +1.533 + +> +2 + +> +61.440 + +> +5 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 519 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3112 -0.7323 0.0 0 +M V30 3 N -2.6224 0.0236 0.0 0 +M V30 4 C -1.323 -2.2208 0.0 0 +M V30 5 C -2.6342 1.5356 0.0 0 +M V30 6 C -0.0354 -2.9532 0.0 0 +M V30 7 C -2.6342 -2.9532 0.0 0 +M V30 8 C -3.9454 2.2916 0.0 0 +M V30 9 C -0.0472 -4.4416 0.0 0 +M V30 10 C 1.2521 -2.1971 0.0 0 +M V30 11 C -2.646 -4.4416 0.0 0 +M V30 12 C -3.9573 3.8037 0.0 0 +M V30 13 N -1.3584 -5.1858 0.0 0 +M V30 14 C 1.2403 -5.1858 0.0 0 +M V30 15 C 2.5397 -2.9295 0.0 0 +M V30 16 O -3.9573 -5.1858 0.0 0 +M V30 17 C -2.7523 4.7015 0.0 0 +M V30 18 C -5.1858 4.7015 0.0 0 +M V30 19 C 2.5279 -4.4298 0.0 0 +M V30 20 C -3.2249 6.1426 0.0 0 +M V30 21 C -1.2994 4.4061 0.0 0 +M V30 22 N -4.7369 6.1426 0.0 0 +M V30 23 C -2.2326 7.2649 0.0 0 +M V30 24 C -0.3071 5.5284 0.0 0 +M V30 25 C -0.7796 6.9695 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 2 9 14 +M V30 14 1 10 15 +M V30 15 1 11 16 +M V30 16 1 12 17 +M V30 17 2 12 18 +M V30 18 1 14 19 +M V30 19 2 17 20 +M V30 20 1 17 21 +M V30 21 1 18 22 +M V30 22 1 20 23 +M V30 23 2 21 24 +M V30 24 2 23 25 +M V30 25 2 11 13 +M V30 26 2 15 19 +M V30 27 1 20 22 +M V30 28 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +519 + +> +Z26395724 + +> +331.368 + +> +3.918 + +> +3 + +> +78.010 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL343022 + +> +0.87 + +$$$$ +Compound 520 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4912 0.0 0 +M V30 3 N 1.2781 -2.2249 0.0 0 +M V30 4 C -1.3255 -2.2249 0.0 0 +M V30 5 C 2.5682 -1.4675 0.0 0 +M V30 6 C -2.6392 -1.4675 0.0 0 +M V30 7 C -1.3373 -3.7162 0.0 0 +M V30 8 C 3.8582 -2.2013 0.0 0 +M V30 9 C -3.9529 -2.2013 0.0 0 +M V30 10 C -2.651 -4.4618 0.0 0 +M V30 11 C 5.1482 -1.4438 0.0 0 +M V30 12 C -3.9647 -3.6925 0.0 0 +M V30 13 C 6.5092 -2.0474 0.0 0 +M V30 14 C 5.2902 0.0591 0.0 0 +M V30 15 C -5.2784 -4.4381 0.0 0 +M V30 16 C 7.5034 -0.9231 0.0 0 +M V30 17 C 6.959 -3.4676 0.0 0 +M V30 18 N 6.7459 0.3787 0.0 0 +M V30 19 N -6.5921 -3.6688 0.0 0 +M V30 20 C 8.9591 -1.219 0.0 0 +M V30 21 C 8.4147 -3.7635 0.0 0 +M V30 22 C -6.6039 -2.1539 0.0 0 +M V30 23 C 9.4088 -2.6392 0.0 0 +M V30 24 O -5.3139 -1.3965 0.0 0 +M V30 25 C -7.9176 -1.3965 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 11 13 +M V30 13 2 11 14 +M V30 14 1 12 15 +M V30 15 2 13 16 +M V30 16 1 13 17 +M V30 17 1 14 18 +M V30 18 1 15 19 +M V30 19 1 16 20 +M V30 20 2 17 21 +M V30 21 1 19 22 +M V30 22 2 20 23 +M V30 23 2 22 24 +M V30 24 1 22 25 +M V30 25 1 10 12 +M V30 26 1 16 18 +M V30 27 1 21 23 +M V30 END BOND +M V30 END CTAB +M END +> +520 + +> +Z87552893 + +> +335.400 + +> +1.756 + +> +3 + +> +73.990 + +> +6 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL551001 + +> +0.86 + +$$$$ +Compound 521 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.5159 0.0 0 +M V30 3 N 1.2791 2.2739 0.0 0 +M V30 4 N -1.3264 2.2739 0.0 0 +M V30 5 C 1.2672 3.7899 0.0 0 +M V30 6 C 2.57 1.5396 0.0 0 +M V30 7 C -1.3383 3.7899 0.0 0 +M V30 8 C -0.0473 4.5479 0.0 0 +M V30 9 C 2.5582 4.5479 0.0 0 +M V30 10 C 3.861 2.2976 0.0 0 +M V30 11 O -2.6529 4.5479 0.0 0 +M V30 12 C -0.0592 6.0639 0.0 0 +M V30 13 C 2.5463 6.0639 0.0 0 +M V30 14 C 5.152 1.5633 0.0 0 +M V30 15 C 1.2317 6.8219 0.0 0 +M V30 16 O 5.1401 0.071 0.0 0 +M V30 17 N 6.4429 2.3213 0.0 0 +M V30 18 C 7.7339 1.587 0.0 0 +M V30 19 C 9.0248 2.345 0.0 0 +M V30 20 C 7.722 0.0947 0.0 0 +M V30 21 C 10.3158 1.6107 0.0 0 +M V30 22 C 9.013 -0.6395 0.0 0 +M V30 23 C 10.304 0.1184 0.0 0 +M V30 24 C 11.5949 -0.6158 0.0 0 +M V30 25 N 12.8859 -1.3501 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 10 14 +M V30 14 2 12 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 1 17 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 2 21 23 +M V30 23 1 23 24 +M V30 24 3 24 25 +M V30 25 1 7 8 +M V30 26 1 13 15 +M V30 27 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +521 + +> +Z195196156 + +> +334.329 + +> +1.881 + +> +2 + +> +102.300 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3105417 + +> +0.94 + +$$$$ +Compound 522 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5088 0.0 0 +M V30 3 N -1.3202 2.2633 0.0 0 +M V30 4 C 1.2731 2.2633 0.0 0 +M V30 5 C -1.332 3.7722 0.0 0 +M V30 6 C 1.2613 3.7722 0.0 0 +M V30 7 C 2.558 1.5206 0.0 0 +M V30 8 N -0.0471 4.5266 0.0 0 +M V30 9 C -2.6405 4.5266 0.0 0 +M V30 10 C 2.5462 4.5266 0.0 0 +M V30 11 C 3.8429 2.2751 0.0 0 +M V30 12 N -2.6523 6.0355 0.0 0 +M V30 13 C 3.8311 3.7957 0.0 0 +M V30 14 C -3.9608 6.7899 0.0 0 +M V30 15 C -1.3674 6.7899 0.0 0 +M V30 16 C -3.9726 8.2988 0.0 0 +M V30 17 C -1.3792 8.2988 0.0 0 +M V30 18 C -2.6877 9.0533 0.0 0 +M V30 19 C -2.6994 10.5621 0.0 0 +M V30 20 C -1.4145 11.3284 0.0 0 +M V30 21 C -1.4263 12.8373 0.0 0 +M V30 22 C -0.1296 10.5857 0.0 0 +M V30 23 C -0.1414 13.5917 0.0 0 +M V30 24 C 1.1552 11.352 0.0 0 +M V30 25 C 1.1434 12.8608 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 1 12 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 18 19 +M V30 19 1 19 20 +M V30 20 2 20 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 2 22 24 +M V30 24 2 23 25 +M V30 25 1 6 8 +M V30 26 1 11 13 +M V30 27 1 17 18 +M V30 28 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +522 + +> +Z44502425 + +> +333.427 + +> +3.269 + +> +1 + +> +44.700 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL249813 + +> +0.86 + +$$$$ +Compound 523 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2926 0.7589 0.0 0 +M V30 3 C 2.5852 0.0118 0.0 0 +M V30 4 C 1.2807 2.2768 0.0 0 +M V30 5 C 3.8778 0.7708 0.0 0 +M V30 6 C 2.5733 3.0358 0.0 0 +M V30 7 N 5.1704 0.0237 0.0 0 +M V30 8 C 3.8659 2.2887 0.0 0 +M V30 9 C 6.463 0.7826 0.0 0 +M V30 10 C 5.1585 3.0477 0.0 0 +M V30 11 N 6.4512 2.3006 0.0 0 +M V30 12 C 7.7556 0.0355 0.0 0 +M V30 13 O 5.1467 4.5656 0.0 0 +M V30 14 N 7.7438 -1.4586 0.0 0 +M V30 15 C 9.0364 -2.2057 0.0 0 +M V30 16 C 6.4274 -2.2057 0.0 0 +M V30 17 C 9.0245 -3.6999 0.0 0 +M V30 18 C 6.4156 -3.6999 0.0 0 +M V30 19 N 7.7082 -4.4352 0.0 0 +M V30 20 C 7.6963 -5.9294 0.0 0 +M V30 21 C 8.9889 -6.6765 0.0 0 +M V30 22 C 6.38 -6.6765 0.0 0 +M V30 23 C 8.9771 -8.1707 0.0 0 +M V30 24 C 6.3681 -8.1707 0.0 0 +M V30 25 C 7.6608 -8.9059 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 2 20 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 2 22 24 +M V30 24 2 23 25 +M V30 25 1 6 8 +M V30 26 1 10 11 +M V30 27 1 18 19 +M V30 28 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +523 + +> +Z44494586 + +> +354.833 + +> +2.815 + +> +1 + +> +47.940 + +> +3 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL2377259 + +> +0.87 + +$$$$ +Compound 524 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -0.3176 1.4707 0.0 0 +M V30 3 C 0.7765 2.4826 0.0 0 +M V30 4 C -1.7531 1.9414 0.0 0 +M V30 5 C 0.4588 3.9534 0.0 0 +M V30 6 C -2.0708 3.4122 0.0 0 +M V30 7 C -0.9766 4.4241 0.0 0 +M V30 8 C -1.2942 5.8949 0.0 0 +M V30 9 N -0.3059 7.0127 0.0 0 +M V30 10 C -2.6709 6.5185 0.0 0 +M V30 11 N -1.0589 8.3187 0.0 0 +M V30 12 C -3.977 5.7772 0.0 0 +M V30 13 C -2.5297 8.0128 0.0 0 +M V30 14 O -3.9887 4.2946 0.0 0 +M V30 15 N -5.283 6.5302 0.0 0 +M V30 16 C -6.5891 5.789 0.0 0 CFG=2 +M V30 17 C -7.8951 6.542 0.0 0 +M V30 18 C -6.6008 4.3064 0.0 0 +M V30 19 N -9.2012 5.8007 0.0 0 +M V30 20 C -10.5778 6.4243 0.0 0 +M V30 21 C -9.3659 4.3299 0.0 0 +M V30 22 N -11.5897 5.3301 0.0 0 +M V30 23 C -10.8367 4.0358 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 2 8 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 2 10 13 +M V30 13 2 12 14 +M V30 14 1 12 15 +M V30 15 1 16 15 CFG=1 +M V30 16 1 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 2 21 23 +M V30 23 1 6 7 +M V30 24 1 11 13 +M V30 25 1 22 23 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 16) +M V30 END COLLECTION +M V30 END CTAB +M END +> +524 + +> +Z647978072 + +> +313.330 + +> +1.898 + +> +2 + +> +75.600 + +> +5 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 525 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2942 -0.748 0.0 0 +M V30 3 N 2.5884 0.0237 0.0 0 +M V30 4 C 1.2823 -2.2441 0.0 0 +M V30 5 C 3.8827 -0.7242 0.0 0 +M V30 6 C 2.5765 -2.9802 0.0 0 +M V30 7 C -0.0356 -2.9802 0.0 0 +M V30 8 N 3.8708 -2.2203 0.0 0 +M V30 9 C 5.1769 0.0474 0.0 0 +M V30 10 C 2.5647 -4.4763 0.0 0 +M V30 11 C -0.0474 -4.4763 0.0 0 +M V30 12 C 6.4711 -0.7005 0.0 0 +M V30 13 C 1.2467 -5.2244 0.0 0 +M V30 14 C 7.7654 0.0712 0.0 0 +M V30 15 O 7.7535 1.591 0.0 0 +M V30 16 N 9.0596 -0.6768 0.0 0 +M V30 17 C 10.3538 0.0949 0.0 0 +M V30 18 C 11.6481 -0.653 0.0 0 CFG=2 +M V30 19 C 11.7905 -2.1372 0.0 0 +M V30 20 C 13.0135 -0.0237 0.0 0 +M V30 21 O 13.251 -2.4341 0.0 0 +M V30 22 C 14.0109 -1.128 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 1 18 17 CFG=1 +M V30 18 1 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 6 8 +M V30 23 1 11 13 +M V30 24 1 21 22 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 18) +M V30 END COLLECTION +M V30 END CTAB +M END +> +525 + +> +Z384364426 + +> +301.340 + +> +-0.996 + +> +2 + +> +79.790 + +> +5 + +> +parp3 + +> + + +> + + +$$$$ +Compound 526 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.4455 -0.4502 0.0 0 +M V30 3 C 1.1019 -0.9952 0.0 0 +M V30 4 N -1.9195 -1.8721 0.0 0 +M V30 5 N -2.6778 0.4502 0.0 0 +M V30 6 C 2.5237 -0.5213 0.0 0 +M V30 7 N -3.4361 -1.8602 0.0 0 +M V30 8 C -3.91 -0.4265 0.0 0 +M V30 9 C -2.6896 1.9668 0.0 0 +M V30 10 C 3.6257 -1.5166 0.0 0 +M V30 11 C 2.82 0.9597 0.0 0 +M V30 12 O -5.3556 0.0473 0.0 0 +M V30 13 C 5.0475 -1.0426 0.0 0 +M V30 14 C 4.2418 1.4337 0.0 0 +M V30 15 C 6.1495 -2.0379 0.0 0 +M V30 16 C 5.3437 0.4384 0.0 0 +M V30 17 O 5.8295 -3.4953 0.0 0 +M V30 18 N 7.5713 -1.564 0.0 0 +M V30 19 C 8.6733 -2.5593 0.0 0 +M V30 20 C 10.0951 -2.0853 0.0 0 +M V30 21 C 8.3533 -4.0167 0.0 0 +M V30 22 C 11.197 -3.0806 0.0 0 +M V30 23 C 9.4553 -5.012 0.0 0 +M V30 24 C 10.8771 -4.538 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 2 8 12 +M V30 12 1 10 13 +M V30 13 2 11 14 +M V30 14 1 13 15 +M V30 15 2 13 16 +M V30 16 2 15 17 +M V30 17 1 15 18 +M V30 18 1 18 19 +M V30 19 2 19 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 2 21 23 +M V30 23 2 22 24 +M V30 24 1 7 8 +M V30 25 1 14 16 +M V30 26 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +526 + +> +Z729340502 + +> +340.400 + +> +2.417 + +> +2 + +> +73.800 + +> +5 + +> +parp1 + +> + + +> + + +$$$$ +Compound 527 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2884 0.7565 0.0 0 +M V30 3 C 2.5769 0.0236 0.0 0 +M V30 4 C 1.2766 2.2696 0.0 0 +M V30 5 N 3.8654 0.7801 0.0 0 +M V30 6 N 2.5651 -1.4657 0.0 0 +M V30 7 C 2.5651 3.0261 0.0 0 +M V30 8 C 3.8536 2.2932 0.0 0 +M V30 9 C 1.253 -2.2105 0.0 0 +M V30 10 C 2.5533 4.5392 0.0 0 +M V30 11 C 1.2411 -3.6999 0.0 0 +M V30 12 O 3.8418 5.2957 0.0 0 +M V30 13 N 1.2411 5.2957 0.0 0 +M V30 14 C -0.0709 -4.4328 0.0 0 +M V30 15 N -1.383 -3.6763 0.0 0 +M V30 16 C -0.0827 -5.9222 0.0 0 +M V30 17 C -2.6951 -4.4092 0.0 0 +M V30 18 C -1.3948 -6.6551 0.0 0 +M V30 19 C -2.7069 -5.8986 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 1 10 13 +M V30 13 1 11 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 2 17 19 +M V30 19 1 7 8 +M V30 20 1 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +527 + +> +Z728941594 + +> +276.722 + +> +1.689 + +> +2 + +> +80.900 + +> +5 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 528 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.4404 -0.4486 0.0 0 +M V30 3 C 0.8737 -1.2043 0.0 0 +M V30 4 N -2.751 0.3069 0.0 0 +M V30 5 C -1.4522 -1.9363 0.0 0 +M V30 6 C -0.0236 -2.4086 0.0 0 +M V30 7 C 2.3496 -1.346 0.0 0 +M V30 8 C -4.0616 -0.425 0.0 0 +M V30 9 C -2.7628 -2.6684 0.0 0 +M V30 10 C 0.5785 -3.7664 0.0 0 +M V30 11 C 2.9517 -2.7038 0.0 0 +M V30 12 S -5.3722 0.3306 0.0 0 +M V30 13 N -4.0734 -1.9127 0.0 0 +M V30 14 O -2.7746 -4.1561 0.0 0 +M V30 15 C 2.0544 -3.9081 0.0 0 +M V30 16 C -6.6828 -0.4014 0.0 0 +M V30 17 C -7.9934 0.3542 0.0 0 +M V30 18 O -8.0052 1.8655 0.0 0 +M V30 19 N -9.304 -0.3778 0.0 0 +M V30 20 C -10.6146 0.3778 0.0 0 +M V30 21 C -11.9252 -0.3542 0.0 0 +M V30 22 O -13.2358 0.4014 0.0 0 +M V30 23 C -14.5464 -0.3306 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 10 15 +M V30 15 1 12 16 +M V30 16 1 16 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 1 20 21 +M V30 21 1 21 22 +M V30 22 1 22 23 +M V30 23 1 5 6 +M V30 24 1 9 13 +M V30 25 1 11 15 +M V30 END BOND +M V30 END CTAB +M END +> +528 + +> +Z13682394 + +> +353.460 + +> +1.867 + +> +2 + +> +79.790 + +> +6 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL2440673 + +> +0.92 + +$$$$ +Compound 529 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.4724 0.318 0.0 0 +M V30 3 C 0.1413 -1.4724 0.0 0 +M V30 4 N -2.2381 1.6255 0.0 0 +M V30 5 C -2.2381 -0.9659 0.0 0 +M V30 6 C -1.2368 -2.0731 0.0 0 +M V30 7 C 1.3428 -2.3441 0.0 0 +M V30 8 C -3.7458 1.6373 0.0 0 +M V30 9 C -3.7458 -0.9541 0.0 0 +M V30 10 C -1.4017 -3.5456 0.0 0 +M V30 11 C 1.1779 -3.8165 0.0 0 +M V30 12 S -4.5115 2.9448 0.0 0 +M V30 13 N -4.5115 0.3533 0.0 0 +M V30 14 O -4.5115 -2.2381 0.0 0 +M V30 15 C -0.2002 -4.4173 0.0 0 +M V30 16 C -6.0193 2.9566 0.0 0 +M V30 17 C -6.7732 4.2641 0.0 0 +M V30 18 O -6.0428 5.5717 0.0 0 +M V30 19 N -8.2809 4.2759 0.0 0 +M V30 20 C -9.0348 5.5834 0.0 0 +M V30 21 C -9.0348 2.9919 0.0 0 +M V30 22 C -10.5426 5.5952 0.0 0 +M V30 23 C -10.5426 3.0037 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 10 15 +M V30 15 1 12 16 +M V30 16 1 16 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 5 6 +M V30 24 1 9 13 +M V30 25 1 11 15 +M V30 END BOND +M V30 END CTAB +M END +> +529 + +> +Z13682430 + +> +351.487 + +> +2.918 + +> +1 + +> +61.770 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL2440673 + +> +0.93 + +$$$$ +Compound 530 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2832 0.7534 0.0 0 +M V30 3 C 1.2715 2.2604 0.0 0 +M V30 4 C 2.5665 0.0235 0.0 0 +M V30 5 N -0.0353 3.0257 0.0 0 +M V30 6 C 2.5547 3.0257 0.0 0 +M V30 7 C 3.8498 0.777 0.0 0 +M V30 8 C -1.3421 2.284 0.0 0 +M V30 9 C 3.838 2.284 0.0 0 +M V30 10 Cl 5.1331 0.047 0.0 0 +M V30 11 O -1.3539 0.8005 0.0 0 +M V30 12 C -2.6489 3.0492 0.0 0 +M V30 13 O -3.9558 2.3075 0.0 0 +M V30 14 C -5.2626 3.0728 0.0 0 +M V30 15 C -5.2744 4.5797 0.0 0 +M V30 16 C -6.5694 2.3311 0.0 0 +M V30 17 C -6.5812 5.3332 0.0 0 +M V30 18 C -7.8763 3.0963 0.0 0 +M V30 19 C -7.888 4.5915 0.0 0 +M V30 20 C -9.1949 5.345 0.0 0 +M V30 21 O -9.2066 6.852 0.0 0 +M V30 22 N -10.5017 4.6033 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 12 13 +M V30 13 1 13 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 2 17 19 +M V30 19 1 19 20 +M V30 20 2 20 21 +M V30 21 1 20 22 +M V30 22 1 7 9 +M V30 23 1 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +530 + +> +Z18243052 + +> +339.173 + +> +2.376 + +> +2 + +> +81.420 + +> +5 + +> +parp14 + +> + + +> + + +$$$$ +Compound 531 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5065 0.0 0 +M V30 3 N -1.3181 2.2597 0.0 0 +M V30 4 C 1.2711 2.2597 0.0 0 +M V30 5 C -1.3299 3.7662 0.0 0 +M V30 6 C 1.2593 3.7662 0.0 0 +M V30 7 C 2.554 1.53 0.0 0 +M V30 8 N -0.047 4.5313 0.0 0 +M V30 9 C -2.6363 4.5313 0.0 0 +M V30 10 C 2.5422 4.5313 0.0 0 +M V30 11 C 3.8369 2.2833 0.0 0 +M V30 12 C -3.9428 3.7898 0.0 0 +M V30 13 C 3.8251 3.7898 0.0 0 +M V30 14 C -5.2492 4.5548 0.0 0 +M V30 15 C -6.5556 3.8133 0.0 0 +M V30 16 O -6.5674 2.3303 0.0 0 +M V30 17 N -7.8621 4.5783 0.0 0 +M V30 18 C -9.1685 3.8369 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 17 18 +M V30 18 1 6 8 +M V30 19 1 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +531 + +> +Z30271708 + +> +245.277 + +> +-0.439 + +> +2 + +> +70.560 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL2377256 + +> +0.92 + +$$$$ +Compound 532 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.4912 -0.1409 0.0 0 +M V30 3 C -2.3835 1.0802 0.0 0 +M V30 4 C -2.1135 -1.4912 0.0 0 +M V30 5 C -3.8748 0.9393 0.0 0 +M V30 6 C -3.6047 -1.6321 0.0 0 +M V30 7 O -4.7671 2.1605 0.0 0 +M V30 8 C -4.4971 -0.4109 0.0 0 +M V30 9 C -6.2584 2.0195 0.0 0 +M V30 10 C -5.9883 -0.5518 0.0 0 +M V30 11 C -7.1507 3.2407 0.0 0 +M V30 12 O -6.8807 0.6692 0.0 0 +M V30 13 N -6.6106 -1.9021 0.0 0 +M V30 14 N -6.7045 4.6732 0.0 0 +M V30 15 C -8.6537 3.2524 0.0 0 +M V30 16 C -7.9257 5.5656 0.0 0 +M V30 17 S -9.1234 4.685 0.0 0 +M V30 18 C -7.9374 7.0685 0.0 0 +M V30 19 C -6.6576 7.82 0.0 0 +M V30 20 C -9.2408 7.82 0.0 0 +M V30 21 C -6.6693 9.323 0.0 0 +M V30 22 C -9.2525 9.323 0.0 0 +M V30 23 C -7.9727 10.0745 0.0 0 +M V30 24 C -7.9844 11.5774 0.0 0 +M V30 25 O -6.7045 12.3289 0.0 0 +M V30 26 N -9.2878 12.3289 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 1 10 13 +M V30 13 1 11 14 +M V30 14 2 11 15 +M V30 15 2 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 2 21 23 +M V30 23 1 23 24 +M V30 24 2 24 25 +M V30 25 1 24 26 +M V30 26 1 6 8 +M V30 27 1 16 17 +M V30 28 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +532 + +> +Z913565914 + +> +387.840 + +> +2.443 + +> +2 + +> +108.300 + +> +6 + +> +parp14 + +> + + +> + + +$$$$ +Compound 533 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 N -1.4812 0.3199 0.0 0 +M V30 3 C 0.1422 -1.4812 0.0 0 +M V30 4 C -2.2397 -0.9717 0.0 0 +M V30 5 N -1.2442 -2.0856 0.0 0 +M V30 6 N 1.4338 -2.216 0.0 0 +M V30 7 C -3.7447 -1.1139 0.0 0 +M V30 8 C 2.7255 -1.4575 0.0 0 +M V30 9 C -4.3727 -2.4767 0.0 0 +M V30 10 C -4.6453 0.1185 0.0 0 +M V30 11 O 2.7137 0.0592 0.0 0 +M V30 12 C 4.0172 -2.1923 0.0 0 +M V30 13 C -5.8777 -2.6189 0.0 0 +M V30 14 C -6.1503 -0.0237 0.0 0 +M V30 15 C 5.3089 -1.4338 0.0 0 +M V30 16 C -6.7784 -1.3864 0.0 0 +M V30 17 C 6.6006 -2.1686 0.0 0 +M V30 18 N 7.8923 -1.4101 0.0 0 +M V30 19 N 6.5888 -3.6617 0.0 0 +M V30 20 C 9.184 -2.1449 0.0 0 +M V30 21 C 7.8804 -4.4083 0.0 0 +M V30 22 C 9.1721 -3.638 0.0 0 +M V30 23 C 10.4757 -1.3864 0.0 0 +M V30 24 O 7.8686 -5.9014 0.0 0 +M V30 25 C 10.4638 -4.3846 0.0 0 +M V30 26 C 11.7674 -2.1212 0.0 0 +M V30 27 C 11.7555 -3.6143 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 2 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 12 15 +M V30 15 2 13 16 +M V30 16 1 15 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 1 20 23 +M V30 23 2 21 24 +M V30 24 1 22 25 +M V30 25 2 23 26 +M V30 26 2 25 27 +M V30 27 1 4 5 +M V30 28 1 14 16 +M V30 29 1 21 22 +M V30 30 1 26 27 +M V30 END BOND +M V30 END CTAB +M END +> +533 + +> +Z737273616 + +> +377.420 + +> +2.079 + +> +2 + +> +96.340 + +> +5 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 534 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.4999 -0.1417 0.0 0 +M V30 3 N -2.1258 -1.4999 0.0 0 +M V30 4 C -2.3974 1.0865 0.0 0 +M V30 5 C -1.3817 -2.7872 0.0 0 CFG=2 +M V30 6 C -3.6021 -1.7951 0.0 0 +M V30 7 N -3.9091 1.0983 0.0 0 +M V30 8 C -1.9486 2.5273 0.0 0 +M V30 9 C -2.3974 -3.8855 0.0 0 +M V30 10 C 0.0944 -2.9289 0.0 0 +M V30 11 C -3.7674 -3.2714 0.0 0 +M V30 12 N -4.3815 2.5392 0.0 0 +M V30 13 C -3.1769 3.4249 0.0 0 +M V30 14 C 0.6968 -4.2871 0.0 0 +M V30 15 C -3.1887 4.9366 0.0 0 +M V30 16 C 2.173 -4.4288 0.0 0 +M V30 17 C -0.2007 -5.4917 0.0 0 +M V30 18 C -4.4997 5.6925 0.0 0 +M V30 19 C 2.7754 -5.787 0.0 0 +M V30 20 C 0.4015 -6.8499 0.0 0 +M V30 21 C -4.5115 7.2042 0.0 0 +M V30 22 C 1.8778 -6.9916 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 5 10 CFG=1 +M V30 10 1 6 11 +M V30 11 1 7 12 +M V30 12 1 8 13 +M V30 13 1 10 14 +M V30 14 1 13 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 1 9 11 +M V30 23 2 12 13 +M V30 24 1 20 22 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 5) +M V30 END COLLECTION +M V30 END CTAB +M END +> +534 + +> +Z298616602 + +> +297.395 + +> +3.391 + +> +1 + +> +48.990 + +> +5 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 535 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.4628 -0.2973 0.0 0 +M V30 3 C 1.9148 -1.7245 0.0 0 +M V30 4 C 2.4619 0.8325 0.0 0 +M V30 5 C 3.3777 -2.0218 0.0 0 +M V30 6 C 1.1417 -3.0209 0.0 0 +M V30 7 C 3.9248 0.5352 0.0 0 +M V30 8 C 4.3767 -0.892 0.0 0 +M V30 9 C 3.5204 -3.5085 0.0 0 CFG=2 +M V30 10 C 2.1408 -4.1269 0.0 0 +M V30 11 N 4.8168 -4.2459 0.0 0 +M V30 12 C 6.1131 -3.4847 0.0 0 +M V30 13 O 6.1012 -1.9624 0.0 0 +M V30 14 C 7.4095 -4.2221 0.0 0 +M V30 15 N 7.3976 -5.7206 0.0 0 +M V30 16 C 8.7059 -3.4609 0.0 0 +M V30 17 C 8.694 -6.4699 0.0 0 +M V30 18 C 10.0022 -4.1983 0.0 0 +M V30 19 O 8.6821 -7.9685 0.0 0 +M V30 20 C 9.9904 -5.6969 0.0 0 +M V30 21 C 11.2986 -3.4371 0.0 0 +M V30 22 C 11.2867 -6.4461 0.0 0 +M V30 23 C 12.595 -4.1745 0.0 0 +M V30 24 C 12.5831 -5.6731 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 9 11 CFG=1 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 2 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 17 19 +M V30 19 1 17 20 +M V30 20 1 18 21 +M V30 21 1 20 22 +M V30 22 2 21 23 +M V30 23 2 22 24 +M V30 24 1 7 8 +M V30 25 1 9 10 +M V30 26 2 18 20 +M V30 27 1 23 24 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 9) +M V30 END COLLECTION +M V30 END CTAB +M END +> +535 + +> +Z812197000 + +> +322.333 + +> +2.640 + +> +2 + +> +58.200 + +> +2 + +> +parp3 + +> + + +> + + +$$$$ +Compound 536 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5102 0.0 0 +M V30 3 N -1.3214 2.2653 0.0 0 +M V30 4 N 1.2742 2.2653 0.0 0 +M V30 5 C -1.3332 3.7755 0.0 0 +M V30 6 C -2.6311 1.5338 0.0 0 +M V30 7 C 2.5603 1.5338 0.0 0 +M V30 8 C -2.6429 4.5307 0.0 0 +M V30 9 C -3.9407 2.2889 0.0 0 +M V30 10 C 3.8463 2.2889 0.0 0 +M V30 11 C -3.9525 3.7873 0.0 0 +M V30 12 O 5.1324 1.5574 0.0 0 +M V30 13 C -5.2622 4.5425 0.0 0 +M V30 14 C 5.1206 0.0707 0.0 0 +M V30 15 C -5.4274 6.0409 0.0 0 +M V30 16 C -6.6426 3.9407 0.0 0 +M V30 17 C 6.4067 -0.6607 0.0 0 +M V30 18 C 3.8109 -0.6607 0.0 0 +M V30 19 C -6.9022 6.3595 0.0 0 +M V30 20 C -4.4363 7.1618 0.0 0 +M V30 21 N -7.6573 5.0616 0.0 0 +M V30 22 C 6.3949 -2.1473 0.0 0 +M V30 23 C 3.7991 -2.1473 0.0 0 +M V30 24 N -7.3742 7.7989 0.0 0 +M V30 25 C -4.9082 8.6012 0.0 0 +M V30 26 C 5.0852 -2.8788 0.0 0 +M V30 27 C -6.3831 8.9198 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 2 13 16 +M V30 16 2 14 17 +M V30 17 1 14 18 +M V30 18 2 15 19 +M V30 19 1 15 20 +M V30 20 1 16 21 +M V30 21 1 17 22 +M V30 22 2 18 23 +M V30 23 1 19 24 +M V30 24 2 20 25 +M V30 25 2 22 26 +M V30 26 2 24 27 +M V30 27 1 9 11 +M V30 28 1 19 21 +M V30 29 1 23 26 +M V30 30 1 25 27 +M V30 END BOND +M V30 END CTAB +M END +> +536 + +> +Z336508756 + +> +362.425 + +> +3.212 + +> +2 + +> +70.250 + +> +5 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1965845 + +> +0.86 + +$$$$ +Compound 537 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 N -1.3067 -0.7416 0.0 0 CHG=1 +M V30 3 O -1.3185 -2.225 0.0 0 CHG=-1 +M V30 4 C -2.6134 0.0117 0.0 0 +M V30 5 C -3.9202 -0.7298 0.0 0 +M V30 6 C -2.6252 1.5186 0.0 0 +M V30 7 C -5.2269 0.0235 0.0 0 +M V30 8 C -3.932 2.272 0.0 0 +M V30 9 N -6.5337 -0.7181 0.0 0 +M V30 10 C -5.2387 1.5304 0.0 0 +M V30 11 C -7.8404 0.0353 0.0 0 +M V30 12 O -7.8522 1.5422 0.0 0 +M V30 13 N -9.1472 -0.7063 0.0 0 +M V30 14 C -10.4539 0.047 0.0 0 +M V30 15 C -11.7607 -0.6945 0.0 0 +M V30 16 C -13.0674 0.0588 0.0 0 +M V30 17 N -14.3742 -0.6828 0.0 0 +M V30 18 C -14.386 -2.1661 0.0 0 +M V30 19 C -15.6809 0.0706 0.0 0 +M V30 20 C -15.6927 -2.896 0.0 0 CFG=1 +M V30 21 C -16.9877 -0.671 0.0 0 +M V30 22 C -15.7045 -4.3793 0.0 0 +M V30 23 C -16.9995 -2.1425 0.0 0 +M V30 24 O -14.4213 -5.1092 0.0 0 +M V30 25 N -17.0112 -5.1092 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 2 7 10 +M V30 10 1 9 11 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 20 22 CFG=1 +M V30 22 1 20 23 +M V30 23 2 22 24 +M V30 24 1 22 25 +M V30 25 1 8 10 +M V30 26 1 21 23 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 20) +M V30 END COLLECTION +M V30 END CTAB +M END +> +537 + +> +Z1033336030 + +> +349.385 + +> +1.695 + +> +3 + +> +130.600 + +> +7 + +> +DNA_pk + +> + + +> + + +$$$$ +Compound 538 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5085 0.0 0 +M V30 3 N -1.3199 2.2746 0.0 0 +M V30 4 C 1.2728 2.2746 0.0 0 +M V30 5 C -1.3317 3.7831 0.0 0 +M V30 6 C 1.261 3.7831 0.0 0 +M V30 7 C 2.5574 1.5321 0.0 0 +M V30 8 C -2.6399 4.5374 0.0 0 +M V30 9 C -0.0471 4.5374 0.0 0 +M V30 10 C 2.5456 4.5374 0.0 0 +M V30 11 C 3.842 2.2981 0.0 0 +M V30 12 O -2.6517 6.0459 0.0 0 +M V30 13 N -3.9481 3.8067 0.0 0 +M V30 14 C 3.8303 3.8067 0.0 0 +M V30 15 C -5.2563 4.561 0.0 0 +M V30 16 C -3.9599 2.3217 0.0 0 +M V30 17 C -6.5645 3.8303 0.0 0 +M V30 18 C -5.2681 6.0695 0.0 0 +M V30 19 C -5.2681 1.5792 0.0 0 +M V30 20 C -6.647 2.2038 0.0 0 +M V30 21 C -5.4331 0.106 0.0 0 +M V30 22 N -7.6606 1.1078 0.0 0 +M V30 23 N -6.9063 -0.1885 0.0 0 +M V30 24 C -7.5309 -1.5439 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 1 13 16 +M V30 16 1 15 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 1 19 20 +M V30 20 2 19 21 +M V30 21 2 20 22 +M V30 22 1 21 23 +M V30 23 1 23 24 +M V30 24 1 6 9 +M V30 25 1 11 14 +M V30 26 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +538 + +> +Z776182730 + +> +324.377 + +> +0.910 + +> +1 + +> +67.230 + +> +4 + +> +parp2 + +> + + +> + + +$$$$ +Compound 539 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 38 41 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 0.8717 1.2251 0.0 0 +M V30 3 C -1.4961 0.1649 0.0 0 +M V30 4 N 2.356 1.2369 0.0 0 +M V30 5 N 0.4005 2.6623 0.0 0 +M V30 6 C -2.3914 -1.0366 0.0 0 +M V30 7 N 2.8037 2.6741 0.0 0 +M V30 8 C -1.0366 3.1335 0.0 0 +M V30 9 C 1.6021 3.5576 0.0 0 +M V30 10 O -1.7906 -2.3914 0.0 0 +M V30 11 N -3.8875 -0.8717 0.0 0 +M V30 12 C -2.1558 2.144 0.0 0 +M V30 13 C -1.3547 4.6061 0.0 0 +M V30 14 C 1.5903 5.0655 0.0 0 +M V30 15 C -4.7828 -2.0733 0.0 0 +M V30 16 C -3.593 2.6152 0.0 0 +M V30 17 C -2.7919 5.0773 0.0 0 +M V30 18 C 2.8744 5.8195 0.0 0 +M V30 19 C 0.2827 5.8195 0.0 0 +M V30 20 C -4.182 -3.428 0.0 0 +M V30 21 C -6.2789 -1.9084 0.0 0 +M V30 22 C -3.911 4.0877 0.0 0 +M V30 23 C 2.8626 7.3274 0.0 0 +M V30 24 C 0.2709 7.3274 0.0 0 +M V30 25 C -5.0773 -4.6296 0.0 0 +M V30 26 C -7.1742 -3.11 0.0 0 +M V30 27 C 1.555 8.0813 0.0 0 +M V30 28 C -4.4765 -5.9844 0.0 0 +M V30 29 C -6.5734 -4.4647 0.0 0 +M V30 30 C -8.6703 -2.945 0.0 0 +M V30 31 C 1.5432 9.5892 0.0 0 +M V30 32 O -5.3718 -7.186 0.0 0 +M V30 33 N -3.0039 -6.1258 0.0 0 +M V30 34 O -9.5656 -4.1466 0.0 0 +M V30 35 N -9.2947 -1.5667 0.0 0 +M V30 36 C 1.5314 11.0971 0.0 0 +M V30 37 C 0.0353 9.601 0.0 0 +M V30 38 C 3.0275 9.601 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 1 9 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 2 13 17 +M V30 17 2 14 18 +M V30 18 1 14 19 +M V30 19 2 15 20 +M V30 20 1 15 21 +M V30 21 2 16 22 +M V30 22 1 18 23 +M V30 23 2 19 24 +M V30 24 1 20 25 +M V30 25 2 21 26 +M V30 26 2 23 27 +M V30 27 1 25 28 +M V30 28 2 25 29 +M V30 29 1 26 30 +M V30 30 1 27 31 +M V30 31 2 28 32 +M V30 32 1 28 33 +M V30 33 2 30 34 +M V30 34 1 30 35 +M V30 35 1 31 36 +M V30 36 1 31 37 +M V30 37 1 31 38 +M V30 38 2 7 9 +M V30 39 1 17 22 +M V30 40 1 24 27 +M V30 41 1 26 29 +M V30 END BOND +M V30 END CTAB +M END +> +539 + +> +Z13857099 + +> +528.625 + +> +3.835 + +> +3 + +> +145.990 + +> +9 + +> +parp14 + +> + + +> + + +$$$$ +Compound 540 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.2956 -1.4545 0.0 0 +M V30 3 N 1.6555 -2.0576 0.0 0 +M V30 4 N -0.7213 -2.5543 0.0 0 +M V30 5 C 1.49 -3.5358 0.0 0 +M V30 6 C 2.9445 -1.3008 0.0 0 +M V30 7 C 0.0118 -3.8433 0.0 0 +M V30 8 O 2.5897 -4.5291 0.0 0 +M V30 9 C 2.9327 0.2128 0.0 0 +M V30 10 C -1.49 -3.9852 0.0 0 +M V30 11 C 0.8869 -5.0495 0.0 0 +M V30 12 O 1.6201 0.9815 0.0 0 +M V30 13 N 4.2217 0.9815 0.0 0 +M V30 14 C -2.1167 -5.3451 0.0 0 +M V30 15 C 0.2601 -6.4094 0.0 0 +M V30 16 C 4.2099 2.4951 0.0 0 CFG=2 +M V30 17 C -1.2416 -6.5513 0.0 0 +M V30 18 C 5.4988 3.252 0.0 0 +M V30 19 C 2.8972 3.252 0.0 0 +M V30 20 C -1.8684 -7.9113 0.0 0 +M V30 21 C 5.487 4.7657 0.0 0 +M V30 22 C 6.7878 2.507 0.0 0 +M V30 23 C 2.8854 4.7657 0.0 0 +M V30 24 C 6.776 5.5225 0.0 0 +M V30 25 C 4.1744 5.5225 0.0 0 +M V30 26 C 8.0768 3.2638 0.0 0 +M V30 27 C 8.065 4.7893 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 1 7 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 16 13 CFG=1 +M V30 16 1 14 17 +M V30 17 1 16 18 +M V30 18 1 16 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 1 18 22 +M V30 22 1 19 23 +M V30 23 1 21 24 +M V30 24 1 21 25 +M V30 25 2 22 26 +M V30 26 2 24 27 +M V30 27 1 5 7 +M V30 28 1 15 17 +M V30 29 1 23 25 +M V30 30 1 26 27 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 16) +M V30 END COLLECTION +M V30 END CTAB +M END +> +540 + +> +Z13864398 + +> +369.457 + +> +3.636 + +> +2 + +> +78.510 + +> +3 + +> +ATM + +> + + +> + + +$$$$ +Compound 541 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 29 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2864 -0.7435 0.0 0 +M V30 3 N 1.2746 -2.2305 0.0 0 +M V30 4 C 2.5728 0.0236 0.0 0 CFG=1 +M V30 5 C 2.561 -2.9623 0.0 0 +M V30 6 C -0.0354 -2.9623 0.0 0 +M V30 7 O 3.8592 -0.7199 0.0 0 +M V30 8 C 2.561 1.5342 0.0 0 +M V30 9 C 3.8474 -2.2069 0.0 0 +M V30 10 C 2.5492 -4.4493 0.0 0 +M V30 11 C -1.3454 -2.2069 0.0 0 +M V30 12 C 5.1339 -2.9387 0.0 0 +M V30 13 C 3.8356 -5.1811 0.0 0 +M V30 14 C -2.6554 -2.9387 0.0 0 +M V30 15 C 5.1221 -4.4257 0.0 0 +M V30 16 O -2.6672 -4.4257 0.0 0 +M V30 17 N -3.9655 -2.1833 0.0 0 +M V30 18 C -5.2755 -2.9151 0.0 0 +M V30 19 C -3.9773 -0.6727 0.0 0 +M V30 20 C -6.5855 -2.1597 0.0 0 +M V30 21 C -6.5973 -0.6491 0.0 0 +M V30 22 C -7.8955 -2.8915 0.0 0 +M V30 23 C -7.9073 0.118 0.0 0 +M V30 24 C -9.2056 -2.1361 0.0 0 +M V30 25 C -9.2174 -0.6255 0.0 0 +M V30 26 C -10.5274 0.1416 0.0 0 +M V30 27 O -10.5392 1.6522 0.0 0 +M V30 28 N -11.8374 -0.6019 0.0 0 +M V30 29 C -13.1475 0.1652 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 4 8 CFG=3 +M V30 8 2 5 9 +M V30 9 1 5 10 +M V30 10 1 6 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 11 14 +M V30 14 2 12 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 1 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 2 20 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 2 22 24 +M V30 24 2 23 25 +M V30 25 1 25 26 +M V30 26 2 26 27 +M V30 27 1 26 28 +M V30 28 1 28 29 +M V30 29 1 7 9 +M V30 30 1 13 15 +M V30 31 1 24 25 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 4) +M V30 END COLLECTION +M V30 END CTAB +M END +> +541 + +> +Z167358780 + +> +395.452 + +> +1.772 + +> +1 + +> +78.950 + +> +6 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL2407972 + +> +0.87 + +$$$$ +Compound 542 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -1.3167 -0.7473 0.0 0 +M V30 3 C -1.3285 -2.2419 0.0 0 +M V30 4 C -2.6334 0.0237 0.0 0 +M V30 5 C -2.6453 -2.9774 0.0 0 +M V30 6 C -3.9501 -0.7236 0.0 0 +M V30 7 C -2.6571 -4.4721 0.0 0 +M V30 8 C -3.962 -2.2182 0.0 0 +M V30 9 O -3.9739 -5.2076 0.0 0 +M V30 10 N -1.3641 -5.2076 0.0 0 +M V30 11 C -1.376 -6.7022 0.0 0 +M V30 12 C -0.0711 -4.4484 0.0 0 +M V30 13 C -0.083 -7.4377 0.0 0 +M V30 14 N 1.2099 -6.6785 0.0 0 +M V30 15 N -0.0948 -8.9324 0.0 0 +M V30 16 C 2.5029 -7.414 0.0 0 +M V30 17 C 1.1981 -9.6797 0.0 0 +M V30 18 C 2.4911 -8.9086 0.0 0 +M V30 19 C 3.7959 -6.6548 0.0 0 +M V30 20 O 1.1862 -11.1744 0.0 0 +M V30 21 C 3.7841 -9.656 0.0 0 +M V30 22 C 5.0889 -7.3902 0.0 0 +M V30 23 C 5.0771 -8.8968 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 10 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 2 21 23 +M V30 23 1 6 8 +M V30 24 1 17 18 +M V30 25 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +542 + +> +Z30084953 + +> +311.310 + +> +1.177 + +> +1 + +> +61.770 + +> +3 + +> +parp15 + +> + + +> + + +$$$$ +Compound 543 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5083 0.0 0 +M V30 3 N -1.3197 2.2742 0.0 0 +M V30 4 C 1.2726 2.2742 0.0 0 +M V30 5 C -1.3315 3.7825 0.0 0 +M V30 6 C 1.2608 3.7825 0.0 0 +M V30 7 C 2.557 1.5318 0.0 0 +M V30 8 N -0.0471 4.5367 0.0 0 +M V30 9 C -2.6395 4.5367 0.0 0 +M V30 10 C 2.5452 4.5367 0.0 0 +M V30 11 C 3.8414 2.2978 0.0 0 +M V30 12 N -3.9475 3.8061 0.0 0 +M V30 13 C 3.8296 3.8061 0.0 0 +M V30 14 C -3.9593 2.3213 0.0 0 +M V30 15 C -5.2555 4.5602 0.0 0 +M V30 16 C -5.2673 1.579 0.0 0 +M V30 17 C -3.4644 0.9309 0.0 0 +M V30 18 C -2.4981 2.5924 0.0 0 +M V30 19 C -6.5635 3.8296 0.0 0 CFG=2 +M V30 20 O -6.5752 2.3449 0.0 0 +M V30 21 C -7.8714 4.5838 0.0 0 +M V30 22 C -9.1794 3.8532 0.0 0 +M V30 23 C -7.8832 6.0921 0.0 0 +M V30 24 C -10.4874 4.6074 0.0 0 +M V30 25 C -9.1912 6.8463 0.0 0 +M V30 26 C -10.4992 6.1157 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 1 12 15 +M V30 15 1 14 16 +M V30 16 1 14 17 +M V30 17 1 14 18 +M V30 18 1 15 19 +M V30 19 1 16 20 +M V30 20 1 19 21 CFG=3 +M V30 21 2 21 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 2 23 25 +M V30 25 2 24 26 +M V30 26 1 6 8 +M V30 27 1 11 13 +M V30 28 1 19 20 +M V30 29 1 25 26 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 19) +M V30 END COLLECTION +M V30 END CTAB +M END +> +543 + +> +Z1120829637 + +> +349.426 + +> +2.648 + +> +1 + +> +53.930 + +> +3 + +> +parp1 + +> + + +> + + +$$$$ +Compound 544 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.0993 1.0165 0.0 0 +M V30 3 C 0.9338 2.5178 0.0 0 +M V30 4 C 2.5533 0.721 0.0 0 +M V30 5 N 2.2932 3.1443 0.0 0 +M V30 6 C -0.3782 3.2743 0.0 0 +M V30 7 C 3.298 2.0331 0.0 0 +M V30 8 C 3.298 -0.5674 0.0 0 +M V30 9 O -0.39 4.7874 0.0 0 +M V30 10 N -1.6903 2.5414 0.0 0 +M V30 11 C 4.7874 2.045 0.0 0 +M V30 12 C 4.7874 -0.5555 0.0 0 +M V30 13 C -3.0024 3.298 0.0 0 +M V30 14 C 5.5321 0.7565 0.0 0 +M V30 15 C -4.3146 2.5651 0.0 0 +M V30 16 O -4.3264 1.0756 0.0 0 +M V30 17 N -5.6267 3.3216 0.0 0 +M V30 18 C -6.9388 2.5887 0.0 0 +M V30 19 C -6.9506 1.0993 0.0 0 +M V30 20 C -8.2509 3.3453 0.0 0 +M V30 21 C -8.2627 0.3664 0.0 0 +M V30 22 C -5.6621 0.3664 0.0 0 +M V30 23 C -9.563 2.6124 0.0 0 +M V30 24 C -9.5748 1.1229 0.0 0 +M V30 25 C -5.674 -1.1229 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 10 13 +M V30 13 2 11 14 +M V30 14 1 13 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 17 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 19 22 +M V30 22 2 20 23 +M V30 23 2 21 24 +M V30 24 1 22 25 +M V30 25 1 5 7 +M V30 26 1 12 14 +M V30 27 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +544 + +> +Z463392016 + +> +355.818 + +> +2.627 + +> +3 + +> +73.990 + +> +5 + +> +ATM + +> + + +> + + +$$$$ +Compound 545 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 0.8716 1.225 0.0 0 +M V30 3 C 2.3441 1.0837 0.0 0 +M V30 4 C 0.2473 2.6032 0.0 0 +M V30 5 C 2.9448 -0.2709 0.0 0 +M V30 6 C 3.2157 2.3087 0.0 0 +M V30 7 C 1.119 3.8283 0.0 0 +M V30 8 O 2.0496 -1.4724 0.0 0 +M V30 9 N 4.4172 -0.4122 0.0 0 +M V30 10 C 2.5914 3.6869 0.0 0 +M V30 11 N 0.4947 5.2064 0.0 0 +M V30 12 C 1.3664 6.4315 0.0 0 +M V30 13 O 2.8388 6.2902 0.0 0 +M V30 14 C 0.7421 7.8097 0.0 0 +M V30 15 N 1.6137 9.0348 0.0 0 +M V30 16 C 3.0979 9.0465 0.0 0 +M V30 17 C 1.1426 10.4718 0.0 0 +M V30 18 S 3.5455 10.4836 0.0 0 +M V30 19 O 3.9696 7.845 0.0 0 +M V30 20 C -0.2944 10.943 0.0 0 +M V30 21 C 2.3441 11.3671 0.0 0 +M V30 22 C -1.4135 9.9535 0.0 0 +M V30 23 C -0.6125 12.4154 0.0 0 +M V30 24 C 2.3323 12.8748 0.0 0 +M V30 25 C -2.8506 10.4247 0.0 0 +M V30 26 C -2.0496 12.8866 0.0 0 +M V30 27 C -3.1686 11.8971 0.0 0 +M V30 28 F -4.6057 12.3683 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 7 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 16 19 +M V30 19 1 17 20 +M V30 20 2 17 21 +M V30 21 2 20 22 +M V30 22 1 20 23 +M V30 23 1 21 24 +M V30 24 1 22 25 +M V30 25 2 23 26 +M V30 26 2 25 27 +M V30 27 1 27 28 +M V30 28 1 7 10 +M V30 29 1 18 21 +M V30 30 1 26 27 +M V30 END BOND +M V30 END CTAB +M END +> +545 + +> +Z443922120 + +> +419.857 + +> +1.619 + +> +2 + +> +92.500 + +> +5 + +> +parp14 + +> + + +> + + +$$$$ +Compound 546 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5072 0.0 0 +M V30 3 N -1.3188 2.2727 0.0 0 +M V30 4 C 1.2717 2.2727 0.0 0 +M V30 5 C -1.3306 3.7799 0.0 0 +M V30 6 C 1.2599 3.7799 0.0 0 +M V30 7 C 2.5553 1.5308 0.0 0 +M V30 8 C -2.6377 4.5336 0.0 0 +M V30 9 C -0.0471 4.5336 0.0 0 +M V30 10 C 2.5435 4.5336 0.0 0 +M V30 11 C 3.8388 2.2962 0.0 0 +M V30 12 O -2.6495 6.0409 0.0 0 +M V30 13 N -3.9448 3.8035 0.0 0 +M V30 14 C 3.8271 3.8035 0.0 0 +M V30 15 C -5.2519 4.5571 0.0 0 CFG=2 +M V30 16 C -6.559 3.8271 0.0 0 +M V30 17 C -5.2637 6.0644 0.0 0 +M V30 18 C -7.9368 4.4512 0.0 0 +M V30 19 C -6.7239 2.3551 0.0 0 +M V30 20 N -8.9495 3.356 0.0 0 +M V30 21 C -8.2547 5.9231 0.0 0 +M V30 22 N -8.1958 2.0607 0.0 0 +M V30 23 C -10.445 3.5209 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 2 10 14 +M V30 14 1 15 13 CFG=3 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 1 18 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 1 20 23 +M V30 23 1 6 9 +M V30 24 1 11 14 +M V30 25 1 20 22 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 15) +M V30 END COLLECTION +M V30 END CTAB +M END +> +546 + +> +Z395943628 + +> +310.350 + +> +0.726 + +> +2 + +> +76.020 + +> +3 + +> +parp15, Tankyrase1 + +> + + +> + + +$$$$ +Compound 547 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5047 0.0 0 +M V30 3 N -1.3166 2.2688 0.0 0 +M V30 4 C 1.2696 2.2688 0.0 0 +M V30 5 C -1.3283 3.7735 0.0 0 +M V30 6 C 1.2578 3.7735 0.0 0 +M V30 7 C 2.5509 1.5282 0.0 0 +M V30 8 N -0.047 4.5258 0.0 0 +M V30 9 C -2.6332 4.5258 0.0 0 +M V30 10 C 2.5392 4.5258 0.0 0 +M V30 11 C 3.8323 2.2923 0.0 0 +M V30 12 C -3.9381 3.797 0.0 0 +M V30 13 C 3.8205 3.797 0.0 0 +M V30 14 C -5.2429 4.5494 0.0 0 +M V30 15 O -5.2547 6.0541 0.0 0 +M V30 16 N -6.5478 3.8205 0.0 0 +M V30 17 C -7.8527 4.5729 0.0 0 +M V30 18 C -7.8644 6.0776 0.0 0 +M V30 19 C -9.1575 3.844 0.0 0 +M V30 20 C -9.1693 6.8417 0.0 0 +M V30 21 C -10.4624 4.5964 0.0 0 +M V30 22 N -10.4742 6.1011 0.0 0 +M V30 23 C -11.779 6.8652 0.0 0 +M V30 24 O -11.7908 8.3699 0.0 0 +M V30 25 C -13.0839 6.1246 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 22 23 +M V30 23 2 23 24 +M V30 24 1 23 25 +M V30 25 1 6 8 +M V30 26 1 11 13 +M V30 27 1 21 22 +M V30 END BOND +M V30 END CTAB +M END +> +547 + +> +Z90652384 + +> +342.392 + +> +-2.160 + +> +2 + +> +90.870 + +> +4 + +> +parp2 + +> + + +> + + +$$$$ +Compound 548 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.5138 0.0 0 +M V30 3 N -1.3246 2.2707 0.0 0 +M V30 4 C 1.2773 2.2707 0.0 0 +M V30 5 C -2.6374 1.5375 0.0 0 +M V30 6 C 2.5664 1.5375 0.0 0 +M V30 7 C 1.2654 3.7846 0.0 0 +M V30 8 C -3.9502 2.2944 0.0 0 +M V30 9 C 3.8555 2.2944 0.0 0 +M V30 10 C 2.5546 0.0473 0.0 0 +M V30 11 C 2.5546 4.5533 0.0 0 +M V30 12 N -5.263 1.5611 0.0 0 +M V30 13 N 3.8437 3.8082 0.0 0 +M V30 14 C 5.1447 1.5611 0.0 0 +M V30 15 C 3.8437 -0.6859 0.0 0 +M V30 16 O 2.5427 6.0672 0.0 0 +M V30 17 C -5.2748 0.0709 0.0 0 CFG=2 +M V30 18 C -6.5757 2.318 0.0 0 +M V30 19 C 5.1329 0.0709 0.0 0 +M V30 20 C -6.5876 -0.6623 0.0 0 +M V30 21 C -3.9856 -0.6623 0.0 0 +M V30 22 C -7.8885 1.5848 0.0 0 +M V30 23 C -7.9004 0.0946 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 2 9 14 +M V30 14 1 10 15 +M V30 15 1 11 16 +M V30 16 1 12 17 +M V30 17 1 12 18 +M V30 18 1 14 19 +M V30 19 1 17 20 +M V30 20 1 17 21 CFG=1 +M V30 21 1 18 22 +M V30 22 1 20 23 +M V30 23 2 11 13 +M V30 24 2 15 19 +M V30 25 1 22 23 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 17) +M V30 END COLLECTION +M V30 END CTAB +M END +> +548 + +> +Z451443816 + +> +313.394 + +> +3.828 + +> +2 + +> +65.460 + +> +4 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 549 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4494 1.4428 0.0 0 +M V30 3 N -0.4494 2.6728 0.0 0 +M V30 4 C 1.8686 1.9159 0.0 0 +M V30 5 C 0.4257 3.9028 0.0 0 +M V30 6 C -1.9632 2.6846 0.0 0 +M V30 7 C 1.8568 3.4297 0.0 0 +M V30 8 C 3.1577 1.1708 0.0 0 +M V30 9 O -0.0473 5.3457 0.0 0 +M V30 10 C 3.1459 4.1866 0.0 0 +M V30 11 C 4.4468 1.9277 0.0 0 +M V30 12 C 4.435 3.4534 0.0 0 +M V30 13 C 5.7359 1.1826 0.0 0 +M V30 14 O 7.0251 1.9395 0.0 0 +M V30 15 N 5.7241 -0.3074 0.0 0 +M V30 16 C 7.0132 -1.0525 0.0 0 +M V30 17 C 7.0014 -2.5427 0.0 0 +M V30 18 C 8.2905 -3.276 0.0 0 +M V30 19 C 5.6886 -3.276 0.0 0 +M V30 20 C 8.2787 -4.7661 0.0 0 +M V30 21 C 5.6768 -4.7661 0.0 0 +M V30 22 C 6.9659 -5.5112 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 2 10 12 +M V30 12 1 11 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 2 20 22 +M V30 22 1 5 7 +M V30 23 1 11 12 +M V30 24 1 21 22 +M V30 END BOND +M V30 END CTAB +M END +> +549 + +> +Z27749066 + +> +294.305 + +> +2.655 + +> +1 + +> +66.480 + +> +3 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL611975 + +> +0.91 + +$$$$ +Compound 550 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.7716 -1.2939 0.0 0 +M V30 3 C -1.0208 1.1277 0.0 0 +M V30 4 C -0.1661 -2.659 0.0 0 +M V30 5 C -2.2554 -0.9734 0.0 0 +M V30 6 C -2.4097 0.5223 0.0 0 +M V30 7 N -0.9378 -3.953 0.0 0 +M V30 8 N 1.2939 -2.9558 0.0 0 +M V30 9 N 0.0593 -5.057 0.0 0 +M V30 10 C 1.4363 -4.4397 0.0 0 +M V30 11 C 2.3979 -1.9349 0.0 0 +M V30 12 S 2.7303 -5.1757 0.0 0 +M V30 13 C 2.0774 -0.451 0.0 0 +M V30 14 C 2.7184 -6.6714 0.0 0 +M V30 15 C 4.0123 -7.4074 0.0 0 +M V30 16 N 5.3062 -6.6477 0.0 0 +M V30 17 N 4.0004 -8.9031 0.0 0 +M V30 18 C 6.6002 -7.3837 0.0 0 +M V30 19 C 5.2944 -9.651 0.0 0 +M V30 20 C 6.5883 -8.8794 0.0 0 +M V30 21 C 7.8941 -6.6239 0.0 0 +M V30 22 O 5.2825 -11.1467 0.0 0 +M V30 23 C 7.8822 -9.6273 0.0 0 +M V30 24 C 9.188 -7.3599 0.0 0 +M V30 25 C 9.1762 -8.8556 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 1 8 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 1 20 23 +M V30 23 2 21 24 +M V30 24 2 23 25 +M V30 25 1 5 6 +M V30 26 2 9 10 +M V30 27 1 19 20 +M V30 28 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +550 + +> +Z14376268 + +> +369.464 + +> +2.008 + +> +1 + +> +72.170 + +> +5 + +> +parp10 + +> + + +> + + +$$$$ +Compound 551 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 30 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.001 -1.1291 0.0 0 +M V30 3 C -0.5238 -2.5376 0.0 0 +M V30 4 C -2.491 -0.8381 0.0 0 +M V30 5 C -1.5248 -3.6667 0.0 0 +M V30 6 C -3.4921 -1.9672 0.0 0 +M V30 7 N -1.0476 -5.0752 0.0 0 +M V30 8 C -3.0148 -3.3757 0.0 0 +M V30 9 C -2.0487 -6.2043 0.0 0 +M V30 10 O -4.0159 -4.5048 0.0 0 +M V30 11 O -3.5386 -5.9133 0.0 0 +M V30 12 C -1.5714 -7.6128 0.0 0 +M V30 13 C -5.5058 -4.2138 0.0 0 +M V30 14 N -0.1047 -7.8805 0.0 0 +M V30 15 C 0.873 -6.7281 0.0 0 +M V30 16 C 0.3724 -9.289 0.0 0 +M V30 17 C 2.3397 -6.9958 0.0 0 +M V30 18 O 2.8169 -8.4043 0.0 0 +M V30 19 N 3.3175 -5.8434 0.0 0 +M V30 20 C 4.7841 -6.1111 0.0 0 +M V30 21 O 5.2614 -7.5196 0.0 0 +M V30 22 N 5.7619 -4.9587 0.0 0 +M V30 23 C 7.2286 -5.2265 0.0 0 +M V30 24 C 7.7059 -6.635 0.0 0 +M V30 25 C 8.2064 -4.0741 0.0 0 +M V30 26 C 9.1726 -6.9027 0.0 0 +M V30 27 C 6.7048 -7.7641 0.0 0 +M V30 28 C 9.6731 -4.3418 0.0 0 +M V30 29 C 10.1504 -5.7503 0.0 0 +M V30 30 C 9.6498 -8.3112 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 1 9 12 +M V30 12 1 10 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 2 20 21 +M V30 21 1 20 22 +M V30 22 1 22 23 +M V30 23 1 23 24 +M V30 24 1 23 25 +M V30 25 1 24 26 +M V30 26 1 24 27 +M V30 27 1 25 28 +M V30 28 1 26 29 +M V30 29 1 26 30 +M V30 30 1 6 8 +M V30 31 1 28 29 +M V30 END BOND +M V30 END CTAB +M END +> +551 + +> +Z46393223 + +> +438.948 + +> +4.294 + +> +3 + +> +99.770 + +> +7 + +> +ATM + +> + + +> + + +$$$$ +Compound 552 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4491 1.4419 0.0 0 +M V30 3 N -0.4491 2.6711 0.0 0 +M V30 4 C 1.8674 1.9146 0.0 0 +M V30 5 C 0.4254 3.9002 0.0 0 +M V30 6 C -1.9619 2.6829 0.0 0 +M V30 7 C 1.8555 3.4275 0.0 0 +M V30 8 C 3.1556 1.17 0.0 0 +M V30 9 O -0.0472 5.3422 0.0 0 +M V30 10 C 3.1438 4.1957 0.0 0 +M V30 11 C 4.4439 1.9383 0.0 0 +M V30 12 C 4.4321 3.4629 0.0 0 +M V30 13 C 5.7322 1.1937 0.0 0 +M V30 14 O 5.7204 -0.2954 0.0 0 +M V30 15 N 7.0205 1.9619 0.0 0 +M V30 16 C 8.3088 1.2173 0.0 0 +M V30 17 C 9.5971 1.9856 0.0 0 +M V30 18 C 8.297 -0.2718 0.0 0 +M V30 19 C 10.8853 1.241 0.0 0 +M V30 20 C 9.5852 -1.0164 0.0 0 +M V30 21 C 10.8735 -0.2482 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 2 10 12 +M V30 12 1 11 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 5 7 +M V30 22 1 11 12 +M V30 23 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +552 + +> +Z27759192 + +> +286.326 + +> +2.718 + +> +1 + +> +66.480 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL597888 + +> +0.91 + +$$$$ +Compound 553 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 30 34 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.7553 1.3101 0.0 0 +M V30 3 C 1.4517 0.3186 0.0 0 +M V30 4 N 0.236 2.4314 0.0 0 +M V30 5 N -2.2543 1.4753 0.0 0 +M V30 6 C 1.5934 1.8176 0.0 0 +M V30 7 C -3.1514 0.2714 0.0 0 +M V30 8 C 2.8799 2.5848 0.0 0 +M V30 9 O -2.5494 -1.0858 0.0 0 +M V30 10 C -4.6504 0.4367 0.0 0 +M V30 11 C 4.1664 1.8412 0.0 0 +M V30 12 C 2.8681 4.0956 0.0 0 +M V30 13 C -5.5474 -0.7672 0.0 0 +M V30 14 C 5.453 2.6084 0.0 0 +M V30 15 C 4.1546 4.8628 0.0 0 +M V30 16 N -4.9454 -2.1245 0.0 0 +M V30 17 C -7.0464 -0.6019 0.0 0 +M V30 18 O 6.7395 1.8648 0.0 0 +M V30 19 C 5.4412 4.1192 0.0 0 +M V30 20 N -5.8425 -3.3284 0.0 0 +M V30 21 C -7.9434 -1.8058 0.0 0 +M V30 22 C -7.672 0.779 0.0 0 +M V30 23 C 8.026 2.632 0.0 0 +M V30 24 O 6.7277 4.8864 0.0 0 +M V30 25 C -7.3415 -3.1632 0.0 0 +M V30 26 C -9.4424 -1.6406 0.0 0 +M V30 27 C -9.1709 0.9442 0.0 0 +M V30 28 C 8.0142 4.1428 0.0 0 +M V30 29 O -8.2385 -4.3671 0.0 0 +M V30 30 C -10.068 -0.2596 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 10 13 +M V30 13 1 11 14 +M V30 14 2 12 15 +M V30 15 2 13 16 +M V30 16 1 13 17 +M V30 17 1 14 18 +M V30 18 2 14 19 +M V30 19 1 16 20 +M V30 20 2 17 21 +M V30 21 1 17 22 +M V30 22 1 18 23 +M V30 23 1 19 24 +M V30 24 1 20 25 +M V30 25 1 21 26 +M V30 26 2 22 27 +M V30 27 1 23 28 +M V30 28 2 25 29 +M V30 29 2 26 30 +M V30 30 1 4 6 +M V30 31 1 15 19 +M V30 32 1 21 25 +M V30 33 1 24 28 +M V30 34 1 27 30 +M V30 END BOND +M V30 END CTAB +M END +> +553 + +> +Z28671689 + +> +420.441 + +> +2.047 + +> +2 + +> +101.910 + +> +4 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 554 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 30 32 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O 0.7436 1.3101 0.0 0 +M V30 3 O -0.7554 -1.2865 0.0 0 +M V30 4 N -1.3101 0.7554 0.0 0 +M V30 5 C 1.2865 -0.7436 0.0 0 +M V30 6 C -1.3219 2.2662 0.0 0 +M V30 7 C -2.6321 3.0216 0.0 0 CFG=2 +M V30 8 N -2.6439 4.5325 0.0 0 +M V30 9 C -3.9423 2.2898 0.0 0 +M V30 10 C -1.3574 5.2879 0.0 0 +M V30 11 C -3.9541 5.2879 0.0 0 +M V30 12 C -5.2525 3.0453 0.0 0 +M V30 13 O -1.3692 6.7988 0.0 0 +M V30 14 N -0.0708 4.5561 0.0 0 +M V30 15 C -5.2643 4.5561 0.0 0 +M V30 16 C 1.2157 5.3115 0.0 0 +M V30 17 C 2.5023 4.5797 0.0 0 +M V30 18 C 1.2039 6.8224 0.0 0 +M V30 19 N 2.4905 3.0925 0.0 0 +M V30 20 C 3.7889 5.3351 0.0 0 +M V30 21 C 2.4905 7.5778 0.0 0 +M V30 22 C 3.6944 2.219 0.0 0 +M V30 23 C 1.2629 2.219 0.0 0 +M V30 24 C 3.7771 6.846 0.0 0 +M V30 25 C 2.4787 9.0886 0.0 0 +M V30 26 C 3.2223 0.8026 0.0 0 +M V30 27 C 1.7115 0.8026 0.0 0 +M V30 28 F 2.4669 10.5995 0.0 0 +M V30 29 F 3.9659 9.1004 0.0 0 +M V30 30 F 0.9678 9.1004 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 4 6 +M V30 6 1 7 6 CFG=1 +M V30 7 1 7 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 1 8 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 14 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 1 19 22 +M V30 22 1 19 23 +M V30 23 2 20 24 +M V30 24 1 21 25 +M V30 25 1 22 26 +M V30 26 1 23 27 +M V30 27 1 25 28 +M V30 28 1 25 29 +M V30 29 1 25 30 +M V30 30 1 12 15 +M V30 31 1 21 24 +M V30 32 1 26 27 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 7) +M V30 END COLLECTION +M V30 END CTAB +M END +> +554 + +> +Z411102092 + +> +448.503 + +> +3.271 + +> +2 + +> +81.750 + +> +5 + +> +ATM + +> + + +> + + +$$$$ +Compound 555 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.296 -0.7491 0.0 0 +M V30 3 C 2.5921 0.0237 0.0 0 +M V30 4 C 1.2841 -2.2473 0.0 0 +M V30 5 C 3.8882 -0.7253 0.0 0 +M V30 6 C 2.5802 -2.9845 0.0 0 +M V30 7 C 3.8763 -2.2235 0.0 0 +M V30 8 S 5.1724 -2.9607 0.0 0 +M V30 9 O 4.3995 -4.2568 0.0 0 +M V30 10 O 5.9096 -1.6409 0.0 0 +M V30 11 C 6.4685 -3.6979 0.0 0 +M V30 12 C 7.7645 -2.9369 0.0 0 +M V30 13 N 9.0606 -3.6742 0.0 0 +M V30 14 C 10.3567 -2.9132 0.0 0 +M V30 15 O 10.3448 -1.3912 0.0 0 +M V30 16 C 11.6528 -3.6504 0.0 0 +M V30 17 N 11.6409 -5.1486 0.0 0 +M V30 18 C 12.9489 -2.8894 0.0 0 +M V30 19 C 12.937 -5.8977 0.0 0 +M V30 20 C 14.2449 -3.6266 0.0 0 +M V30 21 O 12.9251 -7.3959 0.0 0 +M V30 22 C 14.233 -5.1248 0.0 0 +M V30 23 C 15.541 -2.8656 0.0 0 +M V30 24 C 15.5291 -5.8739 0.0 0 +M V30 25 C 16.8371 -3.6028 0.0 0 +M V30 26 C 16.8252 -5.101 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 2 8 9 +M V30 9 2 8 10 +M V30 10 1 8 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 1 13 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 2 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 1 19 22 +M V30 22 1 20 23 +M V30 23 1 22 24 +M V30 24 2 23 25 +M V30 25 2 24 26 +M V30 26 1 6 7 +M V30 27 2 20 22 +M V30 28 1 25 26 +M V30 END BOND +M V30 END CTAB +M END +> +555 + +> +Z811206498 + +> +390.841 + +> +1.796 + +> +2 + +> +92.340 + +> +5 + +> +parp3 + +> + + +> + + +$$$$ +Compound 556 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -1.3066 0.7534 0.0 0 +M V30 3 F -2.06 -0.5297 0.0 0 +M V30 4 F -0.5768 2.06 0.0 0 +M V30 5 C -2.6133 1.5068 0.0 0 +M V30 6 C -3.92 0.7651 0.0 0 +M V30 7 C -2.6251 3.0136 0.0 0 +M V30 8 C -5.2267 1.5185 0.0 0 +M V30 9 C -3.9318 3.767 0.0 0 +M V30 10 N -6.5333 0.7769 0.0 0 +M V30 11 C -5.2384 3.0253 0.0 0 +M V30 12 C -7.84 1.5303 0.0 0 +M V30 13 O -7.8518 3.0371 0.0 0 +M V30 14 N -9.1467 0.7887 0.0 0 +M V30 15 C -10.4534 1.5421 0.0 0 +M V30 16 C -11.7601 0.8004 0.0 0 +M V30 17 O -11.7718 -0.6827 0.0 0 +M V30 18 N -13.0667 1.5538 0.0 0 +M V30 19 C -14.3734 0.8122 0.0 0 +M V30 20 C -14.3852 -0.6709 0.0 0 +M V30 21 C -15.6801 1.5656 0.0 0 +M V30 22 O -13.1021 -1.4008 0.0 0 +M V30 23 C -15.6919 -1.4008 0.0 0 +M V30 24 C -16.9868 0.824 0.0 0 +M V30 25 C -13.1138 -2.8841 0.0 0 +M V30 26 C -16.9985 -0.6474 0.0 0 +M V30 27 C -18.2934 1.5774 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 2 5 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 2 8 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 18 19 +M V30 19 2 19 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 20 23 +M V30 23 2 21 24 +M V30 24 1 22 25 +M V30 25 2 23 26 +M V30 26 1 24 27 +M V30 27 1 9 11 +M V30 28 1 24 26 +M V30 END BOND +M V30 END CTAB +M END +> +556 + +> +Z409670212 + +> +381.349 + +> +3.421 + +> +3 + +> +79.460 + +> +6 + +> +ATM + +> + + +> + + +$$$$ +Compound 557 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 31 34 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3135 0.7573 0.0 0 +M V30 3 C 1.2898 0.7573 0.0 0 +M V30 4 N -2.627 0.0236 0.0 0 +M V30 5 N -1.3253 2.272 0.0 0 +M V30 6 C 2.5797 0.0236 0.0 0 +M V30 7 C -3.9406 0.781 0.0 0 +M V30 8 C -2.6389 3.0294 0.0 0 +M V30 9 O 2.5679 -1.4673 0.0 0 +M V30 10 N 3.8696 0.781 0.0 0 +M V30 11 N -5.3843 0.3313 0.0 0 +M V30 12 C -3.9524 2.2957 0.0 0 +M V30 13 O -2.6507 4.5441 0.0 0 +M V30 14 C 5.1594 0.0473 0.0 0 +M V30 15 N -6.2836 1.562 0.0 0 +M V30 16 C -5.8576 -1.0886 0.0 0 +M V30 17 C -5.3961 2.769 0.0 0 +M V30 18 C 5.1476 -1.4437 0.0 0 +M V30 19 C 6.4493 0.8046 0.0 0 +M V30 20 C -4.8636 -2.1892 0.0 0 +M V30 21 C -7.3368 -1.3845 0.0 0 +M V30 22 C 6.4375 -2.1773 0.0 0 +M V30 23 C 7.7392 0.071 0.0 0 +M V30 24 C -5.3369 -3.6092 0.0 0 +M V30 25 C -7.8102 -2.8045 0.0 0 +M V30 26 C 7.7273 -1.42 0.0 0 +M V30 27 C -6.8161 -3.9051 0.0 0 +M V30 28 O 9.0172 -2.1537 0.0 0 +M V30 29 C 10.3071 -1.3963 0.0 0 +M V30 30 F 11.5969 -2.13 0.0 0 +M V30 31 F 10.2952 0.1183 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 7 12 +M V30 12 2 8 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 11 16 +M V30 16 1 12 17 +M V30 17 2 14 18 +M V30 18 1 14 19 +M V30 19 2 16 20 +M V30 20 1 16 21 +M V30 21 1 18 22 +M V30 22 2 19 23 +M V30 23 1 20 24 +M V30 24 2 21 25 +M V30 25 2 22 26 +M V30 26 2 24 27 +M V30 27 1 26 28 +M V30 28 1 28 29 +M V30 29 1 29 30 +M V30 30 1 29 31 +M V30 31 1 8 12 +M V30 32 2 15 17 +M V30 33 1 23 26 +M V30 34 1 25 27 +M V30 END BOND +M V30 END CTAB +M END +> +557 + +> +Z15383125 + +> +443.427 + +> +2.969 + +> +2 + +> +97.610 + +> +7 + +> +parp1 + +> + + +> + + +$$$$ +Compound 558 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4874 0.0 0 +M V30 3 C -1.3221 -2.2193 0.0 0 +M V30 4 C 1.2749 -2.2193 0.0 0 +M V30 5 N -2.6325 -1.4638 0.0 0 +M V30 6 C -1.3339 -3.7068 0.0 0 +M V30 7 C 1.2631 -3.7068 0.0 0 +M V30 8 C -3.9429 -2.1957 0.0 0 +M V30 9 C -0.0472 -4.4387 0.0 0 +M V30 10 O -3.9547 -3.6831 0.0 0 +M V30 11 C -5.2532 -1.4402 0.0 0 +M V30 12 S -6.5636 -2.1721 0.0 0 +M V30 13 C -7.874 -1.4166 0.0 0 +M V30 14 N -9.1843 -2.1485 0.0 0 +M V30 15 N -7.8858 0.0944 0.0 0 +M V30 16 C -10.4947 -1.393 0.0 0 +M V30 17 C -9.1961 0.8499 0.0 0 +M V30 18 N -11.9349 -1.8415 0.0 0 +M V30 19 C -10.5065 0.118 0.0 0 +M V30 20 O -9.2079 2.361 0.0 0 +M V30 21 N -12.8321 -0.6138 0.0 0 +M V30 22 C -12.4071 -3.2582 0.0 0 +M V30 23 C -11.9467 0.5902 0.0 0 +M V30 24 C -11.4155 -4.356 0.0 0 +M V30 25 C -13.8828 -3.5533 0.0 0 +M V30 26 C -11.8877 -5.7727 0.0 0 +M V30 27 C -14.355 -4.9699 0.0 0 +M V30 28 C -13.3633 -6.0678 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 2 8 10 +M V30 10 1 8 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 16 19 +M V30 19 2 17 20 +M V30 20 1 18 21 +M V30 21 1 18 22 +M V30 22 1 19 23 +M V30 23 2 22 24 +M V30 24 1 22 25 +M V30 25 1 24 26 +M V30 26 2 25 27 +M V30 27 2 26 28 +M V30 28 1 7 9 +M V30 29 1 17 19 +M V30 30 2 21 23 +M V30 31 1 27 28 +M V30 END BOND +M V30 END CTAB +M END +> +558 + +> +Z15383610 + +> +411.865 + +> +2.568 + +> +2 + +> +88.380 + +> +5 + +> +parp10 + +> + + +> + + +$$$$ +Compound 559 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 29 32 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.3127 0.7687 0.0 0 +M V30 3 C -1.3245 2.2825 0.0 0 +M V30 4 C -2.6254 0.0236 0.0 0 +M V30 5 C -2.6373 3.0394 0.0 0 +M V30 6 C -0.0354 3.0394 0.0 0 +M V30 7 C -3.9382 0.7923 0.0 0 +M V30 8 C -3.95 2.3061 0.0 0 +M V30 9 N 1.2536 2.3061 0.0 0 +M V30 10 C 2.5427 3.063 0.0 0 +M V30 11 O 2.5308 4.5768 0.0 0 +M V30 12 C 3.8317 2.3298 0.0 0 +M V30 13 S 5.1208 3.0867 0.0 0 +M V30 14 C 6.4099 2.3534 0.0 0 +M V30 15 N 7.699 3.1103 0.0 0 +M V30 16 N 6.3981 0.8633 0.0 0 +M V30 17 C 8.9881 2.3771 0.0 0 +M V30 18 C 7.6872 0.1182 0.0 0 +M V30 19 N 10.4073 2.8501 0.0 0 +M V30 20 C 8.9763 0.8869 0.0 0 +M V30 21 O 7.6754 -1.3718 0.0 0 +M V30 22 N 11.2825 1.6438 0.0 0 +M V30 23 C 10.8567 4.293 0.0 0 +M V30 24 C 10.3955 0.4375 0.0 0 +M V30 25 C 9.8396 5.4165 0.0 0 +M V30 26 C 12.3114 4.6123 0.0 0 +M V30 27 C 10.289 6.8593 0.0 0 +M V30 28 C 12.7608 6.0551 0.0 0 +M V30 29 C 11.7437 7.1787 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 1 9 10 +M V30 10 2 10 11 +M V30 11 1 10 12 +M V30 12 1 12 13 +M V30 13 1 13 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 17 20 +M V30 20 2 18 21 +M V30 21 1 19 22 +M V30 22 1 19 23 +M V30 23 1 20 24 +M V30 24 2 23 25 +M V30 25 1 23 26 +M V30 26 1 25 27 +M V30 27 2 26 28 +M V30 28 2 27 29 +M V30 29 1 7 8 +M V30 30 1 18 20 +M V30 31 2 22 24 +M V30 32 1 28 29 +M V30 END BOND +M V30 END CTAB +M END +> +559 + +> +Z15383633 + +> +425.891 + +> +3.060 + +> +2 + +> +88.380 + +> +6 + +> +parp14, parp10 + +> + + +> + + +$$$$ +Compound 560 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 30 33 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.309 -0.7311 0.0 0 +M V30 3 C -1.3208 -2.2171 0.0 0 +M V30 4 C -2.618 0.0235 0.0 0 +M V30 5 O -0.0353 -2.9482 0.0 0 +M V30 6 C -2.6298 -2.9482 0.0 0 +M V30 7 C -3.9271 -0.7075 0.0 0 +M V30 8 C 1.25 -2.1935 0.0 0 +M V30 9 C -3.9388 -2.1935 0.0 0 +M V30 10 N -5.2361 0.0471 0.0 0 +M V30 11 C -6.5451 -0.684 0.0 0 +M V30 12 O -6.5569 -2.1699 0.0 0 +M V30 13 C -7.8542 0.0707 0.0 0 +M V30 14 S -9.1632 -0.6604 0.0 0 +M V30 15 C -10.4722 0.0943 0.0 0 +M V30 16 N -11.7813 -0.6368 0.0 0 +M V30 17 N -10.484 1.6038 0.0 0 +M V30 18 C -13.0903 0.1179 0.0 0 +M V30 19 C -11.7931 2.3586 0.0 0 +M V30 20 N -14.5291 -0.3302 0.0 0 +M V30 21 C -13.1021 1.6274 0.0 0 +M V30 22 O -11.8049 3.8681 0.0 0 +M V30 23 N -15.4253 0.8962 0.0 0 +M V30 24 C -15.0008 -1.7453 0.0 0 +M V30 25 C -14.5409 2.0991 0.0 0 +M V30 26 C -14.0102 -2.8421 0.0 0 +M V30 27 C -16.4749 -2.0402 0.0 0 +M V30 28 C -14.4819 -4.2573 0.0 0 +M V30 29 C -16.9466 -3.4553 0.0 0 +M V30 30 C -15.956 -4.5521 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 1 10 11 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 2 18 21 +M V30 21 2 19 22 +M V30 22 1 20 23 +M V30 23 1 20 24 +M V30 24 1 21 25 +M V30 25 2 24 26 +M V30 26 1 24 27 +M V30 27 1 26 28 +M V30 28 2 27 29 +M V30 29 2 28 30 +M V30 30 1 7 9 +M V30 31 1 19 21 +M V30 32 2 23 25 +M V30 33 1 29 30 +M V30 END BOND +M V30 END CTAB +M END +> +560 + +> +Z15383645 + +> +441.891 + +> +3.204 + +> +2 + +> +97.610 + +> +6 + +> +parp1 + +> + + +> + + +$$$$ +Compound 561 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3793 -0.6012 0.0 0 +M V30 3 C 0.9903 -1.0964 0.0 0 +M V30 4 N -2.6879 0.1532 0.0 0 +M V30 5 C -1.2378 -2.0749 0.0 0 +M V30 6 C 0.2357 -2.3814 0.0 0 +M V30 7 C -2.6997 1.6623 0.0 0 +M V30 8 C -2.3578 -3.0652 0.0 0 +M V30 9 O -1.4147 2.4168 0.0 0 +M V30 10 C -4.0083 2.4168 0.0 0 +M V30 11 N -3.4778 -4.0555 0.0 0 +M V30 12 S -4.0201 3.9258 0.0 0 +M V30 13 C -5.3288 4.6803 0.0 0 +M V30 14 N -6.6374 3.9494 0.0 0 +M V30 15 N -5.3405 6.1894 0.0 0 +M V30 16 C -7.946 4.7039 0.0 0 +M V30 17 C -6.6492 6.9439 0.0 0 +M V30 18 N -9.3843 4.2559 0.0 0 +M V30 19 C -7.9578 6.213 0.0 0 +M V30 20 O -6.661 8.4529 0.0 0 +M V30 21 N -10.2803 5.482 0.0 0 +M V30 22 C -9.8559 2.8412 0.0 0 +M V30 23 C -9.3961 6.6845 0.0 0 +M V30 24 C -8.8656 1.7448 0.0 0 +M V30 25 C -11.3296 2.5465 0.0 0 +M V30 26 C -9.3372 0.3301 0.0 0 +M V30 27 C -11.8011 1.1317 0.0 0 +M V30 28 C -10.8108 0.0353 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 3 8 11 +M V30 11 1 10 12 +M V30 12 1 12 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 16 19 +M V30 19 2 17 20 +M V30 20 1 18 21 +M V30 21 1 18 22 +M V30 22 1 19 23 +M V30 23 2 22 24 +M V30 24 1 22 25 +M V30 25 1 24 26 +M V30 26 2 25 27 +M V30 27 2 26 28 +M V30 28 1 5 6 +M V30 29 1 17 19 +M V30 30 2 21 23 +M V30 31 1 27 28 +M V30 END BOND +M V30 END CTAB +M END +> +561 + +> +Z15383243 + +> +408.457 + +> +1.518 + +> +2 + +> +112.170 + +> +5 + +> +parp10 + +> + + +> + + +$$$$ +Compound 562 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 29 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 0.4839 -1.4046 0.0 0 +M V30 3 C -1.4872 0.295 0.0 0 +M V30 4 N 1.9476 -1.6761 0.0 0 +M V30 5 N -0.5075 -2.526 0.0 0 +M V30 6 C -1.9948 1.7233 0.0 0 +M V30 7 C 2.4316 -3.0808 0.0 0 +M V30 8 C -0.0236 -3.9307 0.0 0 +M V30 9 O -1.0269 2.8683 0.0 0 +M V30 10 N -3.4821 2.0184 0.0 0 +M V30 11 N 3.8008 -3.6474 0.0 0 +M V30 12 C 1.44 -4.2022 0.0 0 +M V30 13 O -1.0151 -5.052 0.0 0 +M V30 14 C -3.9897 3.4467 0.0 0 +M V30 15 C -4.4736 0.8971 0.0 0 +M V30 16 N 3.6592 -5.1347 0.0 0 +M V30 17 C 5.0756 -2.8565 0.0 0 +M V30 18 C 2.2073 -5.477 0.0 0 +M V30 19 C -5.477 3.7418 0.0 0 +M V30 20 C -5.9609 1.1921 0.0 0 +M V30 21 C 5.0402 -1.3456 0.0 0 +M V30 22 C 6.3741 -3.5647 0.0 0 +M V30 23 O -5.9846 5.1701 0.0 0 +M V30 24 O -6.9525 0.0708 0.0 0 +M V30 25 C 6.3151 -0.5547 0.0 0 +M V30 26 C 7.6489 -2.7739 0.0 0 +M V30 27 C -7.4719 5.4652 0.0 0 +M V30 28 C -8.4398 0.3659 0.0 0 +M V30 29 C 7.6135 -1.263 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 7 12 +M V30 12 2 8 13 +M V30 13 1 10 14 +M V30 14 1 10 15 +M V30 15 1 11 16 +M V30 16 1 11 17 +M V30 17 1 12 18 +M V30 18 1 14 19 +M V30 19 1 15 20 +M V30 20 2 17 21 +M V30 21 1 17 22 +M V30 22 1 19 23 +M V30 23 1 20 24 +M V30 24 1 21 25 +M V30 25 2 22 26 +M V30 26 1 23 27 +M V30 27 1 24 28 +M V30 28 2 25 29 +M V30 29 1 8 12 +M V30 30 2 16 18 +M V30 31 1 26 29 +M V30 END BOND +M V30 END CTAB +M END +> +562 + +> +Z15383268 + +> +417.482 + +> +2.017 + +> +1 + +> +98.050 + +> +10 + +> +parp2 + +> + + +> + + +$$$$ +Compound 563 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O 1.3546 -0.6007 0.0 0 +M V30 3 O -0.4711 -1.4135 0.0 0 +M V30 4 C -1.4724 0.318 0.0 0 +M V30 5 C 0.7303 1.3075 0.0 0 +M V30 6 C -1.6373 1.814 0.0 0 CFG=2 +M V30 7 C -0.2827 2.4266 0.0 0 +M V30 8 N -2.9449 2.5679 0.0 0 +M V30 9 C -2.9567 4.0758 0.0 0 +M V30 10 O -1.6727 4.8297 0.0 0 +M V30 11 C -4.2642 4.8297 0.0 0 +M V30 12 S -4.276 6.3375 0.0 0 +M V30 13 C -5.5836 7.0914 0.0 0 +M V30 14 N -6.8911 6.361 0.0 0 +M V30 15 N -5.5954 8.5992 0.0 0 +M V30 16 C -8.1987 7.1149 0.0 0 +M V30 17 C -6.9029 9.3531 0.0 0 +M V30 18 N -9.6358 6.6673 0.0 0 +M V30 19 C -8.2105 8.6228 0.0 0 +M V30 20 O -6.9147 10.8609 0.0 0 +M V30 21 N -10.5311 7.8924 0.0 0 +M V30 22 C -10.107 5.2537 0.0 0 +M V30 23 C -9.6476 9.0939 0.0 0 +M V30 24 C -9.1175 4.1582 0.0 0 +M V30 25 C -11.5795 4.9592 0.0 0 +M V30 26 C -9.5887 2.7446 0.0 0 +M V30 27 C -12.0507 3.5457 0.0 0 +M V30 28 C -11.0612 2.4501 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 6 8 CFG=1 +M V30 8 1 8 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 16 19 +M V30 19 2 17 20 +M V30 20 1 18 21 +M V30 21 1 18 22 +M V30 22 1 19 23 +M V30 23 2 22 24 +M V30 24 1 22 25 +M V30 25 1 24 26 +M V30 26 2 25 27 +M V30 27 2 26 28 +M V30 28 1 6 7 +M V30 29 1 17 19 +M V30 30 2 21 23 +M V30 31 1 27 28 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 6) +M V30 END COLLECTION +M V30 END CTAB +M END +> +563 + +> +Z15383278 + +> +419.478 + +> +-0.498 + +> +2 + +> +122.520 + +> +5 + +> +parp1, parp2 + +> + + +> + + +$$$$ +Compound 564 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.3081 -0.7306 0.0 0 +M V30 3 C -2.6163 0.0235 0.0 0 +M V30 4 C -1.3199 -2.2156 0.0 0 +M V30 5 C -3.9245 -0.7071 0.0 0 +M V30 6 C -2.6281 -2.9463 0.0 0 +M V30 7 N -5.2327 0.0471 0.0 0 +M V30 8 C -3.9363 -2.192 0.0 0 +M V30 9 C -6.5408 -0.6835 0.0 0 +M V30 10 O -6.5526 -2.1685 0.0 0 +M V30 11 C -7.849 0.0707 0.0 0 +M V30 12 S -9.1572 -0.6599 0.0 0 +M V30 13 C -10.4654 0.0942 0.0 0 +M V30 14 N -11.7735 -0.6364 0.0 0 +M V30 15 N -10.4772 1.6028 0.0 0 +M V30 16 C -13.0817 0.1178 0.0 0 +M V30 17 C -11.7853 2.357 0.0 0 +M V30 18 N -14.5195 -0.3299 0.0 0 +M V30 19 C -13.0935 1.6263 0.0 0 +M V30 20 O -11.7971 3.8656 0.0 0 +M V30 21 N -15.4152 0.8956 0.0 0 +M V30 22 C -14.991 -1.7442 0.0 0 +M V30 23 C -14.5313 2.0977 0.0 0 +M V30 24 C -14.001 -2.8402 0.0 0 +M V30 25 C -16.4641 -2.0388 0.0 0 +M V30 26 C -14.4724 -4.2545 0.0 0 +M V30 27 C -16.9355 -3.4531 0.0 0 +M V30 28 C -15.9456 -4.5491 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 7 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 16 19 +M V30 19 2 17 20 +M V30 20 1 18 21 +M V30 21 1 18 22 +M V30 22 1 19 23 +M V30 23 2 22 24 +M V30 24 1 22 25 +M V30 25 1 24 26 +M V30 26 2 25 27 +M V30 27 2 26 28 +M V30 28 1 6 8 +M V30 29 1 17 19 +M V30 30 2 21 23 +M V30 31 1 27 28 +M V30 END BOND +M V30 END CTAB +M END +> +564 + +> +Z15383718 + +> +411.865 + +> +3.418 + +> +2 + +> +88.380 + +> +5 + +> +parp10 + +> + + +> + + +$$$$ +Compound 565 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3125 0.7567 0.0 0 +M V30 3 C 1.2888 0.7567 0.0 0 +M V30 4 N -2.625 0.0236 0.0 0 +M V30 5 N -1.3243 2.2702 0.0 0 +M V30 6 C 2.5777 0.0236 0.0 0 +M V30 7 C -3.9375 0.7804 0.0 0 +M V30 8 C -2.6368 3.027 0.0 0 +M V30 9 O 2.5658 -1.4662 0.0 0 +M V30 10 N 3.8665 0.7804 0.0 0 +M V30 11 N -5.3801 0.331 0.0 0 +M V30 12 C -3.9493 2.2939 0.0 0 +M V30 13 O -2.6486 4.5405 0.0 0 +M V30 14 C 5.1554 0.0472 0.0 0 +M V30 15 N -6.2787 1.5608 0.0 0 +M V30 16 C -5.853 -1.0878 0.0 0 +M V30 17 C -5.3919 2.7669 0.0 0 +M V30 18 C 6.4442 0.804 0.0 0 +M V30 19 C 5.1436 -1.4425 0.0 0 +M V30 20 C -4.8598 -2.1875 0.0 0 +M V30 21 C -7.3311 -1.3834 0.0 0 +M V30 22 C 7.7331 0.0709 0.0 0 +M V30 23 C 6.4324 2.3175 0.0 0 +M V30 24 C 6.4324 -2.1756 0.0 0 +M V30 25 C -5.3328 -3.6064 0.0 0 +M V30 26 C -7.8041 -2.8023 0.0 0 +M V30 27 C 7.7213 -1.4189 0.0 0 +M V30 28 C -6.8108 -3.902 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 7 12 +M V30 12 2 8 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 11 16 +M V30 16 1 12 17 +M V30 17 1 14 18 +M V30 18 1 14 19 +M V30 19 2 16 20 +M V30 20 1 16 21 +M V30 21 1 18 22 +M V30 22 1 18 23 +M V30 23 1 19 24 +M V30 24 1 20 25 +M V30 25 2 21 26 +M V30 26 1 22 27 +M V30 27 2 25 28 +M V30 28 1 8 12 +M V30 29 2 15 17 +M V30 30 1 24 27 +M V30 31 1 26 28 +M V30 END BOND +M V30 END CTAB +M END +> +565 + +> +Z15383306 + +> +397.494 + +> +3.016 + +> +2 + +> +88.380 + +> +5 + +> +parp14 + +> + + +> + + +$$$$ +Compound 566 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3125 0.7567 0.0 0 +M V30 3 C 1.2888 0.7567 0.0 0 +M V30 4 N -2.625 0.0236 0.0 0 +M V30 5 N -1.3243 2.2702 0.0 0 +M V30 6 C 2.5777 0.0236 0.0 0 +M V30 7 C -3.9375 0.7804 0.0 0 +M V30 8 C -2.6368 3.027 0.0 0 +M V30 9 O 2.5658 -1.4662 0.0 0 +M V30 10 N 3.8665 0.7804 0.0 0 +M V30 11 N -5.3801 0.331 0.0 0 +M V30 12 C -3.9493 2.2939 0.0 0 +M V30 13 O -2.6486 4.5405 0.0 0 +M V30 14 C 5.1554 0.0472 0.0 0 +M V30 15 N -6.2787 1.5608 0.0 0 +M V30 16 C -5.853 -1.0878 0.0 0 +M V30 17 C -5.3919 2.7669 0.0 0 +M V30 18 C 6.4442 0.804 0.0 0 +M V30 19 C 5.1436 -1.4425 0.0 0 +M V30 20 C -4.8598 -2.1875 0.0 0 +M V30 21 C -7.3311 -1.3834 0.0 0 +M V30 22 F 6.4324 2.3175 0.0 0 +M V30 23 C 7.7331 0.0709 0.0 0 +M V30 24 C 6.4324 -2.1756 0.0 0 +M V30 25 C -5.3328 -3.6064 0.0 0 +M V30 26 C -7.8041 -2.8023 0.0 0 +M V30 27 C 7.7213 -1.4189 0.0 0 +M V30 28 C -6.8108 -3.902 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 7 12 +M V30 12 2 8 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 11 16 +M V30 16 1 12 17 +M V30 17 2 14 18 +M V30 18 1 14 19 +M V30 19 2 16 20 +M V30 20 1 16 21 +M V30 21 1 18 22 +M V30 22 1 18 23 +M V30 23 2 19 24 +M V30 24 1 20 25 +M V30 25 2 21 26 +M V30 26 2 23 27 +M V30 27 2 25 28 +M V30 28 1 8 12 +M V30 29 2 15 17 +M V30 30 1 24 27 +M V30 31 1 26 28 +M V30 END BOND +M V30 END CTAB +M END +> +566 + +> +Z15383770 + +> +395.410 + +> +2.248 + +> +2 + +> +88.380 + +> +5 + +> +parp14 + +> + + +> + + +$$$$ +Compound 567 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3129 0.7569 0.0 0 +M V30 3 C 1.2892 0.7569 0.0 0 +M V30 4 N -2.6258 0.0236 0.0 0 +M V30 5 N -1.3247 2.2709 0.0 0 +M V30 6 C 2.5784 0.0236 0.0 0 +M V30 7 C -3.9387 0.7806 0.0 0 +M V30 8 C -2.6376 3.0279 0.0 0 +M V30 9 O 2.5666 -1.4666 0.0 0 +M V30 10 N 3.8677 0.7806 0.0 0 +M V30 11 N -5.3817 0.3311 0.0 0 +M V30 12 C -3.9505 2.2946 0.0 0 +M V30 13 O -2.6494 4.5419 0.0 0 +M V30 14 C 5.1569 0.0473 0.0 0 +M V30 15 N -6.2806 1.5612 0.0 0 +M V30 16 C -5.8548 -1.0881 0.0 0 +M V30 17 C -5.3935 2.7677 0.0 0 +M V30 18 C 6.4462 0.8043 0.0 0 +M V30 19 C 5.1451 -1.443 0.0 0 +M V30 20 C -4.8612 -2.1881 0.0 0 +M V30 21 C -7.3333 -1.3838 0.0 0 +M V30 22 C 7.7354 0.0709 0.0 0 +M V30 23 C 6.4344 -2.1763 0.0 0 +M V30 24 C -5.3344 -3.6075 0.0 0 +M V30 25 C -7.8064 -2.8032 0.0 0 +M V30 26 C 7.7236 -1.4193 0.0 0 +M V30 27 C -6.8129 -3.9032 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 7 12 +M V30 12 2 8 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 11 16 +M V30 16 1 12 17 +M V30 17 1 14 18 +M V30 18 1 14 19 +M V30 19 2 16 20 +M V30 20 1 16 21 +M V30 21 1 18 22 +M V30 22 1 19 23 +M V30 23 1 20 24 +M V30 24 2 21 25 +M V30 25 1 22 26 +M V30 26 2 24 27 +M V30 27 1 8 12 +M V30 28 2 15 17 +M V30 29 1 23 26 +M V30 30 1 25 27 +M V30 END BOND +M V30 END CTAB +M END +> +567 + +> +Z15383776 + +> +383.467 + +> +2.497 + +> +2 + +> +88.380 + +> +5 + +> +parp14 + +> + + +> + + +$$$$ +Compound 568 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 30 33 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3131 0.7571 0.0 0 +M V30 3 C 1.2894 0.7571 0.0 0 +M V30 4 N -2.6262 0.0236 0.0 0 +M V30 5 N -1.3249 2.2713 0.0 0 +M V30 6 C 2.5788 0.0236 0.0 0 +M V30 7 C -3.9393 0.7807 0.0 0 +M V30 8 C -2.638 3.0284 0.0 0 +M V30 9 O 2.567 -1.4668 0.0 0 +M V30 10 N 3.8683 0.7807 0.0 0 +M V30 11 N -5.3825 0.3312 0.0 0 +M V30 12 C -3.9511 2.2949 0.0 0 +M V30 13 O -2.6498 4.5426 0.0 0 +M V30 14 C 5.1577 0.0473 0.0 0 +M V30 15 N -6.2816 1.5615 0.0 0 +M V30 16 C -5.8557 -1.0883 0.0 0 +M V30 17 C -5.3943 2.7681 0.0 0 +M V30 18 C 6.4472 0.8044 0.0 0 +M V30 19 C 5.1459 -1.4432 0.0 0 +M V30 20 C -4.862 -2.1885 0.0 0 +M V30 21 C -7.3344 -1.384 0.0 0 +M V30 22 C 7.7366 0.0709 0.0 0 +M V30 23 C 6.4354 -2.1766 0.0 0 +M V30 24 C -5.3352 -3.608 0.0 0 +M V30 25 C -7.8076 -2.8036 0.0 0 +M V30 26 N 9.0261 0.828 0.0 0 CHG=1 +M V30 27 C 7.7248 -1.4195 0.0 0 +M V30 28 C -6.8139 -3.9038 0.0 0 +M V30 29 O 9.0143 2.3423 0.0 0 +M V30 30 O 10.3155 0.0946 0.0 0 CHG=-1 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 7 12 +M V30 12 2 8 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 11 16 +M V30 16 1 12 17 +M V30 17 2 14 18 +M V30 18 1 14 19 +M V30 19 2 16 20 +M V30 20 1 16 21 +M V30 21 1 18 22 +M V30 22 2 19 23 +M V30 23 1 20 24 +M V30 24 2 21 25 +M V30 25 1 22 26 +M V30 26 2 22 27 +M V30 27 2 24 28 +M V30 28 2 26 29 +M V30 29 1 26 30 +M V30 30 1 8 12 +M V30 31 2 15 17 +M V30 32 1 23 27 +M V30 33 1 25 28 +M V30 END BOND +M V30 END CTAB +M END +> +568 + +> +Z15383777 + +> +422.417 + +> +2.742 + +> +2 + +> +131.520 + +> +6 + +> +parp2 + +> + + +> + + +$$$$ +Compound 569 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 29 32 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3124 0.7567 0.0 0 +M V30 3 C 1.2888 0.7567 0.0 0 +M V30 4 N -2.6249 0.0118 0.0 0 +M V30 5 N -1.3242 2.2702 0.0 0 +M V30 6 C 2.5776 0.0118 0.0 0 +M V30 7 C -3.9374 0.7685 0.0 0 +M V30 8 C -2.6367 3.0269 0.0 0 +M V30 9 O 2.5658 -1.478 0.0 0 +M V30 10 N 3.8664 0.7685 0.0 0 +M V30 11 N -5.3799 0.3192 0.0 0 +M V30 12 C -3.9492 2.282 0.0 0 +M V30 13 O -2.6485 4.5404 0.0 0 +M V30 14 C 5.1553 0.0236 0.0 0 +M V30 15 N -6.2785 1.5489 0.0 0 +M V30 16 C -5.8529 -1.0996 0.0 0 +M V30 17 C -5.3917 2.755 0.0 0 +M V30 18 O 5.1434 -1.4661 0.0 0 +M V30 19 N 6.4441 0.7803 0.0 0 +M V30 20 C -4.8596 -2.1992 0.0 0 +M V30 21 C -7.3309 -1.3952 0.0 0 +M V30 22 C 7.7329 0.0354 0.0 0 +M V30 23 C -5.3326 -3.6181 0.0 0 +M V30 24 C -7.8038 -2.8141 0.0 0 +M V30 25 C 9.0927 0.6621 0.0 0 +M V30 26 C 7.8748 -1.4425 0.0 0 +M V30 27 C -6.8106 -3.9137 0.0 0 +M V30 28 C 10.0859 -0.4374 0.0 0 +M V30 29 C 9.3292 -1.7381 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 7 12 +M V30 12 2 8 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 11 16 +M V30 16 1 12 17 +M V30 17 2 14 18 +M V30 18 1 14 19 +M V30 19 2 16 20 +M V30 20 1 16 21 +M V30 21 1 19 22 +M V30 22 1 20 23 +M V30 23 2 21 24 +M V30 24 1 22 25 +M V30 25 1 22 26 +M V30 26 2 23 27 +M V30 27 1 25 28 +M V30 28 1 26 29 +M V30 29 1 8 12 +M V30 30 2 15 17 +M V30 31 1 24 27 +M V30 32 1 28 29 +M V30 END BOND +M V30 END CTAB +M END +> +569 + +> +Z15383363 + +> +412.466 + +> +2.244 + +> +3 + +> +117.480 + +> +5 + +> +parp14 + +> + + +> + + +$$$$ +Compound 570 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 32 36 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.508 0.0 0 +M V30 3 C -1.3077 -0.7304 0.0 0 CFG=2 +M V30 4 N -1.3195 2.262 0.0 0 +M V30 5 N 1.2723 2.262 0.0 0 +M V30 6 C -1.3195 -2.2149 0.0 0 +M V30 7 C -2.6154 0.0235 0.0 0 +M V30 8 C -1.3312 3.77 0.0 0 +M V30 9 C 1.2606 3.77 0.0 0 +M V30 10 O -0.0353 -2.9571 0.0 0 +M V30 11 N -2.6272 -2.9571 0.0 0 +M V30 12 N -2.4505 4.7832 0.0 0 +M V30 13 C -0.0471 4.5358 0.0 0 +M V30 14 O 2.5447 4.5358 0.0 0 +M V30 15 C -2.639 -4.4415 0.0 0 +M V30 16 N -1.8496 6.1616 0.0 0 +M V30 17 C -3.9232 4.4887 0.0 0 +M V30 18 C -0.3652 6.0085 0.0 0 +M V30 19 C -3.9467 -5.172 0.0 0 +M V30 20 C -4.3944 3.0749 0.0 0 +M V30 21 C -4.9364 5.6079 0.0 0 +M V30 22 C -5.2545 -4.418 0.0 0 +M V30 23 C -3.9585 -6.6564 0.0 0 +M V30 24 C -5.8671 2.7804 0.0 0 +M V30 25 C -6.409 5.3134 0.0 0 +M V30 26 C -6.5622 -5.1484 0.0 0 +M V30 27 C -5.2662 -7.3869 0.0 0 +M V30 28 C -6.8803 3.8996 0.0 0 +M V30 29 O -7.9995 -4.6772 0.0 0 +M V30 30 C -6.574 -6.6329 0.0 0 +M V30 31 C -8.8949 -5.8789 0.0 0 +M V30 32 O -8.0113 -7.0806 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 3 1 CFG=3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 3 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 1 8 12 +M V30 12 2 8 13 +M V30 13 2 9 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 1 12 17 +M V30 17 1 13 18 +M V30 18 1 15 19 +M V30 19 2 17 20 +M V30 20 1 17 21 +M V30 21 2 19 22 +M V30 22 1 19 23 +M V30 23 1 20 24 +M V30 24 2 21 25 +M V30 25 1 22 26 +M V30 26 2 23 27 +M V30 27 2 24 28 +M V30 28 1 26 29 +M V30 29 2 26 30 +M V30 30 1 29 31 +M V30 31 1 30 32 +M V30 32 1 9 13 +M V30 33 2 16 18 +M V30 34 1 25 28 +M V30 35 1 27 30 +M V30 36 1 31 32 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 3) +M V30 END COLLECTION +M V30 END CTAB +M END +> +570 + +> +Z15383380 + +> +449.482 + +> +2.621 + +> +2 + +> +106.840 + +> +6 + +> +parp3 + +> + + +> + + +$$$$ +Compound 571 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 30 33 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3137 0.7574 0.0 0 +M V30 3 C 1.29 0.7574 0.0 0 +M V30 4 N -2.6274 0.0236 0.0 0 +M V30 5 N -1.3255 2.2724 0.0 0 +M V30 6 C 2.5801 0.0236 0.0 0 +M V30 7 C -3.9412 0.7811 0.0 0 +M V30 8 C -2.6393 3.0298 0.0 0 +M V30 9 O 2.5682 -1.4675 0.0 0 +M V30 10 N 3.8701 0.7811 0.0 0 +M V30 11 N -5.3851 0.3313 0.0 0 +M V30 12 C -3.953 2.296 0.0 0 +M V30 13 O -2.6511 4.5448 0.0 0 +M V30 14 C 5.1602 0.0473 0.0 0 +M V30 15 N -6.2846 1.5622 0.0 0 +M V30 16 C -5.8585 -1.0888 0.0 0 +M V30 17 C -5.3969 2.7694 0.0 0 +M V30 18 C 5.1484 -1.4439 0.0 0 +M V30 19 C 6.4503 0.8048 0.0 0 +M V30 20 C -4.8643 -2.1895 0.0 0 +M V30 21 C -7.3379 -1.3847 0.0 0 +M V30 22 C 6.4384 -2.1777 0.0 0 +M V30 23 C 7.7403 0.071 0.0 0 +M V30 24 C -5.3377 -3.6098 0.0 0 +M V30 25 C -7.8114 -2.805 0.0 0 +M V30 26 C 7.7285 -1.4202 0.0 0 +M V30 27 C -6.8172 -3.9057 0.0 0 +M V30 28 N 9.0186 -2.154 0.0 0 CHG=1 +M V30 29 O 9.0067 -3.6453 0.0 0 +M V30 30 O 10.3086 -1.3965 0.0 0 CHG=-1 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 7 12 +M V30 12 2 8 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 11 16 +M V30 16 1 12 17 +M V30 17 2 14 18 +M V30 18 1 14 19 +M V30 19 2 16 20 +M V30 20 1 16 21 +M V30 21 1 18 22 +M V30 22 2 19 23 +M V30 23 1 20 24 +M V30 24 2 21 25 +M V30 25 2 22 26 +M V30 26 2 24 27 +M V30 27 1 26 28 +M V30 28 2 28 29 +M V30 29 1 28 30 +M V30 30 1 8 12 +M V30 31 2 15 17 +M V30 32 1 23 26 +M V30 33 1 25 27 +M V30 END BOND +M V30 END CTAB +M END +> +571 + +> +Z15383835 + +> +422.417 + +> +2.742 + +> +2 + +> +131.520 + +> +6 + +> +parp10 + +> + + +> + + +$$$$ +Compound 572 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 32 35 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3132 0.7571 0.0 0 +M V30 3 C 1.2895 0.7571 0.0 0 CFG=2 +M V30 4 N -2.6264 0.0236 0.0 0 +M V30 5 N -1.325 2.2715 0.0 0 +M V30 6 C 2.5791 0.0236 0.0 0 +M V30 7 C 1.2777 2.2715 0.0 0 +M V30 8 C -3.9397 0.7808 0.0 0 +M V30 9 C -2.6383 3.0287 0.0 0 +M V30 10 O 2.5673 -1.467 0.0 0 +M V30 11 N 3.8687 0.7808 0.0 0 +M V30 12 N -5.3831 0.3312 0.0 0 +M V30 13 C -3.9515 2.2952 0.0 0 +M V30 14 O -2.6501 4.5431 0.0 0 +M V30 15 C 5.1583 0.0473 0.0 0 +M V30 16 N -6.2822 1.5616 0.0 0 +M V30 17 C -5.8563 -1.0884 0.0 0 +M V30 18 C -5.3949 2.7684 0.0 0 +M V30 19 C 6.4479 0.8045 0.0 0 +M V30 20 C 5.1465 -1.4433 0.0 0 +M V30 21 C -4.8625 -2.1887 0.0 0 +M V30 22 C -7.3352 -1.3842 0.0 0 +M V30 23 C 7.7375 0.0709 0.0 0 +M V30 24 C 6.436 -2.1769 0.0 0 +M V30 25 C -5.3358 -3.6084 0.0 0 +M V30 26 C -7.8084 -2.8039 0.0 0 +M V30 27 C 7.7256 -1.4197 0.0 0 +M V30 28 C -6.8146 -3.9042 0.0 0 +M V30 29 N 9.0152 -2.1532 0.0 0 +M V30 30 C 9.0034 -3.6439 0.0 0 +M V30 31 O 10.293 -4.3893 0.0 0 +M V30 32 C 7.6901 -4.3893 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 3 1 CFG=1 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 3 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 1 8 12 +M V30 12 2 8 13 +M V30 13 2 9 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 1 12 17 +M V30 17 1 13 18 +M V30 18 2 15 19 +M V30 19 1 15 20 +M V30 20 2 17 21 +M V30 21 1 17 22 +M V30 22 1 19 23 +M V30 23 2 20 24 +M V30 24 1 21 25 +M V30 25 2 22 26 +M V30 26 2 23 27 +M V30 27 2 25 28 +M V30 28 1 27 29 +M V30 29 1 29 30 +M V30 30 2 30 31 +M V30 31 1 30 32 +M V30 32 1 9 13 +M V30 33 2 16 18 +M V30 34 1 24 27 +M V30 35 1 26 28 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 3) +M V30 END COLLECTION +M V30 END CTAB +M END +> +572 + +> +Z15383005 + +> +448.498 + +> +1.775 + +> +3 + +> +117.480 + +> +6 + +> +parp10 + +> + + +> + + +$$$$ +Compound 573 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 31 34 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3132 0.7571 0.0 0 +M V30 3 C -0.0118 -1.4906 0.0 0 CFG=2 +M V30 4 N -2.6264 0.0236 0.0 0 +M V30 5 N -1.325 2.2714 0.0 0 +M V30 6 C 1.2777 -2.2241 0.0 0 +M V30 7 C -1.325 -2.2241 0.0 0 +M V30 8 C -3.9396 0.7808 0.0 0 +M V30 9 C -2.6382 3.0286 0.0 0 +M V30 10 O 2.5672 -1.467 0.0 0 +M V30 11 N 1.2658 -3.7148 0.0 0 +M V30 12 N -5.3829 0.3312 0.0 0 +M V30 13 C -3.9514 2.2951 0.0 0 +M V30 14 O -2.65 4.5429 0.0 0 +M V30 15 C 2.5554 -4.4601 0.0 0 +M V30 16 N -6.282 1.5616 0.0 0 +M V30 17 C -5.8561 -1.0884 0.0 0 +M V30 18 C -5.3947 2.7683 0.0 0 +M V30 19 C 2.5435 -5.9508 0.0 0 +M V30 20 C 3.8449 -3.7029 0.0 0 +M V30 21 C -4.8624 -2.1886 0.0 0 +M V30 22 C -7.335 -1.3841 0.0 0 +M V30 23 C 3.8331 -6.6961 0.0 0 +M V30 24 C 5.1345 -4.4483 0.0 0 +M V30 25 C -5.3356 -3.6083 0.0 0 +M V30 26 C -7.8082 -2.8038 0.0 0 +M V30 27 N 3.8213 -8.1868 0.0 0 CHG=1 +M V30 28 C 5.1226 -5.9271 0.0 0 +M V30 29 C -6.8144 -3.9041 0.0 0 +M V30 30 O 5.1108 -8.9203 0.0 0 +M V30 31 O 2.508 -8.9203 0.0 0 CHG=-1 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 3 1 CFG=3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 3 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 1 8 12 +M V30 12 2 8 13 +M V30 13 2 9 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 1 12 17 +M V30 17 1 13 18 +M V30 18 2 15 19 +M V30 19 1 15 20 +M V30 20 2 17 21 +M V30 21 1 17 22 +M V30 22 1 19 23 +M V30 23 2 20 24 +M V30 24 1 21 25 +M V30 25 2 22 26 +M V30 26 1 23 27 +M V30 27 2 23 28 +M V30 28 2 25 29 +M V30 29 2 27 30 +M V30 30 1 27 31 +M V30 31 1 9 13 +M V30 32 2 16 18 +M V30 33 1 24 28 +M V30 34 1 26 29 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 3) +M V30 END COLLECTION +M V30 END CTAB +M END +> +573 + +> +Z15383009 + +> +436.444 + +> +3.051 + +> +2 + +> +131.520 + +> +6 + +> +parp10 + +> + + +> + + +$$$$ +Compound 574 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 31 34 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3132 0.7571 0.0 0 +M V30 3 C -0.0118 -1.4906 0.0 0 CFG=2 +M V30 4 N -2.6264 0.0236 0.0 0 +M V30 5 N -1.325 2.2714 0.0 0 +M V30 6 C 1.2777 -2.2241 0.0 0 +M V30 7 C -1.325 -2.2241 0.0 0 +M V30 8 C -3.9396 0.7808 0.0 0 +M V30 9 C -2.6382 3.0286 0.0 0 +M V30 10 O 2.5672 -1.467 0.0 0 +M V30 11 N 1.2658 -3.7148 0.0 0 +M V30 12 N -5.3829 0.3312 0.0 0 +M V30 13 C -3.9514 2.2951 0.0 0 +M V30 14 O -2.65 4.5429 0.0 0 +M V30 15 C 2.5554 -4.4601 0.0 0 +M V30 16 N -6.282 1.5616 0.0 0 +M V30 17 C -5.8561 -1.0884 0.0 0 +M V30 18 C -5.3947 2.7683 0.0 0 +M V30 19 C 2.5435 -5.9508 0.0 0 +M V30 20 C 3.8449 -3.7029 0.0 0 +M V30 21 C -4.8624 -2.1886 0.0 0 +M V30 22 C -7.335 -1.3841 0.0 0 +M V30 23 C 3.8331 -6.6961 0.0 0 +M V30 24 C 5.1345 -4.4483 0.0 0 +M V30 25 C -5.3356 -3.6083 0.0 0 +M V30 26 C -7.8082 -2.8038 0.0 0 +M V30 27 C 3.8213 -8.1868 0.0 0 +M V30 28 C 5.1226 -5.9271 0.0 0 +M V30 29 C -6.8144 -3.9041 0.0 0 +M V30 30 O 5.1108 -8.9203 0.0 0 +M V30 31 C 2.508 -8.9203 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 3 1 CFG=3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 3 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 1 8 12 +M V30 12 2 8 13 +M V30 13 2 9 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 1 12 17 +M V30 17 1 13 18 +M V30 18 2 15 19 +M V30 19 1 15 20 +M V30 20 2 17 21 +M V30 21 1 17 22 +M V30 22 1 19 23 +M V30 23 2 20 24 +M V30 24 1 21 25 +M V30 25 2 22 26 +M V30 26 1 23 27 +M V30 27 2 23 28 +M V30 28 2 25 29 +M V30 29 2 27 30 +M V30 30 1 27 31 +M V30 31 1 9 13 +M V30 32 2 16 18 +M V30 33 1 24 28 +M V30 34 1 26 29 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 3) +M V30 END COLLECTION +M V30 END CTAB +M END +> +574 + +> +Z15383014 + +> +433.483 + +> +2.665 + +> +2 + +> +105.450 + +> +6 + +> +parp14, parp2 + +> + + +> + + +$$$$ +Compound 575 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 29 32 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3129 0.757 0.0 0 +M V30 3 C 1.2893 0.757 0.0 0 CFG=2 +M V30 4 N -2.6259 0.0236 0.0 0 +M V30 5 N -1.3248 2.2711 0.0 0 +M V30 6 C 2.5786 0.0236 0.0 0 +M V30 7 C 1.2774 2.2711 0.0 0 +M V30 8 C -3.9389 0.7806 0.0 0 +M V30 9 C -2.6377 3.0281 0.0 0 +M V30 10 O 2.5668 -1.4667 0.0 0 +M V30 11 N 3.8679 0.7806 0.0 0 +M V30 12 N -5.382 0.3312 0.0 0 +M V30 13 C -3.9507 2.2947 0.0 0 +M V30 14 O -2.6496 4.5422 0.0 0 +M V30 15 C 5.1572 0.0473 0.0 0 +M V30 16 N -6.281 1.5613 0.0 0 +M V30 17 C -5.8551 -1.0882 0.0 0 +M V30 18 C -5.3938 2.7679 0.0 0 +M V30 19 C 6.4466 0.8043 0.0 0 +M V30 20 C 5.1454 -1.443 0.0 0 +M V30 21 C -4.8615 -2.1882 0.0 0 +M V30 22 C -7.3337 -1.3839 0.0 0 +M V30 23 C 7.7359 0.0709 0.0 0 +M V30 24 C 6.4347 -2.1764 0.0 0 +M V30 25 C -5.3347 -3.6077 0.0 0 +M V30 26 C -7.8069 -2.8033 0.0 0 +M V30 27 C 7.7241 -1.4194 0.0 0 +M V30 28 C -6.8133 -3.9034 0.0 0 +M V30 29 F 9.0134 -2.1528 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 3 1 CFG=1 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 3 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 1 8 12 +M V30 12 2 8 13 +M V30 13 2 9 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 1 12 17 +M V30 17 1 13 18 +M V30 18 2 15 19 +M V30 19 1 15 20 +M V30 20 2 17 21 +M V30 21 1 17 22 +M V30 22 1 19 23 +M V30 23 2 20 24 +M V30 24 1 21 25 +M V30 25 2 22 26 +M V30 26 2 23 27 +M V30 27 2 25 28 +M V30 28 1 27 29 +M V30 29 1 9 13 +M V30 30 2 16 18 +M V30 31 1 24 27 +M V30 32 1 26 28 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 3) +M V30 END COLLECTION +M V30 END CTAB +M END +> +575 + +> +Z15383459 + +> +409.437 + +> +3.157 + +> +2 + +> +88.380 + +> +5 + +> +parp10 + +> + + +> + + +$$$$ +Compound 576 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 32 35 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3074 0.7538 0.0 0 +M V30 3 C 1.2838 0.7538 0.0 0 CFG=2 +M V30 4 N -2.6148 0.0235 0.0 0 +M V30 5 N -1.3191 2.2614 0.0 0 +M V30 6 C 1.272 2.2614 0.0 0 +M V30 7 C 2.5677 0.0235 0.0 0 +M V30 8 C -3.9222 0.7773 0.0 0 +M V30 9 C -2.6266 3.0152 0.0 0 +M V30 10 O 2.5559 3.0152 0.0 0 +M V30 11 N -0.0353 3.0152 0.0 0 +M V30 12 N -5.3592 0.3297 0.0 0 +M V30 13 C -3.934 2.285 0.0 0 +M V30 14 O -2.6383 4.5229 0.0 0 +M V30 15 C -0.0471 4.5229 0.0 0 +M V30 16 N -6.2543 1.5547 0.0 0 +M V30 17 C -5.8303 -1.0836 0.0 0 +M V30 18 C -5.3709 2.7561 0.0 0 +M V30 19 C 1.2367 5.2767 0.0 0 +M V30 20 C -1.3545 5.2767 0.0 0 +M V30 21 C -4.8409 -2.179 0.0 0 +M V30 22 C -7.3026 -1.378 0.0 0 +M V30 23 C 1.2249 6.7844 0.0 0 +M V30 24 C -1.3663 6.7844 0.0 0 +M V30 25 C -5.3121 -3.5924 0.0 0 +M V30 26 C -7.7738 -2.7915 0.0 0 +M V30 27 O 2.5088 7.5382 0.0 0 +M V30 28 C -0.0824 7.5382 0.0 0 +M V30 29 O -2.6737 7.5382 0.0 0 +M V30 30 C -6.7844 -3.8869 0.0 0 +M V30 31 C 2.497 9.0458 0.0 0 +M V30 32 C -2.6854 9.0458 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 3 1 CFG=3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 3 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 1 8 12 +M V30 12 2 8 13 +M V30 13 2 9 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 1 12 17 +M V30 17 1 13 18 +M V30 18 2 15 19 +M V30 19 1 15 20 +M V30 20 2 17 21 +M V30 21 1 17 22 +M V30 22 1 19 23 +M V30 23 2 20 24 +M V30 24 1 21 25 +M V30 25 2 22 26 +M V30 26 1 23 27 +M V30 27 2 23 28 +M V30 28 1 24 29 +M V30 29 2 25 30 +M V30 30 1 27 31 +M V30 31 1 29 32 +M V30 32 1 9 13 +M V30 33 2 16 18 +M V30 34 1 24 28 +M V30 35 1 26 30 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 3) +M V30 END COLLECTION +M V30 END CTAB +M END +> +576 + +> +Z15383478 + +> +451.498 + +> +2.857 + +> +2 + +> +106.840 + +> +7 + +> +parp14, parp2 + +> + + +> + + +$$$$ +Compound 577 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.2865 -0.7436 0.0 0 +M V30 3 C -1.3101 -0.7436 0.0 0 +M V30 4 N 2.5731 0.0236 0.0 0 +M V30 5 N 1.2747 -2.2308 0.0 0 +M V30 6 C -2.6203 0.0236 0.0 0 +M V30 7 C 3.8596 -0.7199 0.0 0 +M V30 8 C 2.5613 -2.9626 0.0 0 +M V30 9 O -2.6321 1.5344 0.0 0 +M V30 10 N -3.9304 -0.7199 0.0 0 +M V30 11 N 5.276 -0.2478 0.0 0 +M V30 12 C 3.8478 -2.2072 0.0 0 +M V30 13 O 2.5495 -4.4498 0.0 0 +M V30 14 C -5.2406 0.0472 0.0 0 +M V30 15 N 6.1494 -1.4518 0.0 0 +M V30 16 C 5.7245 1.1921 0.0 0 +M V30 17 C 5.2642 -2.6557 0.0 0 +M V30 18 O -5.2524 1.558 0.0 0 +M V30 19 N -6.5508 -0.6963 0.0 0 +M V30 20 C 4.7094 2.3134 0.0 0 +M V30 21 C 7.1763 1.5108 0.0 0 +M V30 22 C -7.8609 0.0708 0.0 0 +M V30 23 C 5.158 3.7534 0.0 0 +M V30 24 C 7.6249 2.9508 0.0 0 +M V30 25 C -9.1711 -0.6727 0.0 0 +M V30 26 C -7.8727 1.5816 0.0 0 +M V30 27 C 6.6098 4.0721 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 7 12 +M V30 12 2 8 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 11 16 +M V30 16 1 12 17 +M V30 17 2 14 18 +M V30 18 1 14 19 +M V30 19 2 16 20 +M V30 20 1 16 21 +M V30 21 1 19 22 +M V30 22 1 20 23 +M V30 23 2 21 24 +M V30 24 1 22 25 +M V30 25 1 22 26 +M V30 26 2 23 27 +M V30 27 1 8 12 +M V30 28 2 15 17 +M V30 29 1 24 27 +M V30 END BOND +M V30 END CTAB +M END +> +577 + +> +Z15383066 + +> +386.428 + +> +1.610 + +> +3 + +> +117.480 + +> +5 + +> +parp10 + +> + + +> + + +$$$$ +Compound 578 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.2881 -0.7327 0.0 0 +M V30 3 C -1.3118 -0.7327 0.0 0 +M V30 4 N 2.5763 0.0236 0.0 0 +M V30 5 N 1.2763 -2.2218 0.0 0 +M V30 6 C -2.6236 0.0236 0.0 0 +M V30 7 C 3.8645 -0.709 0.0 0 +M V30 8 C 2.5645 -2.9545 0.0 0 +M V30 9 O -2.6354 1.5363 0.0 0 +M V30 10 N -3.9354 -0.709 0.0 0 +M V30 11 N 5.2826 -0.2363 0.0 0 +M V30 12 C 3.8527 -2.1981 0.0 0 +M V30 13 O 2.5527 -4.4436 0.0 0 +M V30 14 C -5.2472 0.0472 0.0 0 +M V30 15 N 6.1572 -1.4418 0.0 0 +M V30 16 C 5.7317 1.2054 0.0 0 +M V30 17 C 5.2708 -2.6472 0.0 0 +M V30 18 O -5.259 1.5599 0.0 0 +M V30 19 N -6.559 -0.6854 0.0 0 +M V30 20 C 4.7154 2.3281 0.0 0 +M V30 21 C 7.1854 1.5245 0.0 0 +M V30 22 C -7.8708 0.0709 0.0 0 +M V30 23 C 5.1645 3.7699 0.0 0 +M V30 24 C 7.6344 2.9663 0.0 0 +M V30 25 C -9.1826 -0.6618 0.0 0 +M V30 26 C 6.6181 4.089 0.0 0 +M V30 27 O -10.4944 0.0945 0.0 0 +M V30 28 C -11.8062 -0.6381 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 7 12 +M V30 12 2 8 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 11 16 +M V30 16 1 12 17 +M V30 17 2 14 18 +M V30 18 1 14 19 +M V30 19 2 16 20 +M V30 20 1 16 21 +M V30 21 1 19 22 +M V30 22 1 20 23 +M V30 23 2 21 24 +M V30 24 1 22 25 +M V30 25 2 23 26 +M V30 26 1 25 27 +M V30 27 1 27 28 +M V30 28 1 8 12 +M V30 29 2 15 17 +M V30 30 1 24 26 +M V30 END BOND +M V30 END CTAB +M END +> +578 + +> +Z15383495 + +> +402.428 + +> +1.091 + +> +3 + +> +126.710 + +> +7 + +> +parp2 + +> + + +> + + +$$$$ +Compound 579 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 34 38 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O 1.3548 -0.6008 0.0 0 +M V30 3 O -0.4712 -1.4137 0.0 0 +M V30 4 C -1.4726 0.318 0.0 0 +M V30 5 C 0.7304 1.3077 0.0 0 +M V30 6 C -1.6376 1.8143 0.0 0 CFG=2 +M V30 7 C -0.2827 2.4269 0.0 0 +M V30 8 N -2.9453 2.5683 0.0 0 +M V30 9 C -4.2531 1.8261 0.0 0 +M V30 10 C -2.9571 4.0763 0.0 0 +M V30 11 C -4.2648 0.3416 0.0 0 +M V30 12 C -5.5608 2.5801 0.0 0 +M V30 13 O -1.6729 4.8304 0.0 0 +M V30 14 C -4.2648 4.8304 0.0 0 +M V30 15 C -5.5726 -0.4005 0.0 0 +M V30 16 C -6.8686 1.8379 0.0 0 +M V30 17 S -4.2766 6.3384 0.0 0 +M V30 18 C -6.8803 0.3652 0.0 0 +M V30 19 C -5.5844 7.0924 0.0 0 +M V30 20 N -6.8921 6.3619 0.0 0 +M V30 21 N -5.5962 8.6004 0.0 0 +M V30 22 C -8.1999 7.116 0.0 0 +M V30 23 C -6.9039 9.3544 0.0 0 +M V30 24 N -9.6372 6.6683 0.0 0 +M V30 25 C -8.2116 8.624 0.0 0 +M V30 26 O -6.9157 10.8625 0.0 0 +M V30 27 N -10.5326 7.8935 0.0 0 +M V30 28 C -10.1085 5.2545 0.0 0 +M V30 29 C -9.649 9.0953 0.0 0 +M V30 30 C -9.1188 4.1588 0.0 0 +M V30 31 C -11.5811 4.96 0.0 0 +M V30 32 C -9.5901 2.745 0.0 0 +M V30 33 C -12.0524 3.5462 0.0 0 +M V30 34 C -11.0628 2.4505 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 6 8 CFG=1 +M V30 8 1 8 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 2 12 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 1 17 19 +M V30 19 2 19 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 2 22 25 +M V30 25 2 23 26 +M V30 26 1 24 27 +M V30 27 1 24 28 +M V30 28 1 25 29 +M V30 29 2 28 30 +M V30 30 1 28 31 +M V30 31 1 30 32 +M V30 32 2 31 33 +M V30 33 2 32 34 +M V30 34 1 6 7 +M V30 35 1 16 18 +M V30 36 1 23 25 +M V30 37 2 27 29 +M V30 38 1 33 34 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 6) +M V30 END COLLECTION +M V30 END CTAB +M END +> +579 + +> +Z15383081 + +> +495.574 + +> +1.473 + +> +1 + +> +113.730 + +> +6 + +> +parp2 + +> + + +> + + +$$$$ +Compound 580 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5102 0.0 0 +M V30 3 N -1.3214 2.2771 0.0 0 +M V30 4 C 1.2742 2.2771 0.0 0 +M V30 5 C -1.3332 3.7873 0.0 0 +M V30 6 C 1.2624 3.7873 0.0 0 +M V30 7 C 2.5603 1.5338 0.0 0 +M V30 8 N -0.0471 4.5543 0.0 0 +M V30 9 N -2.6429 4.5543 0.0 0 +M V30 10 C 2.5485 4.5543 0.0 0 +M V30 11 C 3.8463 2.3007 0.0 0 +M V30 12 C -4.0233 3.9525 0.0 0 +M V30 13 C -2.808 6.0527 0.0 0 +M V30 14 C 3.8345 3.8109 0.0 0 +M V30 15 C -4.3419 2.5013 0.0 0 +M V30 16 C -5.038 5.0734 0.0 0 +M V30 17 C -4.2829 6.3713 0.0 0 +M V30 18 C -3.2446 1.5102 0.0 0 +M V30 19 C -5.7813 2.0529 0.0 0 +M V30 20 O -4.9082 7.7517 0.0 0 +M V30 21 C -3.5632 0.0589 0.0 0 +M V30 22 C -6.0999 0.6017 0.0 0 +M V30 23 O -2.4659 -0.932 0.0 0 +M V30 24 C -5.0026 -0.3893 0.0 0 +M V30 25 C -2.7844 -2.3833 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 12 15 +M V30 15 1 12 16 +M V30 16 1 13 17 +M V30 17 2 15 18 +M V30 18 1 15 19 +M V30 19 1 17 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 1 21 23 +M V30 23 2 21 24 +M V30 24 1 23 25 +M V30 25 1 6 8 +M V30 26 1 11 14 +M V30 27 1 16 17 +M V30 28 1 22 24 +M V30 END BOND +M V30 END CTAB +M END +> +580 + +> +Z2000211190 + +> +337.372 + +> +1.384 + +> +2 + +> +74.160 + +> +3 + +> +parp15 + +> + + +> + + +$$$$ +Compound 581 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 N -0.0118 -1.4878 0.0 0 CHG=1 +M V30 3 O 1.2752 -2.2199 0.0 0 CHG=-1 +M V30 4 C -1.3225 -2.2199 0.0 0 +M V30 5 C -2.6332 -1.4524 0.0 0 +M V30 6 C -1.3343 -3.7077 0.0 0 +M V30 7 C -3.9439 -2.1845 0.0 0 +M V30 8 C -2.645 -4.4516 0.0 0 +M V30 9 C -3.9557 -3.6723 0.0 0 +M V30 10 C -5.3845 -1.7121 0.0 0 +M V30 11 N -5.3963 -4.121 0.0 0 +M V30 12 C -6.2819 -2.9166 0.0 0 +M V30 13 C -7.7933 -2.9048 0.0 0 +M V30 14 O -8.549 -1.594 0.0 0 +M V30 15 N -8.549 -4.1918 0.0 0 +M V30 16 C -10.0605 -4.18 0.0 0 +M V30 17 C -7.8169 -5.4789 0.0 0 +M V30 18 C -10.828 -5.4671 0.0 0 +M V30 19 C -8.5727 -6.766 0.0 0 +M V30 20 N -10.0841 -6.7542 0.0 0 +M V30 21 C -10.8516 -8.0413 0.0 0 +M V30 22 C -10.1077 -9.3284 0.0 0 +M V30 23 N -10.8752 -10.6155 0.0 0 +M V30 24 C -10.1313 -11.9026 0.0 0 +M V30 25 C -12.3867 -10.6037 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 1 12 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 20 21 +M V30 21 1 21 22 +M V30 22 1 22 23 +M V30 23 1 23 24 +M V30 24 1 23 25 +M V30 25 1 8 9 +M V30 26 1 11 12 +M V30 27 1 19 20 +M V30 END BOND +M V30 END CTAB +M END +> +581 + +> +Z777828898 + +> +345.396 + +> +2.012 + +> +1 + +> +85.720 + +> +5 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 582 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.5087 0.0117 0.0 0 +M V30 3 C -2.2748 1.3201 0.0 0 +M V30 4 C -2.2748 -1.2729 0.0 0 +M V30 5 Cl -1.5323 2.6284 0.0 0 +M V30 6 C -3.7836 1.3319 0.0 0 +M V30 7 S -1.5323 -2.5577 0.0 0 +M V30 8 C -3.7836 -1.2612 0.0 0 +M V30 9 C -4.5379 0.0471 0.0 0 +M V30 10 O -0.2475 -1.8034 0.0 0 +M V30 11 O -0.8015 -3.8425 0.0 0 +M V30 12 N -2.8406 -3.3003 0.0 0 +M V30 13 C -2.8524 -4.7855 0.0 0 +M V30 14 C -4.149 -2.5459 0.0 0 +M V30 15 C -4.1608 -5.528 0.0 0 +M V30 16 C -5.4573 -3.2885 0.0 0 +M V30 17 N -4.1725 -7.0132 0.0 0 +M V30 18 N -5.4691 -4.7619 0.0 0 +M V30 19 C -6.7657 -2.5341 0.0 0 +M V30 20 C -5.4809 -7.744 0.0 0 +M V30 21 C -6.7775 -5.5045 0.0 0 +M V30 22 C -6.7892 -6.9896 0.0 0 +M V30 23 C -5.7991 -9.1938 0.0 0 +M V30 24 O -8.0858 -4.7383 0.0 0 +M V30 25 S -7.909 -7.9797 0.0 0 +M V30 26 C -7.2961 -9.3352 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 4 8 +M V30 8 2 6 9 +M V30 9 2 7 10 +M V30 10 2 7 11 +M V30 11 1 7 12 +M V30 12 1 12 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 1 17 20 +M V30 20 1 18 21 +M V30 21 2 20 22 +M V30 22 1 20 23 +M V30 23 2 21 24 +M V30 24 1 22 25 +M V30 25 2 23 26 +M V30 26 1 8 9 +M V30 27 1 21 22 +M V30 28 1 25 26 +M V30 END BOND +M V30 END CTAB +M END +> +582 + +> +Z223339442 + +> +432.345 + +> +3.151 + +> +1 + +> +78.840 + +> +5 + +> +parp10 + +> + + +> + + +$$$$ +Compound 583 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.506 0.0117 0.0 0 +M V30 3 C 0.4471 1.4354 0.0 0 +M V30 4 N -1.9766 1.4472 0.0 0 +M V30 5 C -2.4002 -1.1883 0.0 0 +M V30 6 C -0.7765 2.3296 0.0 0 +M V30 7 C -1.8001 -2.5414 0.0 0 +M V30 8 C -3.8945 -1.0236 0.0 0 +M V30 9 C -0.7883 3.8356 0.0 0 +M V30 10 C -2.6943 -3.7415 0.0 0 +M V30 11 C -4.7887 -2.2237 0.0 0 +M V30 12 O 0.4941 4.5886 0.0 0 +M V30 13 N -2.0943 4.5886 0.0 0 +M V30 14 C -4.1886 -3.5768 0.0 0 +M V30 15 C -2.106 6.0947 0.0 0 +M V30 16 C -3.4003 3.8474 0.0 0 +M V30 17 O -5.0828 -4.7769 0.0 0 +M V30 18 C -3.4121 6.8477 0.0 0 +M V30 19 C -0.8236 6.8477 0.0 0 +M V30 20 C -4.7063 4.6004 0.0 0 +M V30 21 C -6.5771 -4.6122 0.0 0 +M V30 22 C -3.4238 8.3537 0.0 0 +M V30 23 C -0.8353 8.3537 0.0 0 +M V30 24 C -2.1413 9.1067 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 1 15 19 +M V30 19 1 16 20 +M V30 20 1 17 21 +M V30 21 1 18 22 +M V30 22 2 19 23 +M V30 23 2 22 24 +M V30 24 1 4 6 +M V30 25 1 11 14 +M V30 26 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +583 + +> +Z99077692 + +> +338.423 + +> +3.746 + +> +0 + +> +42.430 + +> +5 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL2037093 + +> +0.88 + +$$$$ +Compound 584 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 34 37 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 0.8785 -1.2109 0.0 0 +M V30 3 C 2.3625 -1.0447 0.0 0 +M V30 4 C 0.2493 -2.5762 0.0 0 +M V30 5 C 3.241 -2.2557 0.0 0 +M V30 6 C 1.1278 -3.7872 0.0 0 +M V30 7 C 4.7251 -2.0894 0.0 0 +M V30 8 C 2.6118 -3.6209 0.0 0 +M V30 9 N 5.473 -0.7716 0.0 0 +M V30 10 C 5.7223 -3.1935 0.0 0 +M V30 11 N 6.9333 -1.0684 0.0 0 +M V30 12 C 7.0876 -2.5643 0.0 0 +M V30 13 C 8.3817 -3.3123 0.0 0 +M V30 14 C 9.6757 -2.5406 0.0 0 +M V30 15 C 10.9698 -3.2885 0.0 0 +M V30 16 C 12.2638 -2.5168 0.0 0 +M V30 17 C 13.5579 -3.2648 0.0 0 +M V30 18 N 14.852 -2.4931 0.0 0 +M V30 19 C 16.146 -3.241 0.0 0 +M V30 20 C 14.8401 -0.9735 0.0 0 +M V30 21 O 16.1342 -4.7369 0.0 0 +M V30 22 N 17.4401 -2.4693 0.0 0 +M V30 23 C 18.7341 -3.2173 0.0 0 +M V30 24 C 20.0282 -2.4456 0.0 0 +M V30 25 C 18.7223 -4.7132 0.0 0 +M V30 26 C 21.3223 -3.1935 0.0 0 +M V30 27 C 20.0163 -5.4611 0.0 0 +M V30 28 N 21.3104 -4.6894 0.0 0 +M V30 29 C 22.6045 -5.4374 0.0 0 +M V30 30 C 23.8985 -4.6776 0.0 0 +M V30 31 C 24.041 -3.1698 0.0 0 +M V30 32 C 25.2638 -5.283 0.0 0 +M V30 33 C 25.5012 -2.8493 0.0 0 +M V30 34 C 26.2611 -4.1552 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 1 12 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 1 18 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 1 19 22 +M V30 22 1 22 23 +M V30 23 1 23 24 +M V30 24 1 23 25 +M V30 25 1 24 26 +M V30 26 1 25 27 +M V30 27 1 26 28 +M V30 28 1 28 29 +M V30 29 1 29 30 +M V30 30 1 30 31 +M V30 31 1 30 32 +M V30 32 1 31 33 +M V30 33 1 32 34 +M V30 34 1 6 8 +M V30 35 1 11 12 +M V30 36 1 27 28 +M V30 37 1 33 34 +M V30 END BOND +M V30 END CTAB +M END +> +584 + +> +Z915257034 + +> +469.638 + +> +4.856 + +> +2 + +> +64.260 + +> +10 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 585 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 31 35 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4915 0.0 0 +M V30 3 N 1.2784 -2.2373 0.0 0 +M V30 4 C -1.3258 -2.2373 0.0 0 +M V30 5 C 1.2666 -3.7289 0.0 0 +M V30 6 C 2.4386 -1.2903 0.0 0 +M V30 7 C -1.3376 -3.7289 0.0 0 +M V30 8 C -2.6398 -1.4679 0.0 0 +M V30 9 N -0.0473 -4.4747 0.0 0 +M V30 10 C 2.4267 -4.6523 0.0 0 +M V30 11 C 3.8946 -1.6099 0.0 0 +M V30 12 C -2.6516 -4.4747 0.0 0 +M V30 13 C -3.9538 -2.2136 0.0 0 +M V30 14 C 3.8828 -4.309 0.0 0 +M V30 15 C 4.5339 -2.9476 0.0 0 +M V30 16 C -3.9657 -3.7052 0.0 0 +M V30 17 C -5.2797 -4.451 0.0 0 +M V30 18 O -6.5937 -3.6815 0.0 0 +M V30 19 N -5.2915 -5.9426 0.0 0 +M V30 20 C -6.6055 -6.6765 0.0 0 +M V30 21 C -4.0012 -6.6765 0.0 0 +M V30 22 C -6.6173 -8.1681 0.0 0 +M V30 23 C -4.013 -8.1681 0.0 0 +M V30 24 N -5.327 -8.9139 0.0 0 +M V30 25 C -5.3389 -10.4055 0.0 0 +M V30 26 O -4.0485 -11.1394 0.0 0 +M V30 27 C -6.6529 -11.1394 0.0 0 +M V30 28 O -8.0379 -10.512 0.0 0 +M V30 29 C -6.8186 -12.6192 0.0 0 +M V30 30 C -9.056 -11.613 0.0 0 +M V30 31 C -8.2983 -12.9151 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 5 10 +M V30 10 1 6 11 +M V30 11 1 7 12 +M V30 12 2 8 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 2 12 16 +M V30 16 1 16 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 24 25 +M V30 25 2 25 26 +M V30 26 1 25 27 +M V30 27 1 27 28 +M V30 28 2 27 29 +M V30 29 1 28 30 +M V30 30 1 29 31 +M V30 31 1 7 9 +M V30 32 1 13 16 +M V30 33 1 14 15 +M V30 34 1 23 24 +M V30 35 2 30 31 +M V30 END BOND +M V30 END CTAB +M END +> +585 + +> +Z30621241 + +> +420.461 + +> +1.468 + +> +0 + +> +86.430 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3105398 + +> +0.9 + +$$$$ +Compound 586 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.2284 -0.8741 0.0 0 +M V30 3 C -0.4724 1.4411 0.0 0 +M V30 4 C -2.4569 0.0236 0.0 0 +M V30 5 C -1.2402 -2.3624 0.0 0 CFG=1 +M V30 6 C -1.9844 1.4529 0.0 0 +M V30 7 N -2.5514 -3.1066 0.0 0 +M V30 8 C 0.0472 -3.1066 0.0 0 +M V30 9 C -3.8626 -2.3388 0.0 0 +M V30 10 C 1.3347 -2.3388 0.0 0 +M V30 11 O -3.8744 -0.8268 0.0 0 +M V30 12 N -5.1738 -3.083 0.0 0 +M V30 13 O 1.3229 -0.8268 0.0 0 +M V30 14 N 2.6223 -3.083 0.0 0 +M V30 15 C 3.9098 -2.3152 0.0 0 +M V30 16 C 5.1974 -3.0594 0.0 0 +M V30 17 C 3.898 -0.8032 0.0 0 +M V30 18 C 6.4849 -2.2915 0.0 0 +M V30 19 C 5.1856 -0.0472 0.0 0 +M V30 20 N 7.7725 -3.0357 0.0 0 +M V30 21 C 6.4731 -0.7796 0.0 0 +M V30 22 C 7.9142 -4.5123 0.0 0 +M V30 23 C 9.1309 -2.4097 0.0 0 +M V30 24 N 9.3671 -4.8076 0.0 0 +M V30 25 C 10.1231 -3.5082 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 5 7 CFG=3 +M V30 7 1 5 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 17 19 +M V30 19 1 18 20 +M V30 20 2 18 21 +M V30 21 1 20 22 +M V30 22 1 20 23 +M V30 23 2 22 24 +M V30 24 2 23 25 +M V30 25 1 4 6 +M V30 26 1 19 21 +M V30 27 1 24 25 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 5) +M V30 END COLLECTION +M V30 END CTAB +M END +> +586 + +> +Z769573510 + +> +355.414 + +> +1.673 + +> +3 + +> +102.040 + +> +6 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 587 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2876 0.7678 0.0 0 +M V30 3 N 1.2758 2.28 0.0 0 +M V30 4 C 2.5753 0.0236 0.0 0 +M V30 5 N 2.5635 3.0361 0.0 0 +M V30 6 C -0.0354 3.0361 0.0 0 +M V30 7 C 3.863 0.7915 0.0 0 +M V30 8 C 3.8512 2.3036 0.0 0 +M V30 9 C -1.3467 2.3036 0.0 0 +M V30 10 C 5.1389 3.0597 0.0 0 +M V30 11 C -2.658 3.0597 0.0 0 +M V30 12 C -1.3585 0.8151 0.0 0 +M V30 13 C 5.1271 4.5719 0.0 0 +M V30 14 C 6.4266 2.3273 0.0 0 +M V30 15 O -2.6699 4.5719 0.0 0 +M V30 16 C -3.9694 2.3273 0.0 0 +M V30 17 C -2.6699 0.0708 0.0 0 +M V30 18 C 6.4148 5.3398 0.0 0 +M V30 19 C 7.7143 3.0833 0.0 0 +M V30 20 C -1.3822 5.3398 0.0 0 +M V30 21 C -3.9812 0.8387 0.0 0 +M V30 22 C -2.6817 -1.4176 0.0 0 +M V30 23 C 7.7025 4.5955 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 11 16 +M V30 16 2 12 17 +M V30 17 1 13 18 +M V30 18 2 14 19 +M V30 19 1 15 20 +M V30 20 2 16 21 +M V30 21 1 17 22 +M V30 22 2 18 23 +M V30 23 1 7 8 +M V30 24 1 17 21 +M V30 25 1 19 23 +M V30 END BOND +M V30 END CTAB +M END +> +587 + +> +Z803257506 + +> +306.358 + +> +3.511 + +> +0 + +> +41.900 + +> +4 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1982993 + +> +0.88 + +$$$$ +Compound 588 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.5157 0.0 0 +M V30 3 C -1.3262 2.2735 0.0 0 +M V30 4 C 1.2788 2.2735 0.0 0 +M V30 5 N -2.6406 1.5394 0.0 0 +M V30 6 C -1.3381 3.7893 0.0 0 +M V30 7 C 1.267 3.7893 0.0 0 +M V30 8 C -2.6525 0.0473 0.0 0 +M V30 9 C -0.0473 4.5471 0.0 0 +M V30 10 O -1.3617 -0.6868 0.0 0 +M V30 11 C -3.9669 -0.6868 0.0 0 +M V30 12 N -3.9787 -2.1788 0.0 0 +M V30 13 C -5.2931 -2.913 0.0 0 +M V30 14 C -2.688 -2.913 0.0 0 +M V30 15 C -5.305 -4.405 0.0 0 +M V30 16 C -2.6998 -4.405 0.0 0 +M V30 17 N -4.0143 -5.1392 0.0 0 +M V30 18 C -4.0261 -6.6312 0.0 0 +M V30 19 O -5.3405 -7.3654 0.0 0 +M V30 20 C -2.7354 -7.3654 0.0 0 CFG=1 +M V30 21 N -2.7472 -8.8575 0.0 0 +M V30 22 C -1.4446 -6.6076 0.0 0 +M V30 23 C -3.9787 -9.7337 0.0 0 +M V30 24 C -1.5394 -9.7337 0.0 0 +M V30 25 N -3.5287 -11.1547 0.0 0 +M V30 26 C -2.013 -11.1547 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 2 8 10 +M V30 10 1 8 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 17 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 20 21 CFG=3 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 21 24 +M V30 24 2 23 25 +M V30 25 2 24 26 +M V30 26 1 7 9 +M V30 27 1 16 17 +M V30 28 1 25 26 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 20) +M V30 END COLLECTION +M V30 END CTAB +M END +> +588 + +> +Z415229776 + +> +375.853 + +> +1.503 + +> +1 + +> +70.470 + +> +5 + +> +ATM + +> + + +> + + +$$$$ +Compound 589 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 29 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.4709 -1.4129 0.0 0 +M V30 3 C -1.2245 0.8948 0.0 0 +M V30 4 N -1.978 -1.4011 0.0 0 +M V30 5 C 0.4003 -2.6138 0.0 0 +M V30 6 C -2.449 0.0235 0.0 0 +M V30 7 C -1.2362 2.4019 0.0 0 +M V30 8 N -0.2237 -3.9679 0.0 0 +M V30 9 C 1.8721 -2.449 0.0 0 +M V30 10 C -3.8855 0.4945 0.0 0 +M V30 11 O 0.047 3.1555 0.0 0 +M V30 12 N -2.5432 3.1555 0.0 0 +M V30 13 C 0.6475 -5.1689 0.0 0 +M V30 14 C 2.7434 -3.65 0.0 0 +M V30 15 C -2.555 4.6626 0.0 0 CFG=2 +M V30 16 C 2.1193 -5.004 0.0 0 +M V30 17 C -3.8619 5.4161 0.0 0 +M V30 18 C -1.2716 5.4161 0.0 0 +M V30 19 C -3.8737 6.9232 0.0 0 +M V30 20 C -5.1689 4.6861 0.0 0 +M V30 21 C -5.1806 7.6768 0.0 0 +M V30 22 C -6.4758 5.4397 0.0 0 +M V30 23 O -5.1924 9.1839 0.0 0 +M V30 24 C -6.4876 6.9468 0.0 0 +M V30 25 O -7.7827 4.7097 0.0 0 +M V30 26 C -6.4993 9.9492 0.0 0 +M V30 27 O -7.7945 7.7003 0.0 0 +M V30 28 C -9.0897 5.4632 0.0 0 +M V30 29 C -7.8063 9.2074 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 7 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 15 12 CFG=3 +M V30 15 2 13 16 +M V30 16 1 15 17 +M V30 17 1 15 18 +M V30 18 2 17 19 +M V30 19 1 17 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 1 21 23 +M V30 23 2 21 24 +M V30 24 1 22 25 +M V30 25 1 23 26 +M V30 26 1 24 27 +M V30 27 1 25 28 +M V30 28 1 27 29 +M V30 29 1 4 6 +M V30 30 1 14 16 +M V30 31 1 22 24 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 15) +M V30 END COLLECTION +M V30 END CTAB +M END +> +589 + +> +Z424414502 + +> +413.490 + +> +2.594 + +> +1 + +> +82.570 + +> +7 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1978567 + +> +0.85 + +$$$$ +Compound 590 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3119 0.7564 0.0 0 +M V30 3 C 1.2883 0.7564 0.0 0 CFG=2 +M V30 4 N -2.6948 0.1536 0.0 0 +M V30 5 N -1.4774 2.2575 0.0 0 +M V30 6 C 2.5766 0.0236 0.0 0 +M V30 7 C 1.2765 2.2693 0.0 0 +M V30 8 C -3.7113 1.2765 0.0 0 +M V30 9 C -2.9548 2.5766 0.0 0 +M V30 10 C -0.3782 3.2739 0.0 0 +M V30 11 N 3.8649 0.78 0.0 0 +M V30 12 N 2.5648 -1.4656 0.0 0 +M V30 13 C -5.2242 1.2883 0.0 0 +M V30 14 C -3.7231 3.8886 0.0 0 +M V30 15 C 5.1532 0.0472 0.0 0 +M V30 16 C 3.8531 -2.2102 0.0 0 +M V30 17 C -5.9806 2.6002 0.0 0 +M V30 18 C -5.236 3.9004 0.0 0 +M V30 19 C 5.1414 -1.4419 0.0 0 +M V30 20 C 6.4416 0.8037 0.0 0 +M V30 21 O 3.8413 -3.6995 0.0 0 +M V30 22 C 6.4298 -2.1866 0.0 0 +M V30 23 C 7.7299 0.0709 0.0 0 +M V30 24 C 7.7181 -1.4301 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 3 1 CFG=1 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 3 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 1 5 10 +M V30 10 2 6 11 +M V30 11 1 6 12 +M V30 12 1 8 13 +M V30 13 1 9 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 2 13 17 +M V30 17 2 14 18 +M V30 18 2 15 19 +M V30 19 1 15 20 +M V30 20 2 16 21 +M V30 21 1 19 22 +M V30 22 2 20 23 +M V30 23 2 22 24 +M V30 24 2 8 9 +M V30 25 1 16 19 +M V30 26 1 17 18 +M V30 27 1 23 24 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 3) +M V30 END COLLECTION +M V30 END CTAB +M END +> +590 + +> +Z15479257 + +> +336.411 + +> +2.766 + +> +1 + +> +59.280 + +> +3 + +> +parp10 + +> + + +> + + +$$$$ +Compound 591 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2842 0.754 0.0 0 +M V30 3 N 2.5684 0.0235 0.0 0 +M V30 4 C 1.2724 2.2621 0.0 0 +M V30 5 C 3.8526 0.7775 0.0 0 +M V30 6 C 2.5566 3.0279 0.0 0 +M V30 7 C -0.0353 3.0279 0.0 0 +M V30 8 N 5.2664 0.3298 0.0 0 +M V30 9 C 3.8408 2.2856 0.0 0 +M V30 10 C 2.5448 4.5359 0.0 0 +M V30 11 C -1.3431 2.2856 0.0 0 +M V30 12 N 6.1383 1.5551 0.0 0 +M V30 13 C 5.7141 -1.0839 0.0 0 +M V30 14 C 5.2546 2.7569 0.0 0 +M V30 15 C -2.6509 3.0514 0.0 0 +M V30 16 C 5.7023 4.1943 0.0 0 +M V30 17 O -2.6626 4.5595 0.0 0 +M V30 18 N -3.9586 2.3092 0.0 0 +M V30 19 C -5.2664 3.075 0.0 0 +M V30 20 N -6.6449 2.4741 0.0 0 +M V30 21 C -5.4314 4.5713 0.0 0 +M V30 22 N -7.6581 3.5934 0.0 0 +M V30 23 C -6.963 1.025 0.0 0 +M V30 24 C -6.9041 4.8894 0.0 0 +M V30 25 C -7.5285 6.2679 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 8 13 +M V30 13 1 9 14 +M V30 14 1 11 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 1 15 18 +M V30 18 1 18 19 +M V30 19 1 19 20 +M V30 20 2 19 21 +M V30 21 1 20 22 +M V30 22 1 20 23 +M V30 23 1 21 24 +M V30 24 1 24 25 +M V30 25 1 6 9 +M V30 26 2 12 14 +M V30 27 2 22 24 +M V30 END BOND +M V30 END CTAB +M END +> +591 + +> +Z372550618 + +> +342.396 + +> +-0.064 + +> +2 + +> +93.840 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL508796 + +> +0.9 + +$$$$ +Compound 592 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4974 0.0 0 +M V30 3 N -1.331 -2.2342 0.0 0 +M V30 4 C 1.2835 -2.2342 0.0 0 +M V30 5 N -1.3429 -3.7316 0.0 0 +M V30 6 C 1.2716 -3.7316 0.0 0 +M V30 7 C 2.5788 -1.4736 0.0 0 +M V30 8 C -0.0475 -4.4684 0.0 0 +M V30 9 C 2.567 -4.4684 0.0 0 +M V30 10 C 3.8742 -2.2104 0.0 0 +M V30 11 C -0.0594 -5.9659 0.0 0 +M V30 12 C 3.8623 -3.7079 0.0 0 +M V30 13 C 1.2359 -6.7146 0.0 0 +M V30 14 O 2.5313 -5.954 0.0 0 +M V30 15 N 1.224 -8.212 0.0 0 +M V30 16 C 2.5194 -8.9488 0.0 0 +M V30 17 C 2.5075 -10.4463 0.0 0 +M V30 18 C 1.1884 -11.195 0.0 0 +M V30 19 C 3.8029 -11.195 0.0 0 +M V30 20 N 1.1765 -12.6924 0.0 0 +M V30 21 C 3.791 -12.6924 0.0 0 +M V30 22 C 2.4719 -13.4292 0.0 0 +M V30 23 N 2.46 -14.9266 0.0 0 +M V30 24 N 1.224 -15.8061 0.0 0 +M V30 25 C 3.6722 -15.8061 0.0 0 +M V30 26 C 1.6756 -17.2322 0.0 0 +M V30 27 N 3.1968 -17.2322 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 11 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 2 20 22 +M V30 22 1 22 23 +M V30 23 1 23 24 +M V30 24 1 23 25 +M V30 25 2 24 26 +M V30 26 2 25 27 +M V30 27 1 6 8 +M V30 28 1 10 12 +M V30 29 1 21 22 +M V30 30 1 26 27 +M V30 END BOND +M V30 END CTAB +M END +> +592 + +> +Z422220298 + +> +361.357 + +> +-1.639 + +> +2 + +> +114.160 + +> +5 + +> +parp2, parp10 + +> + + +> + + +$$$$ +Compound 593 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 0.9868 1.1278 0.0 0 +M V30 3 C -1.3863 0.6226 0.0 0 +M V30 4 S 2.4672 0.9868 0.0 0 +M V30 5 N 0.2232 2.4437 0.0 0 +M V30 6 N -1.2453 2.1265 0.0 0 +M V30 7 N -2.7022 -0.1174 0.0 0 +M V30 8 C 3.3366 2.2087 0.0 0 +M V30 9 C -4.018 0.6461 0.0 0 +M V30 10 C 4.8169 2.0677 0.0 0 +M V30 11 C -5.3339 -0.0939 0.0 0 +M V30 12 N 5.6863 3.2896 0.0 0 +M V30 13 N 5.4161 0.7049 0.0 0 +M V30 14 C -6.6497 0.6696 0.0 0 +M V30 15 C 7.1667 3.1486 0.0 0 +M V30 16 C 6.8964 0.5639 0.0 0 +M V30 17 C -7.9656 -0.0704 0.0 0 +M V30 18 C 7.7658 1.7858 0.0 0 +M V30 19 C 8.0361 4.3705 0.0 0 +M V30 20 O 7.4956 -0.7989 0.0 0 +M V30 21 C 9.2462 1.6448 0.0 0 +M V30 22 C 9.5164 4.2295 0.0 0 +M V30 23 C 10.1156 2.8666 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 1 4 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 1 10 13 +M V30 13 1 11 14 +M V30 14 1 12 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 1 15 19 +M V30 19 2 16 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 2 21 23 +M V30 23 1 5 6 +M V30 24 1 16 18 +M V30 25 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +593 + +> +Z15503352 + +> +347.458 + +> +2.531 + +> +2 + +> +79.270 + +> +7 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 594 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.489 0.0118 0.0 0 +M V30 3 C -0.4727 -1.4181 0.0 0 +M V30 4 C 1.938 -1.4062 0.0 0 +M V30 5 C 0.7326 -2.2925 0.0 0 +M V30 6 C 3.3561 -1.8553 0.0 0 CFG=1 +M V30 7 N 4.5615 -0.9572 0.0 0 +M V30 8 C 3.8052 -3.2734 0.0 0 +M V30 9 C 4.5497 0.5554 0.0 0 +M V30 10 C 5.7669 -1.8317 0.0 0 +M V30 11 C 5.2942 -3.2616 0.0 0 +M V30 12 O 3.238 1.3235 0.0 0 +M V30 13 C 5.8378 1.3235 0.0 0 +M V30 14 C 5.826 2.8362 0.0 0 +M V30 15 N 4.5142 3.5925 0.0 0 +M V30 16 C 7.1141 3.5925 0.0 0 +M V30 17 N 4.5024 5.1051 0.0 0 +M V30 18 C 7.1023 5.1051 0.0 0 +M V30 19 C 8.4022 2.848 0.0 0 +M V30 20 C 5.7905 5.8614 0.0 0 +M V30 21 C 8.3904 5.8614 0.0 0 +M V30 22 C 9.6903 3.6043 0.0 0 +M V30 23 O 5.7787 7.3741 0.0 0 +M V30 24 C 9.6785 5.1288 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 2 3 5 +M V30 5 1 6 4 CFG=1 +M V30 6 1 6 7 +M V30 7 1 6 8 +M V30 8 1 7 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 1 13 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 1 17 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 2 20 23 +M V30 23 2 21 24 +M V30 24 1 4 5 +M V30 25 1 10 11 +M V30 26 1 18 20 +M V30 27 1 22 24 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 6) +M V30 END COLLECTION +M V30 END CTAB +M END +> +594 + +> +Z424283758 + +> +339.411 + +> +0.907 + +> +1 + +> +61.770 + +> +3 + +> +parp10 + +> + + +> + + +$$$$ +Compound 595 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 34 37 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2868 -0.7437 0.0 0 +M V30 3 C 2.5737 0.0118 0.0 0 +M V30 4 C 1.275 -2.2313 0.0 0 +M V30 5 C 3.8605 -0.7319 0.0 0 +M V30 6 C 2.5618 -2.9751 0.0 0 +M V30 7 N 5.1474 0.0236 0.0 0 +M V30 8 C 3.8487 -2.2195 0.0 0 +M V30 9 C 5.1356 1.5347 0.0 0 +M V30 10 C 6.4342 -0.7201 0.0 0 +M V30 11 S 3.8251 2.2903 0.0 0 +M V30 12 N 6.4224 2.2903 0.0 0 +M V30 13 O 6.4224 -2.2077 0.0 0 +M V30 14 C 7.7211 0.0354 0.0 0 +M V30 15 C 3.8133 3.8015 0.0 0 CFG=2 +M V30 16 C 7.7093 1.5583 0.0 0 +M V30 17 C 9.0079 -0.7083 0.0 0 +M V30 18 C 5.1001 4.5571 0.0 0 +M V30 19 C 2.5028 4.5571 0.0 0 +M V30 20 C 8.9961 2.3139 0.0 0 +M V30 21 C 10.2948 0.0472 0.0 0 +M V30 22 O 6.387 3.8251 0.0 0 +M V30 23 N 5.0883 6.0682 0.0 0 +M V30 24 C 10.283 1.582 0.0 0 +M V30 25 C 3.7779 6.8238 0.0 0 +M V30 26 C 2.4674 6.0918 0.0 0 +M V30 27 C 3.7661 8.335 0.0 0 +M V30 28 C 1.1569 6.8474 0.0 0 +M V30 29 C 2.4556 9.1024 0.0 0 +M V30 30 C 1.1451 8.3586 0.0 0 +M V30 31 N -0.1652 9.126 0.0 0 +M V30 32 C -0.177 10.6371 0.0 0 +M V30 33 O -1.4875 11.3927 0.0 0 +M V30 34 C 1.1097 11.3927 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 7 9 +M V30 9 1 7 10 +M V30 10 1 9 11 +M V30 11 2 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 1 15 11 CFG=1 +M V30 15 1 12 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 15 19 +M V30 19 1 16 20 +M V30 20 2 17 21 +M V30 21 2 18 22 +M V30 22 1 18 23 +M V30 23 2 20 24 +M V30 24 1 23 25 +M V30 25 2 25 26 +M V30 26 1 25 27 +M V30 27 1 26 28 +M V30 28 2 27 29 +M V30 29 2 28 30 +M V30 30 1 30 31 +M V30 31 1 31 32 +M V30 32 2 32 33 +M V30 33 1 32 34 +M V30 34 1 6 8 +M V30 35 2 14 16 +M V30 36 1 21 24 +M V30 37 1 29 30 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 15) +M V30 END COLLECTION +M V30 END CTAB +M END +> +595 + +> +Z15505337 + +> +492.977 + +> +4.382 + +> +2 + +> +90.870 + +> +6 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL461264 + +> +0.85 + +$$$$ +Compound 596 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 30 33 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2882 -0.7445 0.0 0 +M V30 3 C 2.5764 0.0118 0.0 0 +M V30 4 C 1.2764 -2.2337 0.0 0 +M V30 5 C 3.8647 -0.7327 0.0 0 +M V30 6 C 2.5646 -2.9783 0.0 0 +M V30 7 N 5.1529 0.0236 0.0 0 +M V30 8 C 3.8529 -2.2219 0.0 0 +M V30 9 C 5.1411 1.5364 0.0 0 +M V30 10 C 6.4412 -0.7209 0.0 0 +M V30 11 S 3.8292 2.2928 0.0 0 +M V30 12 N 6.4294 2.2928 0.0 0 +M V30 13 O 6.4294 -2.2101 0.0 0 +M V30 14 C 7.7294 0.0354 0.0 0 +M V30 15 C 2.5174 1.56 0.0 0 +M V30 16 C 7.7176 1.56 0.0 0 +M V30 17 C 9.0177 -0.7091 0.0 0 +M V30 18 C 1.2055 2.3164 0.0 0 +M V30 19 C 9.0059 2.3164 0.0 0 +M V30 20 C 10.3059 0.0472 0.0 0 +M V30 21 O -0.1063 1.5837 0.0 0 +M V30 22 N 1.1936 3.8292 0.0 0 +M V30 23 C 10.2941 1.5837 0.0 0 +M V30 24 C -0.1181 4.5856 0.0 0 +M V30 25 C 2.4819 4.5856 0.0 0 +M V30 26 C -0.13 6.0984 0.0 0 +M V30 27 C 2.4701 6.0984 0.0 0 +M V30 28 C 1.1582 6.8548 0.0 0 +M V30 29 C -1.4418 6.8548 0.0 0 +M V30 30 C 3.7583 6.8548 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 7 9 +M V30 9 1 7 10 +M V30 10 1 9 11 +M V30 11 2 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 2 18 21 +M V30 21 1 18 22 +M V30 22 2 19 23 +M V30 23 1 22 24 +M V30 24 1 22 25 +M V30 25 1 24 26 +M V30 26 1 25 27 +M V30 27 1 26 28 +M V30 28 1 26 29 +M V30 29 1 27 30 +M V30 30 1 6 8 +M V30 31 2 14 16 +M V30 32 1 20 23 +M V30 33 1 27 28 +M V30 END BOND +M V30 END CTAB +M END +> +596 + +> +Z15506502 + +> +441.974 + +> +5.455 + +> +0 + +> +52.980 + +> +4 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL460853 + +> +0.88 + +$$$$ +Compound 597 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2904 -0.7458 0.0 0 +M V30 3 C 2.5809 0.0118 0.0 0 +M V30 4 C 1.2786 -2.2375 0.0 0 +M V30 5 C 3.8713 -0.734 0.0 0 +M V30 6 C 2.569 -2.9834 0.0 0 +M V30 7 N 5.1618 0.0236 0.0 0 +M V30 8 C 3.8595 -2.2257 0.0 0 +M V30 9 C 5.15 1.539 0.0 0 +M V30 10 C 6.4523 -0.7221 0.0 0 +M V30 11 S 3.8358 2.2967 0.0 0 +M V30 12 N 6.4404 2.2967 0.0 0 +M V30 13 O 6.4404 -2.2139 0.0 0 +M V30 14 C 7.7427 0.0355 0.0 0 +M V30 15 C 3.824 3.8121 0.0 0 CFG=2 +M V30 16 C 7.7309 1.5627 0.0 0 +M V30 17 C 9.0332 -0.7103 0.0 0 +M V30 18 C 5.1144 4.5698 0.0 0 +M V30 19 C 2.5098 4.5698 0.0 0 +M V30 20 C 9.0213 2.3204 0.0 0 +M V30 21 C 10.3236 0.0473 0.0 0 +M V30 22 O 5.1026 6.0852 0.0 0 +M V30 23 N 6.4049 3.8358 0.0 0 +M V30 24 C 10.3118 1.5864 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 7 9 +M V30 9 1 7 10 +M V30 10 1 9 11 +M V30 11 2 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 1 15 11 CFG=1 +M V30 15 1 12 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 15 19 +M V30 19 1 16 20 +M V30 20 2 17 21 +M V30 21 2 18 22 +M V30 22 1 18 23 +M V30 23 2 20 24 +M V30 24 1 6 8 +M V30 25 2 14 16 +M V30 26 1 21 24 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 15) +M V30 END COLLECTION +M V30 END CTAB +M END +> +597 + +> +Z15506131 + +> +359.830 + +> +3.116 + +> +1 + +> +75.760 + +> +4 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL461051 + +> +0.85 + +$$$$ +Compound 598 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 34 37 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.3145 -0.7342 0.0 0 +M V30 3 C -1.3263 -2.2264 0.0 0 +M V30 4 C -2.6291 0.0236 0.0 0 +M V30 5 C -2.6409 -2.9725 0.0 0 +M V30 6 C -3.9436 -0.7105 0.0 0 +M V30 7 N -2.6527 -4.4647 0.0 0 +M V30 8 C -3.9554 -2.2146 0.0 0 +M V30 9 C -1.3619 -5.2108 0.0 0 +M V30 10 C -3.9673 -5.2108 0.0 0 +M V30 11 S -0.071 -4.441 0.0 0 +M V30 12 N -1.3737 -6.703 0.0 0 +M V30 13 O -5.2818 -4.441 0.0 0 +M V30 14 C -3.9791 -6.703 0.0 0 +M V30 15 C 1.2198 -5.1871 0.0 0 CFG=2 +M V30 16 C -2.6883 -7.4372 0.0 0 +M V30 17 C -5.2937 -7.4372 0.0 0 +M V30 18 C 2.5106 -4.4173 0.0 0 +M V30 19 C 1.2079 -6.6793 0.0 0 +M V30 20 C -2.7001 -8.9294 0.0 0 +M V30 21 C -5.3055 -8.9294 0.0 0 +M V30 22 O 2.4988 -2.9014 0.0 0 +M V30 23 N 3.8015 -5.1634 0.0 0 +M V30 24 C -4.0147 -9.6755 0.0 0 +M V30 25 C 3.7896 -6.6556 0.0 0 +M V30 26 C 5.0924 -4.3936 0.0 0 +M V30 27 C 5.0805 -7.3899 0.0 0 +M V30 28 C 2.4751 -7.3899 0.0 0 +M V30 29 C 6.3832 -5.1397 0.0 0 +M V30 30 C 5.0687 -8.8821 0.0 0 +M V30 31 C 2.4633 -8.8821 0.0 0 +M V30 32 C 7.6741 -4.3699 0.0 0 +M V30 33 C 3.7541 -9.6281 0.0 0 +M V30 34 N 8.965 -3.612 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 7 9 +M V30 9 1 7 10 +M V30 10 1 9 11 +M V30 11 2 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 1 15 11 CFG=3 +M V30 15 1 12 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 15 19 +M V30 19 1 16 20 +M V30 20 2 17 21 +M V30 21 2 18 22 +M V30 22 1 18 23 +M V30 23 2 20 24 +M V30 24 1 23 25 +M V30 25 1 23 26 +M V30 26 2 25 27 +M V30 27 1 25 28 +M V30 28 1 26 29 +M V30 29 1 27 30 +M V30 30 2 28 31 +M V30 31 1 29 32 +M V30 32 2 30 33 +M V30 33 3 32 34 +M V30 34 1 6 8 +M V30 35 2 14 16 +M V30 36 1 21 24 +M V30 37 1 31 33 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 15) +M V30 END COLLECTION +M V30 END CTAB +M END +> +598 + +> +Z15506175 + +> +488.989 + +> +5.288 + +> +0 + +> +76.770 + +> +7 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL460853 + +> +0.88 + +$$$$ +Compound 599 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 33 36 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2868 -0.7437 0.0 0 +M V30 3 C 2.5736 0.0118 0.0 0 +M V30 4 C 1.275 -2.2312 0.0 0 +M V30 5 C 3.8604 -0.7319 0.0 0 +M V30 6 C 2.5618 -2.975 0.0 0 +M V30 7 N 5.1472 0.0236 0.0 0 +M V30 8 C 3.8486 -2.2194 0.0 0 +M V30 9 C 5.1354 1.5347 0.0 0 +M V30 10 C 6.434 -0.7201 0.0 0 +M V30 11 S 3.825 2.2902 0.0 0 +M V30 12 N 6.4222 2.2902 0.0 0 +M V30 13 O 6.4222 -2.2076 0.0 0 +M V30 14 C 7.7208 0.0354 0.0 0 +M V30 15 C 3.8132 3.8013 0.0 0 CFG=2 +M V30 16 C 7.709 1.5583 0.0 0 +M V30 17 C 9.0076 -0.7083 0.0 0 +M V30 18 C 5.1 4.5569 0.0 0 +M V30 19 C 2.5027 4.5569 0.0 0 +M V30 20 C 8.9958 2.3138 0.0 0 +M V30 21 C 10.2944 0.0472 0.0 0 +M V30 22 O 6.3868 3.825 0.0 0 +M V30 23 N 5.0882 6.068 0.0 0 +M V30 24 C 10.2826 1.5819 0.0 0 +M V30 25 C 3.7777 6.8236 0.0 0 +M V30 26 C 2.4673 6.0916 0.0 0 +M V30 27 C 3.7659 8.3347 0.0 0 +M V30 28 C 1.1569 6.8472 0.0 0 +M V30 29 C 2.4555 9.102 0.0 0 +M V30 30 C 1.1451 8.3583 0.0 0 +M V30 31 C -0.1652 9.1257 0.0 0 +M V30 32 O -1.4756 8.3819 0.0 0 +M V30 33 N -0.177 10.6368 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 7 9 +M V30 9 1 7 10 +M V30 10 1 9 11 +M V30 11 2 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 1 15 11 CFG=1 +M V30 15 1 12 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 15 19 +M V30 19 1 16 20 +M V30 20 2 17 21 +M V30 21 2 18 22 +M V30 22 1 18 23 +M V30 23 2 20 24 +M V30 24 1 23 25 +M V30 25 2 25 26 +M V30 26 1 25 27 +M V30 27 1 26 28 +M V30 28 2 27 29 +M V30 29 2 28 30 +M V30 30 1 30 31 +M V30 31 2 31 32 +M V30 32 1 31 33 +M V30 33 1 6 8 +M V30 34 2 14 16 +M V30 35 1 21 24 +M V30 36 1 29 30 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 15) +M V30 END COLLECTION +M V30 END CTAB +M END +> +599 + +> +Z15506202 + +> +478.951 + +> +4.171 + +> +2 + +> +104.860 + +> +6 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL461264 + +> +0.86 + +$$$$ +Compound 600 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 30 33 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2877 -0.7442 0.0 0 +M V30 3 C 2.5755 0.0118 0.0 0 +M V30 4 C 1.2759 -2.2328 0.0 0 +M V30 5 C 3.8632 -0.7324 0.0 0 +M V30 6 C 2.5636 -2.9771 0.0 0 +M V30 7 N 5.151 0.0236 0.0 0 +M V30 8 C 3.8514 -2.221 0.0 0 +M V30 9 C 5.1392 1.5358 0.0 0 +M V30 10 C 6.4387 -0.7206 0.0 0 +M V30 11 S 3.8278 2.2919 0.0 0 +M V30 12 N 6.4269 2.2919 0.0 0 +M V30 13 O 6.4269 -2.2092 0.0 0 +M V30 14 C 7.7265 0.0354 0.0 0 +M V30 15 C 3.816 3.8041 0.0 0 CFG=2 +M V30 16 C 7.7147 1.5594 0.0 0 +M V30 17 C 9.0142 -0.7088 0.0 0 +M V30 18 C 5.1037 4.5603 0.0 0 +M V30 19 C 2.5046 4.5603 0.0 0 +M V30 20 C 9.0024 2.3155 0.0 0 +M V30 21 C 10.302 0.0472 0.0 0 +M V30 22 O 6.3915 3.8278 0.0 0 +M V30 23 N 5.0919 6.0725 0.0 0 +M V30 24 C 10.2902 1.5831 0.0 0 +M V30 25 C 3.7805 6.8286 0.0 0 +M V30 26 C 2.4691 6.0961 0.0 0 +M V30 27 C 3.7687 8.3408 0.0 0 +M V30 28 C 1.1577 6.8522 0.0 0 +M V30 29 C 2.4573 9.1088 0.0 0 +M V30 30 C 1.1459 8.3645 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 7 9 +M V30 9 1 7 10 +M V30 10 1 9 11 +M V30 11 2 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 1 15 11 CFG=1 +M V30 15 1 12 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 15 19 +M V30 19 1 16 20 +M V30 20 2 17 21 +M V30 21 2 18 22 +M V30 22 1 18 23 +M V30 23 2 20 24 +M V30 24 1 23 25 +M V30 25 1 25 26 +M V30 26 1 25 27 +M V30 27 1 26 28 +M V30 28 1 27 29 +M V30 29 1 28 30 +M V30 30 1 6 8 +M V30 31 2 14 16 +M V30 32 1 21 24 +M V30 33 1 29 30 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 15) +M V30 END COLLECTION +M V30 END CTAB +M END +> +600 + +> +Z15506212 + +> +441.974 + +> +5.413 + +> +1 + +> +61.770 + +> +5 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL518175 + +> +0.92 + +$$$$ +Compound 601 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 32 35 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2882 -0.7445 0.0 0 +M V30 3 C 2.5765 0.0118 0.0 0 +M V30 4 C 1.2764 -2.2337 0.0 0 +M V30 5 C 3.8648 -0.7327 0.0 0 +M V30 6 C 2.5647 -2.9783 0.0 0 +M V30 7 N 5.153 0.0236 0.0 0 +M V30 8 C 3.853 -2.2219 0.0 0 +M V30 9 C 5.1412 1.5364 0.0 0 +M V30 10 C 6.4413 -0.7209 0.0 0 +M V30 11 S 3.8293 2.2928 0.0 0 +M V30 12 N 6.4295 2.2928 0.0 0 +M V30 13 O 6.4295 -2.2101 0.0 0 +M V30 14 C 7.7296 0.0354 0.0 0 +M V30 15 C 3.8175 3.8057 0.0 0 CFG=2 +M V30 16 C 7.7178 1.5601 0.0 0 +M V30 17 C 9.0179 -0.7091 0.0 0 +M V30 18 C 2.5056 4.5621 0.0 0 +M V30 19 C 5.1058 4.5621 0.0 0 +M V30 20 C 9.0061 2.3165 0.0 0 +M V30 21 C 10.3061 0.0472 0.0 0 +M V30 22 O 1.1937 3.8293 0.0 0 +M V30 23 N 2.4938 6.0749 0.0 0 +M V30 24 C 10.2943 1.5837 0.0 0 +M V30 25 C 1.1819 6.8314 0.0 0 +M V30 26 C 0.6618 5.4367 0.0 0 +M V30 27 C -0.3072 7.1032 0.0 0 +M V30 28 C 2.1392 7.9896 0.0 0 +M V30 29 N 0.1418 4.0421 0.0 0 +M V30 30 C -0.8273 8.5215 0.0 0 +M V30 31 C 1.6192 9.4079 0.0 0 +M V30 32 C 0.13 9.6797 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 7 9 +M V30 9 1 7 10 +M V30 10 1 9 11 +M V30 11 2 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 1 15 11 CFG=3 +M V30 15 1 12 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 15 19 +M V30 19 1 16 20 +M V30 20 2 17 21 +M V30 21 2 18 22 +M V30 22 1 18 23 +M V30 23 2 20 24 +M V30 24 1 23 25 +M V30 25 1 25 26 +M V30 26 1 25 27 +M V30 27 1 25 28 +M V30 28 3 26 29 +M V30 29 1 27 30 +M V30 30 1 28 31 +M V30 31 1 30 32 +M V30 32 1 6 8 +M V30 33 2 14 16 +M V30 34 1 21 24 +M V30 35 1 31 32 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 15) +M V30 END COLLECTION +M V30 END CTAB +M END +> +601 + +> +Z15506321 + +> +466.983 + +> +4.970 + +> +1 + +> +85.560 + +> +5 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL460853 + +> +0.87 + +$$$$ +Compound 602 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5079 0.0 0 +M V30 3 C 1.2723 2.2736 0.0 0 +M V30 4 C -1.3194 2.2736 0.0 0 +M V30 5 C 1.2605 3.7815 0.0 0 +M V30 6 C -1.3312 3.7815 0.0 0 +M V30 7 N 2.5446 4.5355 0.0 0 +M V30 8 C -0.0471 4.5355 0.0 0 +M V30 9 C 2.5328 6.0434 0.0 0 +M V30 10 C 3.8286 3.7933 0.0 0 +M V30 11 S 1.2251 6.7973 0.0 0 +M V30 12 N 3.8169 6.7973 0.0 0 +M V30 13 O 3.8169 2.3089 0.0 0 +M V30 14 C 5.1127 4.5473 0.0 0 +M V30 15 C 1.2133 8.3053 0.0 0 +M V30 16 C 5.1009 6.0669 0.0 0 +M V30 17 C 6.3968 3.8051 0.0 0 +M V30 18 C -0.0942 9.0592 0.0 0 +M V30 19 C 6.385 6.8209 0.0 0 +M V30 20 C 7.6809 4.559 0.0 0 +M V30 21 O -1.4018 8.3288 0.0 0 +M V30 22 N -0.106 10.5671 0.0 0 +M V30 23 C 7.6691 6.0905 0.0 0 +M V30 24 C -1.4136 11.3211 0.0 0 +M V30 25 C 1.178 11.3211 0.0 0 +M V30 26 C -1.4254 12.829 0.0 0 +M V30 27 C 1.1662 12.829 0.0 0 +M V30 28 C -0.1413 13.5947 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 7 9 +M V30 9 1 7 10 +M V30 10 1 9 11 +M V30 11 2 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 2 18 21 +M V30 21 1 18 22 +M V30 22 2 19 23 +M V30 23 1 22 24 +M V30 24 1 22 25 +M V30 25 1 24 26 +M V30 26 1 25 27 +M V30 27 1 26 28 +M V30 28 1 6 8 +M V30 29 2 14 16 +M V30 30 1 20 23 +M V30 31 1 27 28 +M V30 END BOND +M V30 END CTAB +M END +> +602 + +> +Z15506836 + +> +413.920 + +> +4.417 + +> +0 + +> +52.980 + +> +4 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL460853 + +> +0.86 + +$$$$ +Compound 603 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 F 3.141 3.8073 0.0 0 +M V30 3 C 4.4379 3.0696 0.0 0 +M V30 4 F 3.6645 1.7727 0.0 0 +M V30 5 F 5.1756 4.3903 0.0 0 +M V30 6 C 5.7348 2.3201 0.0 0 +M V30 7 C 7.0316 3.0815 0.0 0 +M V30 8 C 5.7229 0.8209 0.0 0 +M V30 9 C 8.3285 2.3319 0.0 0 +M V30 10 C 7.0197 0.0832 0.0 0 +M V30 11 C 8.3166 0.8328 0.0 0 +M V30 12 C 9.6135 0.0951 0.0 0 +M V30 13 C 10.9104 0.8566 0.0 0 +M V30 14 N 12.2072 0.1189 0.0 0 +M V30 15 C 13.5041 0.8804 0.0 0 +M V30 16 O 13.4922 2.4033 0.0 0 +M V30 17 C 14.801 0.1427 0.0 0 +M V30 18 C 14.7891 -1.3563 0.0 0 +M V30 19 C 16.0979 0.9042 0.0 0 +M V30 20 C 16.086 -2.094 0.0 0 +M V30 21 C 17.3948 0.1665 0.0 0 +M V30 22 C 17.3829 -1.3325 0.0 0 +M V30 23 C 15.848 -3.5812 0.0 0 +M V30 24 C 18.7749 -1.8679 0.0 0 +M V30 25 C 16.8593 -4.6758 0.0 0 +M V30 26 C 19.2032 -3.2957 0.0 0 +M V30 27 N 18.3585 -4.5569 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 2 3 +M V30 2 1 3 4 +M V30 3 1 3 5 +M V30 4 1 3 6 +M V30 5 2 6 7 +M V30 6 1 6 8 +M V30 7 1 7 9 +M V30 8 2 8 10 +M V30 9 2 9 11 +M V30 10 1 11 12 +M V30 11 1 12 13 +M V30 12 1 13 14 +M V30 13 1 14 15 +M V30 14 2 15 16 +M V30 15 1 15 17 +M V30 16 2 17 18 +M V30 17 1 17 19 +M V30 18 1 18 20 +M V30 19 2 19 21 +M V30 20 2 20 22 +M V30 21 1 20 23 +M V30 22 1 22 24 +M V30 23 1 23 25 +M V30 24 1 24 26 +M V30 25 1 25 27 +M V30 26 1 10 11 +M V30 27 1 21 22 +M V30 28 1 26 27 +M V30 END BOND +M V30 END CTAB +M END +> +603 + +> +Z1829778459 + +> +362.389 + +> +3.703 + +> +2 + +> +41.130 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3629675 + +> +0.9 + +$$$$ +Compound 604 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.4398 -0.4484 0.0 0 +M V30 3 N -2.6672 0.4484 0.0 0 +M V30 4 N -1.9119 -1.8647 0.0 0 +M V30 5 C -3.8946 -0.4248 0.0 0 +M V30 6 C -2.679 1.9591 0.0 0 +M V30 7 C -3.4225 -1.8529 0.0 0 CFG=2 +M V30 8 O -5.3344 0.0472 0.0 0 +M V30 9 C -1.3926 2.7144 0.0 0 +M V30 10 C -4.3195 -3.0566 0.0 0 +M V30 11 O -0.1062 1.9709 0.0 0 +M V30 12 N -1.4044 4.225 0.0 0 +M V30 13 C -3.7176 -4.4139 0.0 0 +M V30 14 C -0.118 4.9804 0.0 0 +M V30 15 C -4.6145 -5.6177 0.0 0 +M V30 16 C -0.1298 6.491 0.0 0 +M V30 17 C 1.1683 4.2486 0.0 0 +M V30 18 C -4.0126 -6.9749 0.0 0 +M V30 19 C -6.1133 -5.4524 0.0 0 +M V30 20 C 1.1565 7.2463 0.0 0 +M V30 21 C -1.4398 7.2463 0.0 0 +M V30 22 C 2.4547 5.004 0.0 0 +M V30 23 C -4.9095 -8.1787 0.0 0 +M V30 24 C -7.0103 -6.6562 0.0 0 +M V30 25 C 2.4429 6.5146 0.0 0 +M V30 26 C -6.4084 -8.0135 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 CFG=1 +M V30 10 2 9 11 +M V30 11 1 9 12 +M V30 12 1 10 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 1 15 19 +M V30 19 1 16 20 +M V30 20 1 16 21 +M V30 21 2 17 22 +M V30 22 1 18 23 +M V30 23 2 19 24 +M V30 24 2 20 25 +M V30 25 2 23 26 +M V30 26 1 5 7 +M V30 27 1 22 25 +M V30 28 1 24 26 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 7) +M V30 END COLLECTION +M V30 END CTAB +M END +> +604 + +> +Z15559757 + +> +351.399 + +> +2.442 + +> +2 + +> +78.510 + +> +6 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 605 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.095 1.0126 0.0 0 +M V30 3 N 0.9302 2.5081 0.0 0 +M V30 4 N 2.5434 0.7182 0.0 0 +M V30 5 C -0.3768 3.2617 0.0 0 +M V30 6 C 2.2843 3.1321 0.0 0 +M V30 7 C 3.2734 2.0253 0.0 0 +M V30 8 C -1.6838 2.5316 0.0 0 +M V30 9 C -0.3885 4.7689 0.0 0 +M V30 10 C -2.9908 3.2852 0.0 0 +M V30 11 C -1.6956 5.5225 0.0 0 +M V30 12 N -4.2979 2.5552 0.0 0 +M V30 13 C -3.0026 4.7924 0.0 0 +M V30 14 C -5.6049 3.3088 0.0 0 +M V30 15 O -5.6167 4.816 0.0 0 +M V30 16 C -6.912 2.5787 0.0 0 +M V30 17 O -6.9237 1.095 0.0 0 +M V30 18 N -8.219 3.3323 0.0 0 +M V30 19 C -9.526 2.6023 0.0 0 CFG=2 +M V30 20 C -10.8331 3.3559 0.0 0 +M V30 21 C -9.5378 1.1186 0.0 0 +M V30 22 C -10.8449 4.8631 0.0 0 +M V30 23 C -12.1401 2.6258 0.0 0 +M V30 24 N -12.1519 5.6167 0.0 0 +M V30 25 C -13.4472 3.3794 0.0 0 +M V30 26 C -13.4589 4.8866 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 1 10 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 19 18 CFG=1 +M V30 19 1 19 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 1 20 23 +M V30 23 1 22 24 +M V30 24 2 23 25 +M V30 25 2 24 26 +M V30 26 1 6 7 +M V30 27 1 11 13 +M V30 28 1 25 26 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 19) +M V30 END COLLECTION +M V30 END CTAB +M END +> +605 + +> +Z437251380 + +> +353.375 + +> +-1.028 + +> +3 + +> +103.430 + +> +5 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 606 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.1656 1.5023 0.0 0 +M V30 3 C 1.455 -0.2957 0.0 0 +M V30 4 N 1.1947 2.1292 0.0 0 +M V30 5 N -1.4786 2.2594 0.0 0 +M V30 6 C 2.2002 1.0173 0.0 0 +M V30 7 C -2.7917 1.5259 0.0 0 +M V30 8 C 3.6789 1.1829 0.0 0 +M V30 9 O -2.8035 0.0354 0.0 0 +M V30 10 C -4.1047 2.283 0.0 0 +M V30 11 C 4.5542 -0.0236 0.0 0 +M V30 12 C 4.2822 2.5669 0.0 0 +M V30 13 C -5.4178 1.5496 0.0 0 +M V30 14 C 6.0329 0.1419 0.0 0 +M V30 15 C 5.7608 2.7325 0.0 0 +M V30 16 C -6.7308 2.3067 0.0 0 +M V30 17 C 6.6362 1.5259 0.0 0 +M V30 18 C 6.9083 -1.0646 0.0 0 +M V30 19 O -8.0439 1.5733 0.0 0 +M V30 20 C 8.1149 1.6915 0.0 0 +M V30 21 C 8.3869 -0.899 0.0 0 +M V30 22 C -9.357 2.3303 0.0 0 +M V30 23 C 8.9902 0.485 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 10 13 +M V30 13 1 11 14 +M V30 14 2 12 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 2 14 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 18 21 +M V30 21 1 19 22 +M V30 22 1 20 23 +M V30 23 1 4 6 +M V30 24 1 15 17 +M V30 25 2 21 23 +M V30 END BOND +M V30 END CTAB +M END +> +606 + +> +Z441864874 + +> +326.413 + +> +4.122 + +> +1 + +> +51.220 + +> +6 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL452158 + +> +0.91 + +$$$$ +Compound 607 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.2976 -0.738 0.0 0 +M V30 3 C 2.5952 0.0238 0.0 0 +M V30 4 C 1.2856 -2.238 0.0 0 +M V30 5 C 3.8928 -0.7142 0.0 0 +M V30 6 C 2.5832 -2.9761 0.0 0 +M V30 7 C 3.8809 -2.2142 0.0 0 +M V30 8 C 5.1785 -2.9523 0.0 0 CFG=1 +M V30 9 O 5.1665 -4.4523 0.0 0 +M V30 10 C 6.4761 -2.1904 0.0 0 +M V30 11 C 6.4642 -5.2023 0.0 0 +M V30 12 N 7.7737 -2.9285 0.0 0 +M V30 13 C 7.7618 -4.4285 0.0 0 +M V30 14 C 9.0713 -2.1666 0.0 0 +M V30 15 C 10.3689 -2.9047 0.0 0 +M V30 16 N 11.6665 -2.1428 0.0 0 +M V30 17 N 10.357 -4.4047 0.0 0 +M V30 18 C 12.9641 -2.8809 0.0 0 +M V30 19 C 11.6546 -5.1546 0.0 0 +M V30 20 C 12.9522 -4.3808 0.0 0 +M V30 21 C 14.2617 -2.119 0.0 0 +M V30 22 O 11.6427 -6.6546 0.0 0 +M V30 23 C 14.2498 -5.1308 0.0 0 +M V30 24 C 15.5593 -2.8571 0.0 0 +M V30 25 C 15.5474 -4.357 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 8 7 CFG=3 +M V30 8 1 8 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 1 20 23 +M V30 23 2 21 24 +M V30 24 2 23 25 +M V30 25 1 6 7 +M V30 26 1 12 13 +M V30 27 1 19 20 +M V30 28 1 24 25 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 8) +M V30 END COLLECTION +M V30 END CTAB +M END +> +607 + +> +Z368851352 + +> +339.364 + +> +1.753 + +> +1 + +> +53.930 + +> +3 + +> +parp1 + +> + + +> + + +$$$$ +Compound 608 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4497 -1.4203 0.0 0 +M V30 3 N -0.4497 -2.6277 0.0 0 +M V30 4 C 1.8701 -1.8701 0.0 0 +M V30 5 C 0.4261 -3.835 0.0 0 +M V30 6 C -1.9648 -2.6158 0.0 0 +M V30 7 C 1.8583 -3.3616 0.0 0 +M V30 8 C 3.1603 -1.1008 0.0 0 +M V30 9 O -0.0473 -5.2554 0.0 0 +M V30 10 C -2.7224 -3.906 0.0 0 +M V30 11 C 3.1485 -4.1073 0.0 0 +M V30 12 C 4.4505 -1.8346 0.0 0 +M V30 13 C -4.2375 -3.8942 0.0 0 +M V30 14 C 4.4387 -3.3379 0.0 0 +M V30 15 N -5.0068 -5.1844 0.0 0 +M V30 16 C -4.2611 -6.4746 0.0 0 +M V30 17 C -6.5219 -5.1726 0.0 0 +M V30 18 C -5.0305 -7.7648 0.0 0 +M V30 19 C -7.2913 -6.4628 0.0 0 +M V30 20 O -6.5456 -7.7529 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 10 13 +M V30 13 2 11 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 5 7 +M V30 21 1 12 14 +M V30 22 1 19 20 +M V30 END BOND +M V30 END CTAB +M END +> +608 + +> +Z103718322 + +> +274.315 + +> +1.841 + +> +0 + +> +49.850 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3605976 + +> +0.91 + +$$$$ +Compound 609 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.2903 -0.7339 0.0 0 +M V30 3 C -1.314 -0.7339 0.0 0 +M V30 4 N 2.5807 0.0236 0.0 0 +M V30 5 N 1.2785 -2.2256 0.0 0 +M V30 6 C -2.6281 0.0236 0.0 0 +M V30 7 C 3.8711 -0.7103 0.0 0 +M V30 8 C 2.5689 -2.9714 0.0 0 +M V30 9 C -0.0355 -2.9714 0.0 0 +M V30 10 O -3.9422 -0.7103 0.0 0 +M V30 11 N -2.6399 1.539 0.0 0 +M V30 12 C 3.8593 -2.2019 0.0 0 +M V30 13 C 5.1615 0.0473 0.0 0 +M V30 14 O 2.5571 -4.4631 0.0 0 +M V30 15 C -1.3495 -2.2138 0.0 0 +M V30 16 C -0.0473 -4.4631 0.0 0 +M V30 17 C 5.1497 -2.9477 0.0 0 +M V30 18 C 6.4519 -0.6866 0.0 0 +M V30 19 C -2.6636 -2.9596 0.0 0 +M V30 20 C -1.3614 -5.2089 0.0 0 +M V30 21 C 6.4401 -2.1901 0.0 0 +M V30 22 C -2.6755 -4.4512 0.0 0 +M V30 23 O -3.9895 -5.1971 0.0 0 +M V30 24 C -4.0014 -6.6887 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 2 7 12 +M V30 12 1 7 13 +M V30 13 2 8 14 +M V30 14 2 9 15 +M V30 15 1 9 16 +M V30 16 1 12 17 +M V30 17 2 13 18 +M V30 18 1 15 19 +M V30 19 2 16 20 +M V30 20 2 17 21 +M V30 21 2 19 22 +M V30 22 1 22 23 +M V30 23 1 23 24 +M V30 24 1 8 12 +M V30 25 1 18 21 +M V30 26 1 20 22 +M V30 END BOND +M V30 END CTAB +M END +> +609 + +> +Z17076740 + +> +341.384 + +> +2.176 + +> +1 + +> +84.990 + +> +5 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 610 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.2897 0.7691 0.0 0 +M V30 3 C -1.3134 0.7691 0.0 0 +M V30 4 C 2.5794 0.0236 0.0 0 +M V30 5 C -2.6268 0.0236 0.0 0 +M V30 6 N 3.8692 0.7927 0.0 0 +M V30 7 N 2.5676 -1.4672 0.0 0 +M V30 8 C -2.6386 -1.4672 0.0 0 +M V30 9 C -3.9402 0.7927 0.0 0 +M V30 10 C 5.1589 0.0473 0.0 0 +M V30 11 C 3.8573 -2.2126 0.0 0 +M V30 12 C -3.952 -2.2126 0.0 0 +M V30 13 C -5.2536 0.0473 0.0 0 +M V30 14 C 5.1471 -1.4435 0.0 0 +M V30 15 C 6.4486 0.8164 0.0 0 +M V30 16 O 3.8455 -3.7035 0.0 0 +M V30 17 O -3.9638 -3.7035 0.0 0 +M V30 18 C -5.2654 -1.4435 0.0 0 +M V30 19 C 6.4368 -2.189 0.0 0 +M V30 20 C 7.7384 0.0709 0.0 0 +M V30 21 C -5.2772 -4.4489 0.0 0 +M V30 22 C 7.7265 -1.4198 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 2 10 14 +M V30 14 1 10 15 +M V30 15 2 11 16 +M V30 16 1 12 17 +M V30 17 2 12 18 +M V30 18 1 14 19 +M V30 19 2 15 20 +M V30 20 1 17 21 +M V30 21 2 19 22 +M V30 22 1 11 14 +M V30 23 1 13 18 +M V30 24 1 20 22 +M V30 END BOND +M V30 END CTAB +M END +> +610 + +> +Z955207634 + +> +312.386 + +> +2.740 + +> +1 + +> +50.690 + +> +5 + +> +parp3 + +> + + +> + + +$$$$ +Compound 611 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2867 -0.7437 0.0 0 +M V30 3 N 1.2749 -2.2311 0.0 0 +M V30 4 C 2.5734 0.0236 0.0 0 CFG=1 +M V30 5 C -0.0354 -2.9748 0.0 0 +M V30 6 O 3.8601 -0.72 0.0 0 +M V30 7 C 2.5616 1.5346 0.0 0 +M V30 8 N -0.2006 -4.4504 0.0 0 +M V30 9 C -1.4165 -2.3491 0.0 0 +M V30 10 C 5.1469 0.0472 0.0 0 +M V30 11 O 3.8483 2.3019 0.0 0 +M V30 12 N -1.6762 -4.7455 0.0 0 +M V30 13 C -2.4318 -3.447 0.0 0 +M V30 14 C 5.1351 1.5582 0.0 0 +M V30 15 C 6.4336 -0.6964 0.0 0 +M V30 16 C -3.931 -3.2817 0.0 0 +M V30 17 C 6.4218 2.3255 0.0 0 +M V30 18 C 7.7203 0.0708 0.0 0 +M V30 19 C -4.8282 -4.4858 0.0 0 +M V30 20 C -4.5566 -1.9005 0.0 0 +M V30 21 C 7.7085 1.5818 0.0 0 +M V30 22 O -4.2261 -5.8434 0.0 0 +M V30 23 C -6.3274 -4.3205 0.0 0 +M V30 24 C -6.0559 -1.7353 0.0 0 +M V30 25 C -5.1233 -7.0475 0.0 0 +M V30 26 C -6.953 -2.9394 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 4 2 CFG=3 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 10 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 2 16 19 +M V30 19 1 16 20 +M V30 20 2 17 21 +M V30 21 1 19 22 +M V30 22 1 19 23 +M V30 23 2 20 24 +M V30 24 1 22 25 +M V30 25 2 23 26 +M V30 26 1 11 14 +M V30 27 2 12 13 +M V30 28 1 18 21 +M V30 29 1 24 26 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 4) +M V30 END COLLECTION +M V30 END CTAB +M END +> +611 + +> +Z927973306 + +> +351.356 + +> +2.965 + +> +2 + +> +85.470 + +> +4 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 612 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 29 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2824 0.7529 0.0 0 +M V30 3 C 1.2706 2.2589 0.0 0 +M V30 4 C 2.5648 0.0235 0.0 0 +M V30 5 O -0.0352 3.0237 0.0 0 +M V30 6 C 2.5531 3.0237 0.0 0 +M V30 7 C 3.8473 0.7765 0.0 0 +M V30 8 C -1.3412 2.2825 0.0 0 +M V30 9 C 3.8355 2.2825 0.0 0 +M V30 10 Cl 5.1297 0.047 0.0 0 +M V30 11 C -2.6472 3.0472 0.0 0 CFG=1 +M V30 12 O -2.659 4.5532 0.0 0 +M V30 13 C -3.9532 2.306 0.0 0 +M V30 14 N -5.2592 3.0708 0.0 0 +M V30 15 C -6.6357 2.4707 0.0 0 +M V30 16 C -5.4239 4.565 0.0 0 +M V30 17 O -6.9534 1.0236 0.0 0 +M V30 18 N -7.6476 3.5884 0.0 0 +M V30 19 C -6.8946 4.8827 0.0 0 +M V30 20 C -9.1418 3.4473 0.0 0 +M V30 21 C -7.5181 6.2592 0.0 0 +M V30 22 C -10.036 4.6709 0.0 0 +M V30 23 C -9.7654 2.0942 0.0 0 +M V30 24 C -11.5302 4.5297 0.0 0 +M V30 25 C -11.2596 1.953 0.0 0 +M V30 26 N -12.4244 5.7533 0.0 0 CHG=1 +M V30 27 C -12.1538 3.1766 0.0 0 +M V30 28 O -13.9186 5.6121 0.0 0 +M V30 29 O -11.8243 7.1299 0.0 0 CHG=-1 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 1 11 12 CFG=3 +M V30 12 1 11 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 1 15 18 +M V30 18 2 16 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 1 20 23 +M V30 23 1 22 24 +M V30 24 2 23 25 +M V30 25 1 24 26 +M V30 26 2 24 27 +M V30 27 2 26 28 +M V30 28 1 26 29 +M V30 29 1 7 9 +M V30 30 1 18 19 +M V30 31 1 25 27 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 11) +M V30 END COLLECTION +M V30 END CTAB +M END +> +612 + +> +Z927936530 + +> +438.261 + +> +4.549 + +> +1 + +> +96.150 + +> +7 + +> +parp14 + +> + + +> + + +$$$$ +Compound 613 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5037 0.0 0 +M V30 3 N -1.3158 2.2556 0.0 0 +M V30 4 C 1.2688 2.2556 0.0 0 +M V30 5 C -1.3275 3.7594 0.0 0 +M V30 6 C 1.257 3.7594 0.0 0 +M V30 7 C 2.5493 1.5272 0.0 0 +M V30 8 N -0.0469 4.5231 0.0 0 +M V30 9 C -2.6316 4.5231 0.0 0 +M V30 10 C 2.5376 4.5231 0.0 0 +M V30 11 C 3.8299 2.2791 0.0 0 +M V30 12 C -3.9356 3.7829 0.0 0 +M V30 13 C 3.8182 3.7829 0.0 0 +M V30 14 C -5.2397 4.5466 0.0 0 +M V30 15 O -5.2515 6.0504 0.0 0 +M V30 16 N -6.5438 3.8064 0.0 0 +M V30 17 C -7.8479 4.5701 0.0 0 +M V30 18 C -9.1519 3.8299 0.0 0 +M V30 19 C -10.456 4.5936 0.0 0 +M V30 20 C -11.7601 3.8534 0.0 0 +M V30 21 C -10.4677 6.0973 0.0 0 +M V30 22 C -13.0641 4.6171 0.0 0 +M V30 23 C -11.7718 6.8492 0.0 0 +M V30 24 O -14.3682 3.8769 0.0 0 +M V30 25 C -13.0759 6.1208 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 1 18 19 +M V30 19 2 19 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 2 21 23 +M V30 23 1 22 24 +M V30 24 2 22 25 +M V30 25 1 6 8 +M V30 26 1 11 13 +M V30 27 1 23 25 +M V30 END BOND +M V30 END CTAB +M END +> +613 + +> +Z928029482 + +> +337.372 + +> +0.612 + +> +3 + +> +90.790 + +> +6 + +> +parp3 + +> + + +> + + +$$$$ +Compound 614 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 30 32 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5054 0.0 0 +M V30 3 F -1.5171 1.5171 0.0 0 +M V30 4 F 1.4701 1.5171 0.0 0 +M V30 5 C -0.0235 3.0108 0.0 0 +M V30 6 C -1.3289 3.7752 0.0 0 +M V30 7 C 1.2584 3.7752 0.0 0 +M V30 8 C -1.3407 5.2807 0.0 0 +M V30 9 C 1.2466 5.2807 0.0 0 +M V30 10 N -2.6462 6.0334 0.0 0 +M V30 11 C -0.0588 6.0334 0.0 0 +M V30 12 C -3.9517 5.2924 0.0 0 +M V30 13 N -0.0705 7.5388 0.0 0 +M V30 14 O -3.9634 3.8105 0.0 0 +M V30 15 C -5.2571 6.0451 0.0 0 +M V30 16 C -1.376 8.2915 0.0 0 +M V30 17 C 1.2113 8.2915 0.0 0 +M V30 18 O -5.2689 7.5505 0.0 0 +M V30 19 C -1.3878 9.7969 0.0 0 +M V30 20 C 1.1996 9.7969 0.0 0 +M V30 21 C -6.5744 8.3032 0.0 0 +M V30 22 O -0.1058 10.5496 0.0 0 +M V30 23 C -7.8798 7.5741 0.0 0 +M V30 24 C -6.5861 9.8087 0.0 0 +M V30 25 C -7.8916 6.0922 0.0 0 +M V30 26 C -9.1853 8.3268 0.0 0 +M V30 27 C -7.8916 10.5614 0.0 0 +M V30 28 O -6.6097 5.3512 0.0 0 +M V30 29 N -9.1971 5.3512 0.0 0 +M V30 30 C -9.1971 9.8322 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 2 5 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 2 8 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 2 12 14 +M V30 14 1 12 15 +M V30 15 1 13 16 +M V30 16 1 13 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 1 17 20 +M V30 20 1 18 21 +M V30 21 1 19 22 +M V30 22 2 21 23 +M V30 23 1 21 24 +M V30 24 1 23 25 +M V30 25 1 23 26 +M V30 26 2 24 27 +M V30 27 2 25 28 +M V30 28 1 25 29 +M V30 29 2 26 30 +M V30 30 1 9 11 +M V30 31 1 20 22 +M V30 32 1 27 30 +M V30 END BOND +M V30 END CTAB +M END +> +614 + +> +Z15685892 + +> +423.386 + +> +2.262 + +> +2 + +> +93.890 + +> +7 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3105431 + +> +0.89 + +$$$$ +Compound 615 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.2965 -0.7374 0.0 0 +M V30 3 C 1.2846 -2.2361 0.0 0 +M V30 4 C 2.593 0.0237 0.0 0 +M V30 5 C 2.5811 -2.9736 0.0 0 +M V30 6 C 3.8895 -0.7136 0.0 0 +M V30 7 C 3.8776 -2.2124 0.0 0 +M V30 8 C 5.1741 -2.9498 0.0 0 CFG=1 +M V30 9 N 6.4707 -2.1886 0.0 0 +M V30 10 C 5.1622 -4.4486 0.0 0 +M V30 11 C 7.7672 -2.926 0.0 0 +M V30 12 O 7.7553 -4.4248 0.0 0 +M V30 13 C 9.0637 -2.1648 0.0 0 +M V30 14 O 10.3602 -2.9023 0.0 0 +M V30 15 C 11.6567 -2.141 0.0 0 +M V30 16 C 12.9532 -2.8785 0.0 0 +M V30 17 C 11.6448 -0.6185 0.0 0 +M V30 18 C 12.9414 -4.3772 0.0 0 +M V30 19 C 14.2498 -2.1172 0.0 0 +M V30 20 C 12.9414 0.1427 0.0 0 +M V30 21 O 11.621 -5.1147 0.0 0 +M V30 22 N 14.2379 -5.1147 0.0 0 +M V30 23 C 14.2379 -0.5947 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 CFG=1 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 2 18 21 +M V30 21 1 18 22 +M V30 22 2 19 23 +M V30 23 1 6 7 +M V30 24 1 20 23 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 8) +M V30 END COLLECTION +M V30 END CTAB +M END +> +615 + +> +Z15685798 + +> +316.327 + +> +1.860 + +> +2 + +> +81.420 + +> +6 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3105430 + +> +0.91 + +$$$$ +Compound 616 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 30 33 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.8974 -1.2045 0.0 0 +M V30 3 C 1.417 -0.4487 0.0 0 +M V30 4 N -0.0236 -2.409 0.0 0 +M V30 5 C -2.409 -1.1927 0.0 0 +M V30 6 C 1.4052 -1.9366 0.0 0 +M V30 7 C 2.7042 0.307 0.0 0 +M V30 8 C -3.1648 -2.4799 0.0 0 +M V30 9 C -3.1648 0.118 0.0 0 +M V30 10 C 2.6924 -2.6688 0.0 0 +M V30 11 C 3.9914 -0.4251 0.0 0 +M V30 12 C -4.6764 -2.4681 0.0 0 +M V30 13 C -4.6764 0.1299 0.0 0 +M V30 14 C 3.9796 -1.913 0.0 0 +M V30 15 C 5.2786 0.3306 0.0 0 +M V30 16 C -5.4322 -1.1572 0.0 0 +M V30 17 N -6.9437 -1.1454 0.0 0 +M V30 18 C -7.6995 -2.4326 0.0 0 +M V30 19 O -6.9555 -3.7198 0.0 0 +M V30 20 C -9.2111 -2.4208 0.0 0 +M V30 21 O -9.9669 -3.708 0.0 0 +M V30 22 C -11.4784 -3.6962 0.0 0 +M V30 23 C -12.2342 -2.3854 0.0 0 +M V30 24 C -12.2342 -4.9834 0.0 0 +M V30 25 C -11.5021 -1.0746 0.0 0 +M V30 26 C -13.7458 -2.3736 0.0 0 +M V30 27 C -13.7458 -4.9716 0.0 0 +M V30 28 O -10.0141 -1.0628 0.0 0 +M V30 29 N -12.2578 0.2361 0.0 0 +M V30 30 C -14.5134 -3.6608 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 2 10 14 +M V30 14 1 11 15 +M V30 15 2 12 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 20 21 +M V30 21 1 21 22 +M V30 22 2 22 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 1 23 26 +M V30 26 2 24 27 +M V30 27 2 25 28 +M V30 28 1 25 29 +M V30 29 2 26 30 +M V30 30 1 4 6 +M V30 31 1 11 14 +M V30 32 1 13 16 +M V30 33 1 27 30 +M V30 END BOND +M V30 END CTAB +M END +> +616 + +> +Z15685856 + +> +417.480 + +> +4.096 + +> +2 + +> +94.310 + +> +6 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 617 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4958 0.0 0 +M V30 3 C -1.3296 -2.2319 0.0 0 +M V30 4 C 1.2821 -2.2319 0.0 0 +M V30 5 C -1.3415 -3.7277 0.0 0 +M V30 6 C 1.2702 -3.7277 0.0 0 +M V30 7 C -0.0474 -4.4638 0.0 0 +M V30 8 N -0.0593 -5.9597 0.0 0 +M V30 9 C -1.3771 -6.7076 0.0 0 +M V30 10 C 1.2346 -6.7076 0.0 0 +M V30 11 C -1.389 -8.2035 0.0 0 +M V30 12 C 1.2228 -8.2035 0.0 0 +M V30 13 N -0.0949 -8.9514 0.0 0 +M V30 14 C -0.1068 -10.4472 0.0 0 +M V30 15 O -1.4246 -11.1833 0.0 0 +M V30 16 C 1.1871 -11.1833 0.0 0 +M V30 17 O 1.1753 -12.6792 0.0 0 +M V30 18 C 2.4693 -13.4271 0.0 0 +M V30 19 C 3.7634 -12.6554 0.0 0 +M V30 20 C 2.4574 -14.923 0.0 0 +M V30 21 C 3.7515 -11.1358 0.0 0 +M V30 22 C 5.0574 -13.4034 0.0 0 +M V30 23 C 3.7515 -15.659 0.0 0 +M V30 24 O 2.4337 -10.376 0.0 0 +M V30 25 N 5.0455 -10.376 0.0 0 +M V30 26 C 5.0455 -14.8992 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 1 13 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 19 22 +M V30 22 2 20 23 +M V30 23 2 21 24 +M V30 24 1 21 25 +M V30 25 2 22 26 +M V30 26 1 6 7 +M V30 27 1 12 13 +M V30 28 1 23 26 +M V30 END BOND +M V30 END CTAB +M END +> +617 + +> +Z15685807 + +> +357.379 + +> +2.383 + +> +1 + +> +75.870 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3105432 + +> +0.87 + +$$$$ +Compound 618 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.489 0.0 0 +M V30 3 N 1.2762 -2.2217 0.0 0 +M V30 4 C -1.3235 -2.2217 0.0 0 +M V30 5 C -2.6353 -1.4653 0.0 0 +M V30 6 C -1.3353 -3.7107 0.0 0 +M V30 7 O -2.6471 0.0472 0.0 0 +M V30 8 C -3.947 -2.198 0.0 0 +M V30 9 C -2.6471 -4.4552 0.0 0 +M V30 10 C -1.359 0.8154 0.0 0 +M V30 11 C -3.9588 -3.687 0.0 0 +M V30 12 C -1.3708 2.328 0.0 0 +M V30 13 O -2.6825 3.0843 0.0 0 +M V30 14 N -0.0827 3.0843 0.0 0 +M V30 15 C 1.2053 2.3398 0.0 0 +M V30 16 C -0.0945 4.597 0.0 0 +M V30 17 C 2.4935 3.0962 0.0 0 +M V30 18 C 1.1935 5.3533 0.0 0 +M V30 19 C 2.4816 4.6088 0.0 0 +M V30 20 C 3.7816 2.3517 0.0 0 +M V30 21 C 3.7698 5.3651 0.0 0 +M V30 22 C 5.0697 3.108 0.0 0 +M V30 23 C 5.0579 4.6324 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 17 19 +M V30 19 1 17 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 2 21 23 +M V30 23 1 9 11 +M V30 24 1 18 19 +M V30 25 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +618 + +> +Z15685905 + +> +310.347 + +> +1.531 + +> +1 + +> +72.630 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1544030 + +> +1.0 + +$$$$ +Compound 619 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3107 -0.7439 0.0 0 +M V30 3 N -1.3225 -2.2317 0.0 0 +M V30 4 C -2.6214 0.0236 0.0 0 +M V30 5 C -2.6332 1.535 0.0 0 +M V30 6 C -3.9321 -0.7203 0.0 0 +M V30 7 O -1.3461 2.2907 0.0 0 +M V30 8 C -3.9439 2.2907 0.0 0 +M V30 9 C -5.2428 0.0472 0.0 0 +M V30 10 C -0.059 1.5468 0.0 0 +M V30 11 C -5.2546 1.5468 0.0 0 +M V30 12 C 1.228 2.3026 0.0 0 +M V30 13 O 1.2162 3.814 0.0 0 +M V30 14 N 2.5151 1.5586 0.0 0 +M V30 15 C 3.8022 2.3144 0.0 0 +M V30 16 C 5.0893 1.5704 0.0 0 +M V30 17 C 3.7904 3.8258 0.0 0 +M V30 18 C 6.3764 2.3262 0.0 0 +M V30 19 C 5.0775 0.0826 0.0 0 +M V30 20 C 2.4797 4.5815 0.0 0 +M V30 21 C 5.0775 4.5815 0.0 0 +M V30 22 C 7.6635 1.5823 0.0 0 +M V30 23 C 6.3646 -0.6612 0.0 0 +M V30 24 C 2.4679 6.093 0.0 0 +M V30 25 C 5.0657 6.093 0.0 0 +M V30 26 C 7.6517 0.0944 0.0 0 +M V30 27 C 3.755 6.8487 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 17 21 +M V30 21 1 18 22 +M V30 22 2 19 23 +M V30 23 1 20 24 +M V30 24 2 21 25 +M V30 25 2 22 26 +M V30 26 2 24 27 +M V30 27 1 9 11 +M V30 28 1 23 26 +M V30 29 1 25 27 +M V30 END BOND +M V30 END CTAB +M END +> +619 + +> +Z15685945 + +> +360.406 + +> +2.756 + +> +2 + +> +81.420 + +> +7 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3105430 + +> +0.95 + +$$$$ +Compound 620 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 29 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3146 0.7579 0.0 0 +M V30 3 N -2.6292 0.0236 0.0 0 +M V30 4 C -1.3264 2.2739 0.0 0 +M V30 5 C -0.0355 3.0318 0.0 0 +M V30 6 C -2.641 3.0318 0.0 0 +M V30 7 O 1.2553 2.2976 0.0 0 +M V30 8 C -0.0473 4.5478 0.0 0 +M V30 9 C -2.6529 4.5478 0.0 0 +M V30 10 C 2.5463 3.0555 0.0 0 +M V30 11 C -1.3619 5.3058 0.0 0 +M V30 12 C 3.8372 2.3212 0.0 0 +M V30 13 O 3.8253 0.829 0.0 0 +M V30 14 N 5.1281 3.0792 0.0 0 +M V30 15 C 5.1163 4.5952 0.0 0 +M V30 16 C 6.419 2.3449 0.0 0 +M V30 17 C 6.4072 5.3531 0.0 0 +M V30 18 C 7.71 3.1029 0.0 0 +M V30 19 C 6.3954 6.8691 0.0 0 +M V30 20 C 7.6981 4.607 0.0 0 +M V30 21 C 9.0009 2.3686 0.0 0 +M V30 22 C 7.6863 7.6271 0.0 0 +M V30 23 C 8.989 5.365 0.0 0 +M V30 24 C 10.2918 3.1266 0.0 0 +M V30 25 C 8.989 0.8764 0.0 0 +M V30 26 C 8.9772 6.8928 0.0 0 +M V30 27 C 11.5827 2.3923 0.0 0 +M V30 28 C 10.28 0.1421 0.0 0 +M V30 29 C 11.5709 0.9 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 17 19 +M V30 19 1 17 20 +M V30 20 1 18 21 +M V30 21 1 19 22 +M V30 22 2 20 23 +M V30 23 2 21 24 +M V30 24 1 21 25 +M V30 25 2 22 26 +M V30 26 1 24 27 +M V30 27 2 25 28 +M V30 28 2 27 29 +M V30 29 1 9 11 +M V30 30 1 23 26 +M V30 31 1 28 29 +M V30 END BOND +M V30 END CTAB +M END +> +620 + +> +Z15686068 + +> +388.459 + +> +3.758 + +> +1 + +> +72.630 + +> +9 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1544030 + +> +0.94 + +$$$$ +Compound 621 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.281 0.7521 0.0 0 +M V30 3 N 2.562 0.0235 0.0 0 +M V30 4 C 1.2692 2.2565 0.0 0 +M V30 5 C -0.0352 3.0204 0.0 0 +M V30 6 C 2.5503 3.0204 0.0 0 +M V30 7 O -1.3398 2.28 0.0 0 +M V30 8 C -0.047 4.5247 0.0 0 +M V30 9 C 2.5385 4.5247 0.0 0 +M V30 10 C -2.6443 3.0439 0.0 0 +M V30 11 C 1.234 5.2769 0.0 0 +M V30 12 C -3.9489 2.3035 0.0 0 +M V30 13 O -3.9606 0.8226 0.0 0 +M V30 14 N -5.2534 3.0674 0.0 0 +M V30 15 C -6.558 2.327 0.0 0 CFG=2 +M V30 16 C -7.8625 3.0909 0.0 0 +M V30 17 C -6.5697 0.8461 0.0 0 +M V30 18 C -9.1671 2.3505 0.0 0 +M V30 19 C -7.8743 4.5953 0.0 0 +M V30 20 C -10.4716 3.1144 0.0 0 +M V30 21 C -9.1788 5.3474 0.0 0 +M V30 22 C -10.4834 4.6188 0.0 0 +M V30 23 C -11.7879 5.3709 0.0 0 +M V30 24 C -13.0925 4.6423 0.0 0 +M V30 25 C -11.7997 6.8753 0.0 0 +M V30 26 C -14.397 5.3945 0.0 0 +M V30 27 C -13.1042 7.6275 0.0 0 +M V30 28 C -14.4088 6.8988 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 15 14 CFG=1 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 2 20 22 +M V30 22 1 22 23 +M V30 23 2 23 24 +M V30 24 1 23 25 +M V30 25 1 24 26 +M V30 26 2 25 27 +M V30 27 2 26 28 +M V30 28 1 9 11 +M V30 29 1 21 22 +M V30 30 1 27 28 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 15) +M V30 END COLLECTION +M V30 END CTAB +M END +> +621 + +> +Z15686091 + +> +374.432 + +> +3.605 + +> +2 + +> +81.420 + +> +7 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3105430 + +> +0.93 + +$$$$ +Compound 622 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5063 0.0 0 +M V30 3 N -1.318 2.2713 0.0 0 +M V30 4 C 1.2709 2.2713 0.0 0 +M V30 5 C -1.3298 3.7776 0.0 0 +M V30 6 C 1.2592 3.7776 0.0 0 +M V30 7 C 2.5537 1.5299 0.0 0 +M V30 8 N -0.047 4.5308 0.0 0 +M V30 9 C -2.6361 4.5308 0.0 0 +M V30 10 C 2.5419 4.5308 0.0 0 +M V30 11 C 3.8365 2.2948 0.0 0 +M V30 12 O -2.6479 6.0372 0.0 0 +M V30 13 N -3.9424 3.8012 0.0 0 +M V30 14 C 3.8247 3.8012 0.0 0 +M V30 15 C -5.2487 4.5543 0.0 0 CFG=2 +M V30 16 C -5.2605 6.0607 0.0 0 +M V30 17 C -6.555 3.8247 0.0 0 +M V30 18 C -6.5668 6.8139 0.0 0 +M V30 19 C -4.166 7.0728 0.0 0 +M V30 20 C -7.8613 4.5779 0.0 0 +M V30 21 N -6.2725 8.285 0.0 0 +M V30 22 C -7.8731 6.0842 0.0 0 +M V30 23 N -4.7897 8.4497 0.0 0 +M V30 24 C -7.2846 9.403 0.0 0 +M V30 25 C -8.2967 10.521 0.0 0 +M V30 26 C -8.4026 8.4144 0.0 0 +M V30 27 C -6.1902 10.415 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 15 13 CFG=1 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 1 17 20 +M V30 20 1 18 21 +M V30 21 1 18 22 +M V30 22 2 19 23 +M V30 23 1 21 24 +M V30 24 1 24 25 +M V30 25 1 24 26 +M V30 26 1 24 27 +M V30 27 1 6 8 +M V30 28 1 11 14 +M V30 29 1 20 22 +M V30 30 1 21 23 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 15) +M V30 END COLLECTION +M V30 END CTAB +M END +> +622 + +> +Z827566808 + +> +365.429 + +> +2.299 + +> +2 + +> +88.380 + +> +3 + +> +Tankyrase1, parp3 + +> + + +> + + +$$$$ +Compound 623 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 33 36 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5057 0.0 0 +M V30 3 N -1.3175 2.2585 0.0 0 +M V30 4 C 1.2704 2.2585 0.0 0 +M V30 5 C -1.3292 3.7643 0.0 0 +M V30 6 C 1.2586 3.7643 0.0 0 +M V30 7 C 2.5526 1.5174 0.0 0 +M V30 8 N -0.047 4.5171 0.0 0 +M V30 9 C -2.635 4.5171 0.0 0 +M V30 10 C 2.5409 4.5171 0.0 0 +M V30 11 C 3.8348 2.2703 0.0 0 +M V30 12 C -3.9407 3.7878 0.0 0 +M V30 13 C 3.8231 3.7878 0.0 0 +M V30 14 C -5.2464 4.5406 0.0 0 +M V30 15 O -5.2582 6.0464 0.0 0 +M V30 16 N -6.5522 3.8113 0.0 0 +M V30 17 C -7.8579 4.5642 0.0 0 +M V30 18 C -7.8697 6.0699 0.0 0 +M V30 19 C -9.1637 3.8348 0.0 0 +M V30 20 C -9.1754 6.8228 0.0 0 +M V30 21 C -10.4694 4.5877 0.0 0 +M V30 22 C -10.4812 6.0934 0.0 0 +M V30 23 C -11.7869 6.8463 0.0 0 +M V30 24 O -11.7987 8.352 0.0 0 +M V30 25 N -13.0927 6.1169 0.0 0 +M V30 26 C -14.3984 6.8698 0.0 0 +M V30 27 C -15.7042 6.1405 0.0 0 +M V30 28 C -14.4102 8.3755 0.0 0 +M V30 29 O -15.7159 4.6583 0.0 0 +M V30 30 C -17.0099 6.8933 0.0 0 +M V30 31 C -15.7159 9.1284 0.0 0 +M V30 32 C -17.0217 3.9289 0.0 0 +M V30 33 C -17.0217 8.3991 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 2 20 22 +M V30 22 1 22 23 +M V30 23 2 23 24 +M V30 24 1 23 25 +M V30 25 1 25 26 +M V30 26 2 26 27 +M V30 27 1 26 28 +M V30 28 1 27 29 +M V30 29 1 27 30 +M V30 30 2 28 31 +M V30 31 1 29 32 +M V30 32 2 30 33 +M V30 33 1 6 8 +M V30 34 1 11 13 +M V30 35 1 21 22 +M V30 36 1 31 33 +M V30 END BOND +M V30 END CTAB +M END +> +623 + +> +Z26504319 + +> +442.467 + +> +1.709 + +> +3 + +> +108.890 + +> +7 + +> +Poly [ADP-ribose] polymerase 2 + +> +CHEMBL2314698 + +> +1.0 + +$$$$ +Compound 624 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.3145 0.7579 0.0 0 +M V30 3 C -1.3263 2.2737 0.0 0 +M V30 4 C -2.629 0.0236 0.0 0 +M V30 5 C -0.0355 3.0316 0.0 0 +M V30 6 C -2.6408 3.0316 0.0 0 +M V30 7 C -3.9435 0.7815 0.0 0 +M V30 8 O -0.0473 4.5474 0.0 0 +M V30 9 N 1.2552 2.2974 0.0 0 +M V30 10 C -3.9553 2.2974 0.0 0 +M V30 11 F -5.258 0.0473 0.0 0 +M V30 12 C 2.5461 3.0553 0.0 0 +M V30 13 C 3.8369 2.3211 0.0 0 +M V30 14 C 5.1277 3.079 0.0 0 +M V30 15 C 6.4185 2.3447 0.0 0 +M V30 16 N 7.7804 2.9724 0.0 0 +M V30 17 N 6.5606 0.8644 0.0 0 +M V30 18 C 8.7752 1.8711 0.0 0 +M V30 19 C 8.0173 0.5684 0.0 0 +M V30 20 C 10.2673 1.8829 0.0 0 +M V30 21 C 8.7633 -0.7223 0.0 0 +M V30 22 C 11.0134 0.5921 0.0 0 +M V30 23 C 10.2555 -0.7105 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 7 11 +M V30 11 1 9 12 +M V30 12 1 12 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 2 21 23 +M V30 23 1 7 10 +M V30 24 2 18 19 +M V30 25 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +624 + +> +Z196821150 + +> +331.772 + +> +2.828 + +> +2 + +> +57.780 + +> +5 + +> +ATM + +> + + +> + + +$$$$ +Compound 625 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.489 0.0 0 +M V30 3 N -1.3235 -2.2216 0.0 0 +M V30 4 N 1.2762 -2.2216 0.0 0 +M V30 5 C -1.3353 -3.7106 0.0 0 +M V30 6 C -2.6352 -1.4653 0.0 0 +M V30 7 C 1.2644 -3.7106 0.0 0 +M V30 8 C -0.0472 -4.4433 0.0 0 +M V30 9 C -2.6471 -4.4433 0.0 0 +M V30 10 C -3.947 -2.198 0.0 0 +M V30 11 O 2.5525 -4.4433 0.0 0 +M V30 12 C -0.059 -5.9323 0.0 0 +M V30 13 C -2.6589 -5.9323 0.0 0 +M V30 14 C -5.2587 -1.4417 0.0 0 +M V30 15 C -1.3708 -6.665 0.0 0 +M V30 16 O -5.2705 0.0709 0.0 0 +M V30 17 N -6.5705 -2.1744 0.0 0 +M V30 18 C -7.8822 -1.418 0.0 0 +M V30 19 C -6.5823 -3.6634 0.0 0 +M V30 20 C -9.1939 -2.1507 0.0 0 +M V30 21 N -9.3594 -3.6279 0.0 0 +M V30 22 N -10.5766 -1.5244 0.0 0 +M V30 23 N -10.8366 -3.9234 0.0 0 +M V30 24 C -11.5929 -2.6234 0.0 0 +M V30 25 C -11.0493 -0.0827 0.0 0 +M V30 26 C -13.0701 -2.3044 0.0 0 +M V30 27 C -12.5265 0.2363 0.0 0 +M V30 28 C -13.5428 -0.8626 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 10 14 +M V30 14 2 12 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 1 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 2 20 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 22 25 +M V30 25 1 24 26 +M V30 26 2 25 27 +M V30 27 2 26 28 +M V30 28 1 7 8 +M V30 29 1 13 15 +M V30 30 2 23 24 +M V30 31 1 27 28 +M V30 END BOND +M V30 END CTAB +M END +> +625 + +> +Z978980288 + +> +378.385 + +> +0.164 + +> +1 + +> +99.910 + +> +5 + +> +parp3 + +> + + +> + + +$$$$ +Compound 626 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 CFG=2 +M V30 2 O -0.0118 1.5134 0.0 0 +M V30 3 C 1.2888 -0.733 0.0 0 +M V30 4 C -1.3124 -0.733 0.0 0 +M V30 5 C 2.5776 0.0236 0.0 0 +M V30 6 C 1.2769 -2.2229 0.0 0 +M V30 7 C -2.6249 0.0236 0.0 0 +M V30 8 C 3.8664 -0.7094 0.0 0 +M V30 9 C 2.5658 -2.9678 0.0 0 +M V30 10 N -3.9373 -0.7094 0.0 0 +M V30 11 C 3.8546 -2.1992 0.0 0 +M V30 12 C -5.2498 0.0472 0.0 0 +M V30 13 O -5.2616 1.5607 0.0 0 +M V30 14 C -6.5623 -0.6857 0.0 0 +M V30 15 C -6.5741 -2.1756 0.0 0 +M V30 16 C -7.8747 0.0709 0.0 0 +M V30 17 C -7.8866 -2.9205 0.0 0 +M V30 18 C -9.1872 -0.6621 0.0 0 +M V30 19 N -7.8984 -4.4103 0.0 0 CHG=1 +M V30 20 C -9.199 -2.1519 0.0 0 +M V30 21 O -6.6096 -5.1434 0.0 0 +M V30 22 O -9.2109 -5.1434 0.0 0 CHG=-1 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 1 3 +M V30 3 1 1 4 CFG=3 +M V30 4 2 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 17 19 +M V30 19 2 17 20 +M V30 20 2 19 21 +M V30 21 1 19 22 +M V30 22 1 9 11 +M V30 23 1 18 20 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 1) +M V30 END COLLECTION +M V30 END CTAB +M END +> +626 + +> +Z1171303022 + +> +318.348 + +> +1.915 + +> +1 + +> +89.310 + +> +6 + +> +parp14 + +> + + +> + + +$$$$ +Compound 627 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 29 32 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.5057 0.0117 0.0 0 +M V30 3 C 0.447 1.4351 0.0 0 +M V30 4 N -1.9763 1.4469 0.0 0 +M V30 5 C -2.3998 -1.1881 0.0 0 +M V30 6 C -0.7764 2.3292 0.0 0 +M V30 7 C -1.7998 -2.5409 0.0 0 +M V30 8 C -3.8937 -1.0234 0.0 0 +M V30 9 C -0.7881 3.8349 0.0 0 +M V30 10 C -2.6938 -3.7408 0.0 0 +M V30 11 C -4.7878 -2.2233 0.0 0 +M V30 12 O -2.0939 4.5878 0.0 0 +M V30 13 N 0.494 4.5878 0.0 0 +M V30 14 C -4.1878 -3.5761 0.0 0 +M V30 15 C 0.4823 6.0936 0.0 0 +M V30 16 C -5.0819 -4.776 0.0 0 +M V30 17 C 1.7645 6.8464 0.0 0 +M V30 18 C -0.8234 6.8464 0.0 0 +M V30 19 C 1.7527 8.3522 0.0 0 +M V30 20 C -0.8352 8.3522 0.0 0 +M V30 21 C 0.447 9.1051 0.0 0 +M V30 22 N 0.4352 10.6108 0.0 0 +M V30 23 C 1.7175 11.3637 0.0 0 +M V30 24 C -0.8705 11.3637 0.0 0 +M V30 25 C 1.7057 12.8695 0.0 0 +M V30 26 C -0.8822 12.8695 0.0 0 +M V30 27 N 0.3999 13.6223 0.0 0 +M V30 28 C 0.3882 15.1281 0.0 0 +M V30 29 C -0.9175 15.881 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 1 15 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 2 19 21 +M V30 21 1 21 22 +M V30 22 1 22 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 1 24 26 +M V30 26 1 25 27 +M V30 27 1 27 28 +M V30 28 1 28 29 +M V30 29 1 4 6 +M V30 30 1 11 14 +M V30 31 1 20 21 +M V30 32 1 26 27 +M V30 END BOND +M V30 END CTAB +M END +> +627 + +> +Z90819990 + +> +406.544 + +> +5.247 + +> +1 + +> +48.470 + +> +5 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL2037092 + +> +0.95 + +$$$$ +Compound 628 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.3189 -1.4528 0.0 0 +M V30 3 N -1.7008 -2.0552 0.0 0 +M V30 4 C 0.6732 -2.5513 0.0 0 +M V30 5 C -1.5591 -3.5316 0.0 0 +M V30 6 C -3.0119 -1.2992 0.0 0 +M V30 7 C -0.0826 -3.8387 0.0 0 +M V30 8 C -4.323 -2.0434 0.0 0 CFG=2 +M V30 9 N -5.6341 -1.2874 0.0 0 +M V30 10 C -4.3348 -3.5316 0.0 0 +M V30 11 C -6.9452 -2.0316 0.0 0 +M V30 12 C -3.0474 -4.264 0.0 0 +M V30 13 C -5.6459 -4.264 0.0 0 +M V30 14 O -6.957 -3.5198 0.0 0 +M V30 15 C -8.2563 -1.2756 0.0 0 +M V30 16 C -3.0592 -5.7522 0.0 0 +M V30 17 C -5.6577 -5.7522 0.0 0 +M V30 18 C -9.5674 -2.0197 0.0 0 +M V30 19 C -8.2681 0.2362 0.0 0 +M V30 20 C -4.3703 -6.4845 0.0 0 +M V30 21 C -10.8785 -1.2638 0.0 0 +M V30 22 C -9.5792 -3.508 0.0 0 +M V30 23 C -9.5792 0.9921 0.0 0 +M V30 24 C -10.8903 0.248 0.0 0 +M V30 25 C -9.591 2.504 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 6 8 +M V30 8 1 8 9 CFG=1 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 1 10 13 +M V30 13 2 11 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 2 13 17 +M V30 17 2 15 18 +M V30 18 1 15 19 +M V30 19 2 16 20 +M V30 20 1 18 21 +M V30 21 1 18 22 +M V30 22 2 19 23 +M V30 23 2 21 24 +M V30 24 1 23 25 +M V30 25 1 5 7 +M V30 26 1 17 20 +M V30 27 1 23 24 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 8) +M V30 END COLLECTION +M V30 END CTAB +M END +> +628 + +> +Z437058752 + +> +336.427 + +> +3.158 + +> +1 + +> +49.410 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL596390 + +> +0.85 + +$$$$ +Compound 629 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.1189 1.0128 0.0 0 +M V30 3 N -0.9775 2.5086 0.0 0 +M V30 4 C -2.5911 0.7184 0.0 0 +M V30 5 C -2.3555 3.1329 0.0 0 +M V30 6 C 0.3062 3.2624 0.0 0 +M V30 7 C -3.3567 2.0257 0.0 0 +M V30 8 C 0.2944 4.77 0.0 0 CFG=2 +M V30 9 N 1.5782 5.5356 0.0 0 +M V30 10 C -1.0128 5.5356 0.0 0 +M V30 11 C 1.5664 7.0431 0.0 0 +M V30 12 C -2.3202 4.7936 0.0 0 +M V30 13 C -1.0246 7.0431 0.0 0 +M V30 14 O 0.2591 7.7969 0.0 0 +M V30 15 C 2.8502 7.7969 0.0 0 +M V30 16 C -3.6275 5.5591 0.0 0 +M V30 17 C -2.332 7.7969 0.0 0 +M V30 18 C 4.134 7.0549 0.0 0 +M V30 19 C 2.8384 9.3045 0.0 0 +M V30 20 C -3.6393 7.0667 0.0 0 +M V30 21 C 5.4178 7.8087 0.0 0 +M V30 22 C 4.1222 10.0583 0.0 0 +M V30 23 C 5.406 9.3163 0.0 0 +M V30 24 C 6.7016 7.0667 0.0 0 +M V30 25 C 6.6898 10.0701 0.0 0 +M V30 26 C 7.9854 7.8205 0.0 0 +M V30 27 C 7.9736 9.3398 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 6 8 +M V30 8 1 8 9 CFG=1 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 1 10 13 +M V30 13 2 11 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 2 13 17 +M V30 17 2 15 18 +M V30 18 1 15 19 +M V30 19 2 16 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 2 21 23 +M V30 23 1 21 24 +M V30 24 1 23 25 +M V30 25 1 24 26 +M V30 26 1 25 27 +M V30 27 1 5 7 +M V30 28 1 17 20 +M V30 29 1 22 23 +M V30 30 1 26 27 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 8) +M V30 END COLLECTION +M V30 END CTAB +M END +> +629 + +> +Z437056100 + +> +362.465 + +> +4.072 + +> +1 + +> +49.410 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL596390 + +> +0.86 + +$$$$ +Compound 630 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.4143 -0.4478 0.0 0 +M V30 3 N 2.6166 0.4478 0.0 0 +M V30 4 N 1.8622 -1.8622 0.0 0 +M V30 5 C 2.6048 1.9565 0.0 0 +M V30 6 C 3.8188 -0.4243 0.0 0 +M V30 7 C 3.3473 -1.8504 0.0 0 +M V30 8 C 3.8895 2.7108 0.0 0 +M V30 9 C 1.2965 2.7108 0.0 0 +M V30 10 C 3.8777 4.2195 0.0 0 +M V30 11 C 1.2847 4.2195 0.0 0 +M V30 12 C 2.5694 4.9739 0.0 0 +M V30 13 C 2.5576 6.4825 0.0 0 +M V30 14 O 3.8423 7.2369 0.0 0 +M V30 15 N 1.2493 7.2369 0.0 0 +M V30 16 C 1.2375 8.7455 0.0 0 +M V30 17 C 2.5223 9.4999 0.0 0 +M V30 18 C -0.0707 9.4999 0.0 0 +M V30 19 N 2.5105 11.0085 0.0 0 +M V30 20 C -0.0825 11.0085 0.0 0 +M V30 21 C 1.2022 11.7629 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 2 10 12 +M V30 12 1 12 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 2 19 21 +M V30 21 1 6 7 +M V30 22 1 11 12 +M V30 23 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +630 + +> +Z218690472 + +> +282.297 + +> +0.868 + +> +2 + +> +74.330 + +> +3 + +> +Tankyrase-1 + +> +CHEMBL2381963 + +> +0.87 + +$$$$ +Compound 631 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 29 32 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.4849 -0.1425 0.0 0 +M V30 3 C 2.0907 -1.5086 0.0 0 +M V30 4 C 2.3639 1.0929 0.0 0 +M V30 5 C 3.5756 -1.6512 0.0 0 +M V30 6 C 3.8489 0.9503 0.0 0 +M V30 7 C 4.4547 -0.4157 0.0 0 +M V30 8 N 5.9396 -0.5583 0.0 0 +M V30 9 N 6.6762 -1.8531 0.0 0 +M V30 10 C 6.9375 0.5702 0.0 0 +M V30 11 C 8.1373 -1.5324 0.0 0 +M V30 12 C 8.3036 -0.0356 0.0 0 +M V30 13 C 6.6168 2.0551 0.0 0 +M V30 14 C 9.5985 0.7246 0.0 0 CFG=1 +M V30 15 N 10.8933 -0.0118 0.0 0 +M V30 16 C 9.5866 2.2452 0.0 0 +M V30 17 C 12.1882 0.7484 0.0 0 +M V30 18 O 12.1763 2.2689 0.0 0 +M V30 19 C 13.483 0.0118 0.0 0 +M V30 20 N 13.4712 -1.4849 0.0 0 +M V30 21 C 14.7779 0.7721 0.0 0 +M V30 22 C 14.766 -2.2333 0.0 0 +M V30 23 C 16.0728 0.0356 0.0 0 +M V30 24 O 14.7541 -3.7301 0.0 0 +M V30 25 C 16.0609 -1.4611 0.0 0 +M V30 26 C 17.3676 0.7959 0.0 0 +M V30 27 C 17.3557 -2.2095 0.0 0 +M V30 28 C 18.6625 0.0593 0.0 0 +M V30 29 C 18.6506 -1.4374 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 2 10 12 +M V30 12 1 10 13 +M V30 13 1 12 14 +M V30 14 1 14 15 CFG=3 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 2 19 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 2 22 24 +M V30 24 1 22 25 +M V30 25 1 23 26 +M V30 26 1 25 27 +M V30 27 2 26 28 +M V30 28 2 27 29 +M V30 29 1 6 7 +M V30 30 1 11 12 +M V30 31 2 23 25 +M V30 32 1 28 29 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 14) +M V30 END COLLECTION +M V30 END CTAB +M END +> +631 + +> +Z366330142 + +> +390.410 + +> +2.894 + +> +2 + +> +76.020 + +> +4 + +> +Tankyrase1, parp3 + +> + + +> + + +$$$$ +Compound 632 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.5127 0.0 0 +M V30 3 N -1.3236 2.2691 0.0 0 +M V30 4 C 1.2763 2.2691 0.0 0 +M V30 5 N -1.3354 3.7819 0.0 0 +M V30 6 C -2.6355 1.5245 0.0 0 +M V30 7 C 1.2645 3.7819 0.0 0 +M V30 8 C 2.5646 1.5245 0.0 0 +M V30 9 C -0.0472 4.5382 0.0 0 +M V30 10 C -2.6473 0.0354 0.0 0 +M V30 11 C 2.5527 4.5382 0.0 0 +M V30 12 C 3.8528 2.2809 0.0 0 +M V30 13 O -1.3591 -0.7091 0.0 0 +M V30 14 N -3.9591 -0.7091 0.0 0 +M V30 15 C 3.8409 3.8055 0.0 0 +M V30 16 C -3.971 -2.1982 0.0 0 +M V30 17 C -5.2828 -2.9309 0.0 0 +M V30 18 C -2.6827 -2.9309 0.0 0 +M V30 19 C -5.2946 -4.4201 0.0 0 +M V30 20 C -2.6946 -4.4201 0.0 0 +M V30 21 C -6.6065 -5.1646 0.0 0 +M V30 22 C -4.0064 -5.1646 0.0 0 +M V30 23 O -7.9892 -4.5382 0.0 0 +M V30 24 N -6.7719 -6.6419 0.0 0 +M V30 25 C -9.0056 -5.6374 0.0 0 +M V30 26 N -8.2492 -6.9374 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 2 11 15 +M V30 15 1 14 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 1 19 21 +M V30 21 2 19 22 +M V30 22 1 21 23 +M V30 23 2 21 24 +M V30 24 1 23 25 +M V30 25 1 24 26 +M V30 26 1 7 9 +M V30 27 1 12 15 +M V30 28 1 20 22 +M V30 29 2 25 26 +M V30 END BOND +M V30 END CTAB +M END +> +632 + +> +Z354554712 + +> +347.328 + +> +-0.653 + +> +1 + +> +100.690 + +> +4 + +> +DNA_pk + +> + + +> + + +$$$$ +Compound 633 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3054 0.7526 0.0 0 +M V30 3 N -2.6108 0.0235 0.0 0 +M V30 4 C -1.3171 2.258 0.0 0 +M V30 5 C -3.9162 0.7761 0.0 0 +M V30 6 C -2.6226 3.0107 0.0 0 +M V30 7 C -0.0352 3.0107 0.0 0 +M V30 8 N -3.928 2.2815 0.0 0 +M V30 9 C -5.2216 0.047 0.0 0 +M V30 10 C -2.6343 4.516 0.0 0 +M V30 11 C -0.047 4.516 0.0 0 +M V30 12 C -6.5271 0.7997 0.0 0 +M V30 13 C -1.3524 5.2687 0.0 0 +M V30 14 C -7.8325 0.0705 0.0 0 +M V30 15 O -7.8443 -1.4112 0.0 0 +M V30 16 N -9.1379 0.8232 0.0 0 +M V30 17 C -10.4433 0.094 0.0 0 +M V30 18 C -11.7488 0.8467 0.0 0 +M V30 19 C -13.0542 0.1176 0.0 0 CFG=1 +M V30 20 C -14.3596 0.8702 0.0 0 +M V30 21 C -13.066 -1.3642 0.0 0 +M V30 22 C -15.665 0.1411 0.0 0 +M V30 23 C -14.3714 2.3756 0.0 0 +M V30 24 C -16.9705 0.8938 0.0 0 +M V30 25 C -15.6768 3.1283 0.0 0 +M V30 26 C -16.9822 2.3991 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 1 18 19 +M V30 19 1 19 20 +M V30 20 1 19 21 CFG=3 +M V30 21 2 20 22 +M V30 22 1 20 23 +M V30 23 1 22 24 +M V30 24 2 23 25 +M V30 25 2 24 26 +M V30 26 1 6 8 +M V30 27 1 11 13 +M V30 28 1 25 26 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 19) +M V30 END COLLECTION +M V30 END CTAB +M END +> +633 + +> +Z982731688 + +> +349.426 + +> +2.057 + +> +2 + +> +70.560 + +> +7 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL249813 + +> +0.9 + +$$$$ +Compound 634 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 30 32 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4962 0.0 0 +M V30 3 C -1.33 -2.2325 0.0 0 +M V30 4 C 1.2825 -2.2325 0.0 0 +M V30 5 F -2.6481 -1.4725 0.0 0 +M V30 6 C -1.3418 -3.7287 0.0 0 +M V30 7 C 1.2706 -3.7287 0.0 0 +M V30 8 C -0.0475 -4.4768 0.0 0 +M V30 9 N 2.565 -4.4768 0.0 0 +M V30 10 C 3.8593 -3.7168 0.0 0 +M V30 11 C 2.5531 -5.9731 0.0 0 +M V30 12 S 3.8475 -2.1968 0.0 0 +M V30 13 N 5.1537 -4.465 0.0 0 +M V30 14 O 1.235 -6.7093 0.0 0 +M V30 15 C 3.8475 -6.7093 0.0 0 +M V30 16 C 5.1418 -1.4368 0.0 0 CFG=2 +M V30 17 C 5.1418 -5.9612 0.0 0 +M V30 18 C 3.8356 -8.2056 0.0 0 +M V30 19 C 6.4362 -2.1731 0.0 0 +M V30 20 C 5.13 0.0831 0.0 0 +M V30 21 C 6.4362 -6.6975 0.0 0 +M V30 22 C 5.13 -8.9418 0.0 0 +M V30 23 O 6.4243 -3.6693 0.0 0 +M V30 24 N 7.7306 -1.4131 0.0 0 +M V30 25 C 6.4243 0.8431 0.0 0 +M V30 26 C 3.8118 0.8431 0.0 0 +M V30 27 C 6.4243 -8.1937 0.0 0 +M V30 28 C 9.025 -2.1493 0.0 0 +M V30 29 O 9.0131 -3.6456 0.0 0 +M V30 30 N 10.3193 -1.3893 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 1 9 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 2 10 13 +M V30 13 2 11 14 +M V30 14 1 11 15 +M V30 15 1 16 12 CFG=1 +M V30 16 1 13 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 1 16 20 +M V30 20 1 17 21 +M V30 21 2 18 22 +M V30 22 2 19 23 +M V30 23 1 19 24 +M V30 24 1 20 25 +M V30 25 1 20 26 +M V30 26 2 21 27 +M V30 27 1 24 28 +M V30 28 2 28 29 +M V30 29 1 28 30 +M V30 30 1 7 8 +M V30 31 2 15 17 +M V30 32 1 22 27 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 16) +M V30 END COLLECTION +M V30 END CTAB +M END +> +634 + +> +Z15898160 + +> +448.898 + +> +4.244 + +> +2 + +> +104.860 + +> +5 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL461051 + +> +0.86 + +$$$$ +Compound 635 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.3779 0.5535 0.0 0 +M V30 3 N 2.4614 -0.4475 0.0 0 +M V30 4 C 1.4721 2.0609 0.0 0 CFG=1 +M V30 5 C 3.9335 -0.2119 0.0 0 +M V30 6 N 0.1648 2.8147 0.0 0 +M V30 7 C 2.6969 2.9207 0.0 0 +M V30 8 C 4.6637 1.0952 0.0 0 +M V30 9 C -1.1423 2.0845 0.0 0 +M V30 10 C 4.1102 2.4967 0.0 0 +M V30 11 O -1.1541 0.6006 0.0 0 +M V30 12 N -2.4496 2.8382 0.0 0 +M V30 13 C -3.7568 2.108 0.0 0 +M V30 14 C -2.4614 4.3457 0.0 0 +M V30 15 C -5.0641 2.8618 0.0 0 +M V30 16 N -6.3714 2.1316 0.0 0 +M V30 17 N -5.0759 4.3692 0.0 0 +M V30 18 C -7.6786 2.8853 0.0 0 +M V30 19 C -6.3831 5.123 0.0 0 +M V30 20 C -7.6904 4.3928 0.0 0 +M V30 21 C -8.9859 2.1552 0.0 0 +M V30 22 O -6.3949 6.6304 0.0 0 +M V30 23 C -8.9976 5.1465 0.0 0 +M V30 24 C -10.2931 2.9089 0.0 0 +M V30 25 C -10.3049 4.4046 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 CFG=1 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 9 11 +M V30 11 1 9 12 +M V30 12 1 12 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 1 20 23 +M V30 23 2 21 24 +M V30 24 2 23 25 +M V30 25 1 8 10 +M V30 26 1 19 20 +M V30 27 1 24 25 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 4) +M V30 END COLLECTION +M V30 END CTAB +M END +> +635 + +> +Z505749556 + +> +343.380 + +> +-0.248 + +> +3 + +> +102.900 + +> +3 + +> +parp15 + +> + + +> + + +$$$$ +Compound 636 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 32 35 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2873 -0.744 0.0 0 +M V30 3 C 1.2755 -2.2321 0.0 0 +M V30 4 C 2.5746 0.0118 0.0 0 +M V30 5 F -0.0354 -2.9761 0.0 0 +M V30 6 C 2.5628 -2.9761 0.0 0 +M V30 7 C 3.8619 -0.7322 0.0 0 +M V30 8 C 3.8501 -2.2085 0.0 0 +M V30 9 N 5.1492 0.0236 0.0 0 +M V30 10 C 5.1374 1.5353 0.0 0 +M V30 11 C 6.4366 -0.7204 0.0 0 +M V30 12 S 3.8265 2.2912 0.0 0 +M V30 13 N 6.4248 2.2912 0.0 0 +M V30 14 O 6.4248 -2.2085 0.0 0 +M V30 15 C 7.7239 0.0354 0.0 0 +M V30 16 C 3.8147 3.8029 0.0 0 +M V30 17 C 7.7121 1.5589 0.0 0 +M V30 18 C 9.0112 -0.7086 0.0 0 +M V30 19 C 2.5037 4.5587 0.0 0 +M V30 20 C 8.9994 2.3148 0.0 0 +M V30 21 C 10.2985 0.0472 0.0 0 +M V30 22 O 1.1928 3.8265 0.0 0 +M V30 23 N 2.4919 6.0705 0.0 0 +M V30 24 C 10.2867 1.5825 0.0 0 +M V30 25 C 3.7793 6.8263 0.0 0 +M V30 26 C 1.181 6.8263 0.0 0 +M V30 27 C 3.7674 8.338 0.0 0 +M V30 28 C 1.1692 8.338 0.0 0 +M V30 29 C 2.4565 9.1057 0.0 0 +M V30 30 C 2.4447 10.6174 0.0 0 +M V30 31 O 3.732 11.3733 0.0 0 +M V30 32 N 1.1337 11.3733 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 1 9 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 2 10 13 +M V30 13 2 11 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 1 13 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 2 19 22 +M V30 22 1 19 23 +M V30 23 2 20 24 +M V30 24 1 23 25 +M V30 25 1 23 26 +M V30 26 1 25 27 +M V30 27 1 26 28 +M V30 28 1 27 29 +M V30 29 1 29 30 +M V30 30 2 30 31 +M V30 31 1 30 32 +M V30 32 1 7 8 +M V30 33 2 15 17 +M V30 34 1 21 24 +M V30 35 1 28 29 +M V30 END BOND +M V30 END CTAB +M END +> +636 + +> +Z15898819 + +> +474.936 + +> +2.126 + +> +1 + +> +96.070 + +> +5 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL461264 + +> +0.86 + +$$$$ +Compound 637 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.497 0.0 0 +M V30 3 C -1.3306 -2.2336 0.0 0 +M V30 4 C 1.2831 -2.2336 0.0 0 +M V30 5 F -2.6494 -1.4732 0.0 0 +M V30 6 C -1.3425 -3.7306 0.0 0 +M V30 7 C 1.2712 -3.7306 0.0 0 +M V30 8 C -0.0475 -4.4791 0.0 0 +M V30 9 N 2.5662 -4.4791 0.0 0 +M V30 10 C 3.8613 -3.7187 0.0 0 +M V30 11 C 2.5544 -5.9761 0.0 0 +M V30 12 S 3.8494 -2.1979 0.0 0 +M V30 13 N 5.1563 -4.4672 0.0 0 +M V30 14 O 1.2356 -6.7127 0.0 0 +M V30 15 C 3.8494 -6.7127 0.0 0 +M V30 16 C 5.1444 -1.4375 0.0 0 CFG=2 +M V30 17 C 5.1444 -5.9642 0.0 0 +M V30 18 C 3.8375 -8.2097 0.0 0 +M V30 19 C 6.4394 -2.1742 0.0 0 +M V30 20 C 5.1325 0.0831 0.0 0 +M V30 21 C 6.4394 -6.7008 0.0 0 +M V30 22 C 5.1325 -8.9463 0.0 0 +M V30 23 O 6.4276 -3.6712 0.0 0 +M V30 24 N 7.7345 -1.4138 0.0 0 +M V30 25 C 6.4276 -8.1978 0.0 0 +M V30 26 C 9.0295 -2.1504 0.0 0 +M V30 27 O 9.0176 -3.6474 0.0 0 +M V30 28 N 10.3245 -1.39 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 1 9 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 2 10 13 +M V30 13 2 11 14 +M V30 14 1 11 15 +M V30 15 1 16 12 CFG=1 +M V30 16 1 13 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 1 16 20 +M V30 20 1 17 21 +M V30 21 2 18 22 +M V30 22 2 19 23 +M V30 23 1 19 24 +M V30 24 2 21 25 +M V30 25 1 24 26 +M V30 26 2 26 27 +M V30 27 1 26 28 +M V30 28 1 7 8 +M V30 29 2 15 17 +M V30 30 1 22 25 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 16) +M V30 END COLLECTION +M V30 END CTAB +M END +> +637 + +> +Z15898104 + +> +420.845 + +> +3.316 + +> +2 + +> +104.860 + +> +4 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 638 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.497 0.0 0 +M V30 3 N 1.2832 -2.2337 0.0 0 +M V30 4 C -1.3307 -2.2337 0.0 0 +M V30 5 C 1.2713 -3.7308 0.0 0 +M V30 6 O -1.3426 -3.7308 0.0 0 +M V30 7 C -0.0475 -4.4675 0.0 0 +M V30 8 C 2.5664 -4.4675 0.0 0 +M V30 9 C -0.0594 -5.9646 0.0 0 +M V30 10 C 2.5545 -5.9646 0.0 0 +M V30 11 C 1.2356 -6.7131 0.0 0 +M V30 12 C 3.8496 -6.7131 0.0 0 CFG=1 +M V30 13 N 5.1447 -5.9527 0.0 0 +M V30 14 C 3.8377 -8.2102 0.0 0 +M V30 15 C 6.4398 -6.7012 0.0 0 +M V30 16 O 6.428 -8.1983 0.0 0 +M V30 17 N 7.735 -5.9408 0.0 0 +M V30 18 C 9.0301 -6.6894 0.0 0 +M V30 19 C 10.3252 -5.9289 0.0 0 +M V30 20 C 11.6203 -6.6775 0.0 0 +M V30 21 N 12.9154 -5.917 0.0 0 +M V30 22 N 14.2818 -6.523 0.0 0 +M V30 23 C 13.058 -4.4081 0.0 0 +M V30 24 C 15.2798 -5.3943 0.0 0 +M V30 25 C 14.5194 -4.0873 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 7 9 +M V30 9 2 8 10 +M V30 10 2 9 11 +M V30 11 1 10 12 +M V30 12 1 12 13 CFG=1 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 17 18 +M V30 18 1 18 19 +M V30 19 1 19 20 +M V30 20 1 20 21 +M V30 21 1 21 22 +M V30 22 1 21 23 +M V30 23 2 22 24 +M V30 24 2 23 25 +M V30 25 1 6 7 +M V30 26 1 10 11 +M V30 27 1 24 25 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 12) +M V30 END COLLECTION +M V30 END CTAB +M END +> +638 + +> +Z436607002 + +> +343.380 + +> +0.901 + +> +3 + +> +97.280 + +> +6 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 639 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4961 0.0 0 +M V30 3 N 1.2824 -2.2442 0.0 0 +M V30 4 C -1.3299 -2.2442 0.0 0 +M V30 5 C 1.2705 -3.7404 0.0 0 +M V30 6 C -1.3418 -3.7404 0.0 0 +M V30 7 C -2.6479 -1.4842 0.0 0 +M V30 8 N -0.0474 -4.4766 0.0 0 +M V30 9 C 2.5648 -4.4766 0.0 0 +M V30 10 C -2.6598 -4.4766 0.0 0 +M V30 11 C -3.966 -2.2323 0.0 0 +M V30 12 C 3.8591 -3.7166 0.0 0 +M V30 13 C -3.9779 -3.7166 0.0 0 +M V30 14 C 5.1534 -4.4528 0.0 0 +M V30 15 O 5.1416 -5.949 0.0 0 +M V30 16 O 6.4477 -3.6929 0.0 0 +M V30 17 C 7.7421 -4.4291 0.0 0 +M V30 18 C 9.0364 -3.6691 0.0 0 +M V30 19 O 9.0245 -2.1492 0.0 0 +M V30 20 N 10.3307 -4.4054 0.0 0 +M V30 21 C 11.625 -3.6454 0.0 0 +M V30 22 C 12.9193 -4.3816 0.0 0 CFG=2 +M V30 23 O 13.0618 -5.8659 0.0 0 +M V30 24 C 14.2849 -3.7523 0.0 0 +M V30 25 C 14.5223 -6.1628 0.0 0 +M V30 26 C 15.2823 -4.8566 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 20 21 +M V30 21 1 22 21 CFG=1 +M V30 22 1 22 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 1 24 26 +M V30 26 1 6 8 +M V30 27 1 11 13 +M V30 28 1 25 26 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 22) +M V30 END COLLECTION +M V30 END CTAB +M END +> +639 + +> +Z15966375 + +> +359.376 + +> +0.320 + +> +2 + +> +106.090 + +> +8 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3105401 + +> +0.86 + +$$$$ +Compound 640 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4959 0.0 0 +M V30 3 N 1.2822 -2.2439 0.0 0 +M V30 4 C -1.3297 -2.2439 0.0 0 +M V30 5 C 1.2703 -3.7398 0.0 0 +M V30 6 C -1.3415 -3.7398 0.0 0 +M V30 7 C -2.6475 -1.484 0.0 0 +M V30 8 N -0.0474 -4.4759 0.0 0 +M V30 9 C 2.5644 -4.4759 0.0 0 +M V30 10 C -2.6594 -4.4759 0.0 0 +M V30 11 C -3.9654 -2.232 0.0 0 +M V30 12 C 3.8585 -3.7161 0.0 0 +M V30 13 C -3.9773 -3.7161 0.0 0 +M V30 14 C 5.1526 -4.4522 0.0 0 +M V30 15 O 5.1408 -5.9481 0.0 0 +M V30 16 O 6.4468 -3.6923 0.0 0 +M V30 17 C 7.7409 -4.4284 0.0 0 CFG=2 +M V30 18 C 9.035 -3.6686 0.0 0 +M V30 19 C 7.729 -5.9244 0.0 0 +M V30 20 O 9.0231 -2.1489 0.0 0 +M V30 21 N 10.3291 -4.4047 0.0 0 +M V30 22 C 11.6944 -3.7754 0.0 0 +M V30 23 C 10.4716 -5.8887 0.0 0 +M V30 24 C 12.6917 -4.8796 0.0 0 +M V30 25 C 11.9319 -6.1856 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 17 16 CFG=3 +M V30 17 1 17 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 1 18 21 +M V30 21 1 21 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 1 6 8 +M V30 26 1 11 13 +M V30 27 1 24 25 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 17) +M V30 END COLLECTION +M V30 END CTAB +M END +> +640 + +> +Z15965841 + +> +343.377 + +> +0.566 + +> +1 + +> +88.070 + +> +6 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3105401 + +> +0.86 + +$$$$ +Compound 641 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 34 38 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.29 -0.7337 0.0 0 +M V30 3 C -1.3136 -0.7337 0.0 0 +M V30 4 N 2.58 0.0236 0.0 0 +M V30 5 N 1.2781 -2.2249 0.0 0 +M V30 6 C -2.6273 0.0236 0.0 0 +M V30 7 C 3.87 -0.7101 0.0 0 +M V30 8 C 2.5682 -2.9705 0.0 0 +M V30 9 C -0.0355 -2.9705 0.0 0 +M V30 10 O -2.6392 1.5385 0.0 0 +M V30 11 N -3.941 -0.7101 0.0 0 +M V30 12 C 3.8582 -2.2013 0.0 0 +M V30 13 C 5.16 0.0473 0.0 0 +M V30 14 O 2.5563 -4.4618 0.0 0 +M V30 15 C -0.0473 -4.4618 0.0 0 +M V30 16 C -5.2547 0.0473 0.0 0 +M V30 17 C -3.9529 -2.2013 0.0 0 +M V30 18 C 5.1482 -2.9469 0.0 0 +M V30 19 C 6.45 -0.6864 0.0 0 +M V30 20 C -1.361 -5.2074 0.0 0 +M V30 21 C 1.2426 -5.2074 0.0 0 +M V30 22 C -6.5684 -0.6864 0.0 0 +M V30 23 C -5.2665 1.5622 0.0 0 +M V30 24 C -5.2665 -2.9469 0.0 0 +M V30 25 C 6.4382 -2.1894 0.0 0 +M V30 26 C -1.3728 -6.6986 0.0 0 +M V30 27 C 1.2308 -6.6986 0.0 0 +M V30 28 N -6.5802 -2.1776 0.0 0 +M V30 29 C -7.8821 0.071 0.0 0 +M V30 30 C -6.5802 2.3196 0.0 0 +M V30 31 O -5.2784 -4.4381 0.0 0 +M V30 32 C -0.0828 -7.4324 0.0 0 +M V30 33 C -7.8939 1.5858 0.0 0 +M V30 34 C -0.0946 -8.9236 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 2 7 12 +M V30 12 1 7 13 +M V30 13 2 8 14 +M V30 14 1 9 15 +M V30 15 1 11 16 +M V30 16 1 11 17 +M V30 17 1 12 18 +M V30 18 2 13 19 +M V30 19 2 15 20 +M V30 20 1 15 21 +M V30 21 2 16 22 +M V30 22 1 16 23 +M V30 23 1 17 24 +M V30 24 2 18 25 +M V30 25 1 20 26 +M V30 26 2 21 27 +M V30 27 1 22 28 +M V30 28 1 22 29 +M V30 29 2 23 30 +M V30 30 2 24 31 +M V30 31 2 26 32 +M V30 32 2 29 33 +M V30 33 1 32 34 +M V30 34 1 8 12 +M V30 35 1 19 25 +M V30 36 1 24 28 +M V30 37 1 27 32 +M V30 38 1 30 33 +M V30 END BOND +M V30 END CTAB +M END +> +641 + +> +Z15971903 + +> +470.543 + +> +4.002 + +> +1 + +> +82.080 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3105415 + +> +0.94 + +$$$$ +Compound 642 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2915 0.7583 0.0 0 +M V30 3 C 2.5831 0.0236 0.0 0 +M V30 4 C 1.2797 2.275 0.0 0 +M V30 5 C 3.8747 0.782 0.0 0 +M V30 6 C 2.5713 3.0452 0.0 0 +M V30 7 C 3.8628 2.2987 0.0 0 +M V30 8 C 5.1544 3.0689 0.0 0 +M V30 9 N 6.446 2.3224 0.0 0 +M V30 10 C 7.7376 3.0926 0.0 0 +M V30 11 C 6.4342 0.8294 0.0 0 +M V30 12 O 7.7257 4.6094 0.0 0 +M V30 13 C 9.0292 2.3461 0.0 0 +M V30 14 N 10.3208 3.1163 0.0 0 +M V30 15 C 11.6123 2.3698 0.0 0 +M V30 16 C 10.3089 4.6331 0.0 0 +M V30 17 O 11.6005 0.8768 0.0 0 +M V30 18 C 12.9039 3.14 0.0 0 +M V30 19 N 11.6005 5.3914 0.0 0 +M V30 20 C 12.8921 4.6568 0.0 0 +M V30 21 C 14.1955 2.3935 0.0 0 +M V30 22 C 14.1837 5.4151 0.0 0 +M V30 23 C 15.4871 3.1637 0.0 0 +M V30 24 C 15.4752 4.6686 0.0 0 +M V30 25 N 16.7787 2.4172 0.0 0 CHG=1 +M V30 26 O 18.0703 3.1874 0.0 0 +M V30 27 O 16.7668 0.9242 0.0 0 CHG=-1 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 1 9 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 1 10 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 1 15 18 +M V30 18 2 16 19 +M V30 19 2 18 20 +M V30 20 1 18 21 +M V30 21 1 20 22 +M V30 22 2 21 23 +M V30 23 2 22 24 +M V30 24 1 23 25 +M V30 25 2 25 26 +M V30 26 1 25 27 +M V30 27 1 6 7 +M V30 28 1 19 20 +M V30 29 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +642 + +> +Z15989559 + +> +386.789 + +> +2.596 + +> +0 + +> +96.120 + +> +5 + +> +parp14 + +> + + +> + + +$$$$ +Compound 643 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 30 32 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.293 0.771 0.0 0 +M V30 3 C 2.586 0.0237 0.0 0 +M V30 4 C 1.2811 2.2894 0.0 0 +M V30 5 C 3.879 0.7947 0.0 0 +M V30 6 C 2.5741 3.0486 0.0 0 +M V30 7 C 3.8671 2.3131 0.0 0 +M V30 8 N 5.1601 3.0723 0.0 0 +M V30 9 C 6.4532 2.3369 0.0 0 +M V30 10 O 6.4413 0.8422 0.0 0 +M V30 11 C 7.7462 3.0961 0.0 0 +M V30 12 N 9.0392 2.3606 0.0 0 +M V30 13 C 10.3322 3.1198 0.0 0 +M V30 14 C 9.0273 0.8659 0.0 0 +M V30 15 O 10.3203 4.6382 0.0 0 +M V30 16 C 11.6252 2.3843 0.0 0 +M V30 17 N 12.9182 3.1435 0.0 0 +M V30 18 C 14.2112 2.408 0.0 0 +M V30 19 C 12.9064 4.6619 0.0 0 +M V30 20 O 14.1994 0.9134 0.0 0 +M V30 21 C 15.5042 3.1672 0.0 0 +M V30 22 N 14.1994 5.433 0.0 0 +M V30 23 C 15.4924 4.6856 0.0 0 +M V30 24 C 16.7973 2.4318 0.0 0 +M V30 25 C 16.7854 5.4567 0.0 0 +M V30 26 C 18.0903 3.191 0.0 0 +M V30 27 C 18.0784 4.7094 0.0 0 +M V30 28 N 19.3833 2.4555 0.0 0 CHG=1 +M V30 29 O 20.6763 3.2147 0.0 0 +M V30 30 O 19.3714 0.9608 0.0 0 CHG=-1 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 1 12 14 +M V30 14 2 13 15 +M V30 15 1 13 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 2 21 23 +M V30 23 1 21 24 +M V30 24 1 23 25 +M V30 25 2 24 26 +M V30 26 2 25 27 +M V30 27 1 26 28 +M V30 28 2 28 29 +M V30 29 1 28 30 +M V30 30 1 6 7 +M V30 31 1 22 23 +M V30 32 1 26 27 +M V30 END BOND +M V30 END CTAB +M END +> +643 + +> +Z15989423 + +> +413.359 + +> +2.022 + +> +1 + +> +125.220 + +> +6 + +> +parp14 + +> + + +> + + +$$$$ +Compound 644 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 N -1.3021 -0.739 0.0 0 CHG=1 +M V30 3 O -1.3138 -2.2171 0.0 0 CHG=-1 +M V30 4 C -2.6042 0.0234 0.0 0 +M V30 5 C -3.9064 -0.7155 0.0 0 +M V30 6 C -2.616 1.525 0.0 0 +M V30 7 C -5.2085 0.0469 0.0 0 +M V30 8 C -3.9181 2.2875 0.0 0 +M V30 9 C -6.5106 -0.6921 0.0 0 +M V30 10 C -5.2202 1.5484 0.0 0 +M V30 11 O -6.5224 -2.1702 0.0 0 +M V30 12 N -7.8128 0.0703 0.0 0 +M V30 13 N -6.5224 2.3109 0.0 0 +M V30 14 C -7.8245 1.5719 0.0 0 +M V30 15 C -9.1149 -0.6686 0.0 0 +M V30 16 C -10.417 0.0938 0.0 0 +M V30 17 O -10.4288 1.5954 0.0 0 +M V30 18 N -11.7192 -0.6452 0.0 0 +M V30 19 C -13.0213 0.1173 0.0 0 +M V30 20 C -11.7309 -2.1233 0.0 0 +M V30 21 C -14.3234 -0.6217 0.0 0 +M V30 22 C -13.033 1.6188 0.0 0 +M V30 23 C -15.6256 0.1407 0.0 0 +M V30 24 C -14.3352 2.3813 0.0 0 +M V30 25 C -15.6373 1.6423 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 2 7 10 +M V30 10 2 9 11 +M V30 11 1 9 12 +M V30 12 1 10 13 +M V30 13 1 12 14 +M V30 14 1 12 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 18 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 1 19 22 +M V30 22 1 21 23 +M V30 23 2 22 24 +M V30 24 2 23 25 +M V30 25 1 8 10 +M V30 26 2 13 14 +M V30 27 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +644 + +> +Z15989442 + +> +338.317 + +> +1.810 + +> +0 + +> +96.120 + +> +4 + +> +parp14 + +> + + +> + + +$$$$ +Compound 645 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.3042 0.7637 0.0 0 +M V30 3 C -1.3159 2.2677 0.0 0 +M V30 4 C -2.6084 0.0234 0.0 0 +M V30 5 C -0.0352 3.0197 0.0 0 +M V30 6 C -2.6202 3.0197 0.0 0 +M V30 7 C -3.9127 0.7872 0.0 0 +M V30 8 O 1.2454 2.2912 0.0 0 +M V30 9 N -0.0469 4.5237 0.0 0 +M V30 10 C -3.9244 2.2912 0.0 0 +M V30 11 N -5.2169 0.0469 0.0 0 +M V30 12 C -6.5212 0.8107 0.0 0 +M V30 13 O -6.5329 2.3147 0.0 0 +M V30 14 N -7.8254 0.0704 0.0 0 +M V30 15 C -9.1297 0.8342 0.0 0 +M V30 16 C -10.4339 0.0939 0.0 0 +M V30 17 C -11.7382 0.8577 0.0 0 +M V30 18 N -13.0424 0.1174 0.0 0 +M V30 19 C -14.3467 0.8812 0.0 0 +M V30 20 C -13.0542 -1.3629 0.0 0 +M V30 21 C -15.6509 0.1409 0.0 0 +M V30 22 F -16.9552 -0.5874 0.0 0 +M V30 23 F -16.4029 1.4452 0.0 0 +M V30 24 F -14.9107 -1.1397 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 7 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 1 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 21 22 +M V30 22 1 21 23 +M V30 23 1 21 24 +M V30 24 1 7 10 +M V30 END BOND +M V30 END CTAB +M END +> +645 + +> +Z416208888 + +> +366.767 + +> +1.433 + +> +3 + +> +87.460 + +> +8 + +> +parp14 + +> + + +> + + +$$$$ +Compound 646 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4921 0.0 0 +M V30 3 N 1.2789 -2.2263 0.0 0 +M V30 4 C -1.3263 -2.2263 0.0 0 +M V30 5 C 1.2671 -3.7184 0.0 0 +M V30 6 C 2.5697 -1.4684 0.0 0 +M V30 7 C -1.3381 -3.7184 0.0 0 +M V30 8 C -2.6408 -1.4684 0.0 0 +M V30 9 C -0.0473 -4.4644 0.0 0 +M V30 10 C -2.6526 -4.4644 0.0 0 +M V30 11 C -3.9552 -2.2026 0.0 0 +M V30 12 N -2.6644 -5.9566 0.0 0 +M V30 13 C -3.9671 -3.7065 0.0 0 +M V30 14 C -3.9789 -6.7026 0.0 0 +M V30 15 O -5.2934 -5.9329 0.0 0 +M V30 16 C -3.9908 -8.1947 0.0 0 +M V30 17 C -3.2565 -9.4855 0.0 0 +M V30 18 C -4.7487 -9.4855 0.0 0 +M V30 19 C -1.9657 -10.2197 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 10 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 7 9 +M V30 20 1 11 13 +M V30 21 1 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +646 + +> +Z2001222092 + +> +256.300 + +> +1.765 + +> +1 + +> +49.410 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1767059 + +> +0.87 + +$$$$ +Compound 647 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.2884 -0.7328 0.0 0 +M V30 3 C -1.312 -0.7328 0.0 0 +M V30 4 N 2.5768 0.0236 0.0 0 +M V30 5 N 1.2766 -2.2222 0.0 0 +M V30 6 C -2.6241 0.0236 0.0 0 +M V30 7 C 3.8653 -0.7092 0.0 0 +M V30 8 C 2.565 -2.9669 0.0 0 +M V30 9 O -2.6359 1.5366 0.0 0 +M V30 10 N -3.9362 -0.7092 0.0 0 +M V30 11 C 3.8534 -2.1986 0.0 0 +M V30 12 C 5.1537 0.0472 0.0 0 +M V30 13 O 2.5532 -4.4563 0.0 0 +M V30 14 C -5.2483 0.0472 0.0 0 +M V30 15 C 5.1419 -2.9433 0.0 0 +M V30 16 C 6.4421 -0.6855 0.0 0 +M V30 17 C -6.5603 -0.6855 0.0 0 +M V30 18 C -5.2601 1.5603 0.0 0 +M V30 19 C 6.4303 -2.1749 0.0 0 +M V30 20 F -6.5722 -2.1749 0.0 0 +M V30 21 C -7.8724 0.0709 0.0 0 +M V30 22 C -6.5722 2.3286 0.0 0 +M V30 23 C -7.8842 1.5839 0.0 0 +M V30 24 C -9.1963 2.3522 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 7 12 +M V30 12 2 8 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 2 12 16 +M V30 16 2 14 17 +M V30 17 1 14 18 +M V30 18 2 15 19 +M V30 19 1 17 20 +M V30 20 1 17 21 +M V30 21 2 18 22 +M V30 22 2 21 23 +M V30 23 1 23 24 +M V30 24 1 8 11 +M V30 25 1 16 19 +M V30 26 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +647 + +> +Z16078672 + +> +343.375 + +> +2.317 + +> +2 + +> +70.560 + +> +4 + +> +parp3 + +> + + +> + + +$$$$ +Compound 648 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 29 32 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.2881 -0.7327 0.0 0 +M V30 3 C -0.0118 1.5127 0.0 0 +M V30 4 N 2.5763 0.0236 0.0 0 +M V30 5 N 1.2763 -2.2218 0.0 0 +M V30 6 C -1.3236 2.269 0.0 0 +M V30 7 C 3.8645 -0.709 0.0 0 +M V30 8 C 2.5645 -2.9545 0.0 0 +M V30 9 O -2.6354 1.5363 0.0 0 +M V30 10 N -1.3354 3.7818 0.0 0 +M V30 11 C 3.8527 -2.1981 0.0 0 +M V30 12 C 5.1527 0.0472 0.0 0 +M V30 13 O 2.5527 -4.4436 0.0 0 +M V30 14 C -2.6472 4.5381 0.0 0 +M V30 15 C 5.1409 -2.9309 0.0 0 +M V30 16 C 6.4409 -0.6854 0.0 0 +M V30 17 C -2.659 6.0509 0.0 0 +M V30 18 C -3.959 3.8054 0.0 0 +M V30 19 C 6.4291 -2.1745 0.0 0 +M V30 20 C -3.9709 6.8072 0.0 0 +M V30 21 C -1.3709 6.8072 0.0 0 +M V30 22 C -5.2709 4.5618 0.0 0 +M V30 23 C -5.2827 6.0627 0.0 0 +M V30 24 C -1.3827 8.32 0.0 0 +M V30 25 C -2.6945 9.0763 0.0 0 +M V30 26 C -0.0945 9.0763 0.0 0 +M V30 27 C -2.7063 10.5891 0.0 0 +M V30 28 C -0.1063 10.5891 0.0 0 +M V30 29 C -1.4181 11.3454 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 7 12 +M V30 12 2 8 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 2 12 16 +M V30 16 2 14 17 +M V30 17 1 14 18 +M V30 18 2 15 19 +M V30 19 1 17 20 +M V30 20 1 17 21 +M V30 21 2 18 22 +M V30 22 2 20 23 +M V30 23 1 21 24 +M V30 24 2 24 25 +M V30 25 1 24 26 +M V30 26 1 25 27 +M V30 27 2 26 28 +M V30 28 2 27 29 +M V30 29 1 8 11 +M V30 30 1 16 19 +M V30 31 1 22 23 +M V30 32 1 28 29 +M V30 END BOND +M V30 END CTAB +M END +> +648 + +> +Z16078677 + +> +401.481 + +> +3.214 + +> +2 + +> +70.560 + +> +6 + +> +parp2 + +> + + +> + + +$$$$ +Compound 649 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 0.6062 -1.367 0.0 0 +M V30 3 C 2.0921 -1.5097 0.0 0 +M V30 4 C -0.2971 -2.5795 0.0 0 +M V30 5 C 2.6984 -2.8767 0.0 0 +M V30 6 C 0.309 -3.9466 0.0 0 +M V30 7 C 1.795 -4.0892 0.0 0 +M V30 8 C 2.4012 -5.4563 0.0 0 +M V30 9 S 1.6285 -6.752 0.0 0 +M V30 10 N 3.8634 -5.7535 0.0 0 +M V30 11 C 2.6271 -7.8575 0.0 0 +M V30 12 C 4.006 -7.2394 0.0 0 +M V30 13 C 5.3017 -7.9883 0.0 0 +M V30 14 S 6.5975 -7.2156 0.0 0 +M V30 15 C 7.8932 -7.9645 0.0 0 +M V30 16 N 9.1889 -7.1919 0.0 0 +M V30 17 N 7.8813 -9.4624 0.0 0 +M V30 18 C 10.4847 -7.9408 0.0 0 +M V30 19 C 9.1771 -10.2113 0.0 0 +M V30 20 C 10.4728 -9.4386 0.0 0 +M V30 21 C 11.7804 -7.1681 0.0 0 +M V30 22 O 9.1652 -11.7091 0.0 0 +M V30 23 C 11.7685 -10.1875 0.0 0 +M V30 24 C 13.0761 -7.917 0.0 0 +M V30 25 C 13.0642 -9.4148 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 2 8 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 1 12 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 1 20 23 +M V30 23 2 21 24 +M V30 24 2 23 25 +M V30 25 1 6 7 +M V30 26 2 11 12 +M V30 27 1 19 20 +M V30 28 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +649 + +> +Z16079217 + +> +385.890 + +> +4.209 + +> +1 + +> +54.350 + +> +4 + +> +parp3 + +> + + +> + + +$$$$ +Compound 650 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.489 0.0 0 +M V30 3 C -1.3117 0.7563 0.0 0 +M V30 4 N 1.2763 -2.2336 0.0 0 +M V30 5 N -1.3236 -2.2336 0.0 0 +M V30 6 C -1.3236 2.269 0.0 0 +M V30 7 C 1.2645 -3.7226 0.0 0 +M V30 8 C -1.3354 -3.7226 0.0 0 +M V30 9 O -0.0354 3.0254 0.0 0 +M V30 10 N -2.6354 3.0254 0.0 0 +M V30 11 C -0.0472 -4.4553 0.0 0 +M V30 12 C 2.5526 -4.4553 0.0 0 +M V30 13 O -2.6472 -4.4553 0.0 0 +M V30 14 C -2.6472 4.5381 0.0 0 +M V30 15 C -3.9472 2.2926 0.0 0 +M V30 16 C -0.059 -5.9444 0.0 0 +M V30 17 C 2.5408 -5.9444 0.0 0 +M V30 18 C -1.359 5.2944 0.0 0 +M V30 19 C -3.959 5.2944 0.0 0 +M V30 20 C -3.959 0.8036 0.0 0 +M V30 21 C 1.229 -6.6771 0.0 0 +M V30 22 C -1.3708 6.8071 0.0 0 +M V30 23 C -3.9708 6.8071 0.0 0 +M V30 24 C -5.2708 0.0709 0.0 0 +M V30 25 C -2.6826 7.5635 0.0 0 +M V30 26 N -6.5826 -0.6736 0.0 0 +M V30 27 F -2.6945 9.0762 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 7 12 +M V30 12 2 8 13 +M V30 13 1 10 14 +M V30 14 1 10 15 +M V30 15 1 11 16 +M V30 16 2 12 17 +M V30 17 2 14 18 +M V30 18 1 14 19 +M V30 19 1 15 20 +M V30 20 2 16 21 +M V30 21 1 18 22 +M V30 22 2 19 23 +M V30 23 1 20 24 +M V30 24 2 22 25 +M V30 25 3 24 26 +M V30 26 1 25 27 +M V30 27 1 8 11 +M V30 28 1 17 21 +M V30 29 1 23 25 +M V30 END BOND +M V30 END CTAB +M END +> +650 + +> +Z16078747 + +> +382.411 + +> +2.084 + +> +1 + +> +85.560 + +> +6 + +> +parp2 + +> + + +> + + +$$$$ +Compound 651 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.2897 -0.7454 0.0 0 +M V30 3 C -1.3134 -0.7454 0.0 0 +M V30 4 N 2.5795 0.0236 0.0 0 +M V30 5 N 1.2779 -2.2364 0.0 0 +M V30 6 C -2.6269 0.0236 0.0 0 +M V30 7 C 3.8693 -0.7218 0.0 0 +M V30 8 C 2.5677 -2.97 0.0 0 +M V30 9 O -2.6387 1.5382 0.0 0 +M V30 10 N -3.9403 -0.7218 0.0 0 +M V30 11 C 3.8575 -2.2127 0.0 0 +M V30 12 C 5.1591 0.0473 0.0 0 +M V30 13 O 2.5559 -4.461 0.0 0 +M V30 14 C -5.2538 0.0473 0.0 0 +M V30 15 C 5.1473 -2.9464 0.0 0 +M V30 16 C 6.4489 -0.6981 0.0 0 +M V30 17 C -6.5672 -0.6981 0.0 0 +M V30 18 C -5.2656 1.5619 0.0 0 +M V30 19 C 6.4371 -2.189 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 7 12 +M V30 12 2 8 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 2 12 16 +M V30 16 1 14 17 +M V30 17 1 14 18 +M V30 18 2 15 19 +M V30 19 1 8 11 +M V30 20 1 16 19 +M V30 END BOND +M V30 END CTAB +M END +> +651 + +> +Z16078846 + +> +277.342 + +> +0.874 + +> +2 + +> +70.560 + +> +4 + +> +parp15 + +> + + +> + + +$$$$ +Compound 652 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.5169 0.0 0 +M V30 3 C 1.2798 2.2753 0.0 0 +M V30 4 C -1.3273 2.2753 0.0 0 +M V30 5 N 2.5716 1.5406 0.0 0 +M V30 6 C 1.268 3.7922 0.0 0 +M V30 7 C -1.3391 3.7922 0.0 0 +M V30 8 C 3.8634 2.299 0.0 0 +M V30 9 C -0.0474 4.5626 0.0 0 +M V30 10 O 3.8515 3.8159 0.0 0 +M V30 11 C 5.1551 1.5643 0.0 0 +M V30 12 S 6.4469 2.3227 0.0 0 +M V30 13 C 7.7386 1.588 0.0 0 +M V30 14 N 9.0304 2.3464 0.0 0 +M V30 15 N 7.7268 0.0948 0.0 0 +M V30 16 C 10.3221 1.6117 0.0 0 +M V30 17 C 9.0185 -0.6518 0.0 0 +M V30 18 C 10.3103 0.1185 0.0 0 +M V30 19 C 11.6139 2.3701 0.0 0 +M V30 20 O 9.0067 -2.145 0.0 0 +M V30 21 C 11.602 -0.628 0.0 0 +M V30 22 C 12.9056 1.6354 0.0 0 +M V30 23 C 12.8938 0.1422 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 2 8 10 +M V30 10 1 8 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 2 21 23 +M V30 23 1 7 9 +M V30 24 1 17 18 +M V30 25 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +652 + +> +Z16079363 + +> +345.803 + +> +2.138 + +> +2 + +> +70.560 + +> +4 + +> +parp3 + +> + + +> + + +$$$$ +Compound 653 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3107 -0.7321 0.0 0 +M V30 3 C -0.0118 1.5115 0.0 0 CFG=2 +M V30 4 N -1.3225 -2.22 0.0 0 +M V30 5 N -2.6215 0.0236 0.0 0 +M V30 6 C 1.2753 2.279 0.0 0 +M V30 7 C -1.3225 2.279 0.0 0 +M V30 8 C -2.6333 -2.9639 0.0 0 +M V30 9 C -3.9322 -0.7085 0.0 0 +M V30 10 O 2.5624 1.5351 0.0 0 +M V30 11 N 1.2635 3.7905 0.0 0 +M V30 12 C -3.944 -2.1964 0.0 0 +M V30 13 C -2.6451 -4.4518 0.0 0 +M V30 14 O -5.243 0.0472 0.0 0 +M V30 15 C 2.5506 4.5463 0.0 0 +M V30 16 C -5.2548 -2.9403 0.0 0 +M V30 17 C -3.9558 -5.1839 0.0 0 +M V30 18 C 3.8378 3.8023 0.0 0 +M V30 19 C 2.5388 6.0578 0.0 0 +M V30 20 C -5.2666 -4.4282 0.0 0 +M V30 21 C 5.1249 4.5581 0.0 0 +M V30 22 C 3.826 6.8135 0.0 0 +M V30 23 O 6.412 3.8141 0.0 0 +M V30 24 C 5.1131 6.0696 0.0 0 +M V30 25 C 7.6992 4.5699 0.0 0 +M V30 26 O 6.4002 6.8253 0.0 0 +M V30 27 C 7.6874 6.0814 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 3 1 CFG=1 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 3 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 2 13 17 +M V30 17 2 15 18 +M V30 18 1 15 19 +M V30 19 2 16 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 1 21 23 +M V30 23 2 21 24 +M V30 24 1 23 25 +M V30 25 1 24 26 +M V30 26 1 25 27 +M V30 27 1 9 12 +M V30 28 1 17 20 +M V30 29 1 22 24 +M V30 30 1 26 27 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 3) +M V30 END COLLECTION +M V30 END CTAB +M END +> +653 + +> +Z16078998 + +> +383.421 + +> +2.342 + +> +2 + +> +89.020 + +> +4 + +> +parp3 + +> + + +> + + +$$$$ +Compound 654 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.2886 0.7566 0.0 0 +M V30 3 C -0.0118 -1.4896 0.0 0 CFG=2 +M V30 4 N 1.2768 2.2699 0.0 0 +M V30 5 N 2.5772 0.0236 0.0 0 +M V30 6 C -1.3241 -2.2344 0.0 0 +M V30 7 C 1.2768 -2.2344 0.0 0 +M V30 8 C 2.5654 3.0265 0.0 0 +M V30 9 C 3.8659 0.7802 0.0 0 +M V30 10 O -2.6364 -1.4659 0.0 0 +M V30 11 N -1.3359 -3.724 0.0 0 +M V30 12 C 3.8541 2.2817 0.0 0 +M V30 13 C 2.5536 4.5398 0.0 0 +M V30 14 O 5.1545 0.0472 0.0 0 +M V30 15 C -2.6482 -4.457 0.0 0 +M V30 16 C 5.1427 3.0383 0.0 0 +M V30 17 C 3.8422 5.2964 0.0 0 +M V30 18 C -3.9605 -3.7004 0.0 0 +M V30 19 C -2.66 -5.9466 0.0 0 +M V30 20 C 5.1309 4.5516 0.0 0 +M V30 21 C -5.2728 -4.4334 0.0 0 +M V30 22 C -3.9723 -6.6796 0.0 0 +M V30 23 O -6.7151 -3.9605 0.0 0 +M V30 24 C -5.2846 -5.923 0.0 0 +M V30 25 C -7.6136 -5.1664 0.0 0 +M V30 26 O -6.7269 -6.3722 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 3 1 CFG=1 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 3 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 2 13 17 +M V30 17 2 15 18 +M V30 18 1 15 19 +M V30 19 2 16 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 1 21 23 +M V30 23 2 21 24 +M V30 24 1 23 25 +M V30 25 1 24 26 +M V30 26 1 9 12 +M V30 27 1 17 20 +M V30 28 1 22 24 +M V30 29 1 25 26 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 3) +M V30 END COLLECTION +M V30 END CTAB +M END +> +654 + +> +Z16078999 + +> +369.394 + +> +2.383 + +> +2 + +> +89.020 + +> +4 + +> +parp3 + +> + + +> + + +$$$$ +Compound 655 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.2891 -0.7332 0.0 0 +M V30 3 C -1.3128 -0.7332 0.0 0 CFG=2 +M V30 4 N 2.5782 0.0236 0.0 0 +M V30 5 N 1.2773 -2.2234 0.0 0 +M V30 6 C -2.6256 0.0236 0.0 0 +M V30 7 C -1.3246 -2.2234 0.0 0 +M V30 8 C 3.8674 -0.7096 0.0 0 +M V30 9 C 2.5664 -2.9685 0.0 0 +M V30 10 O -2.6374 1.5375 0.0 0 +M V30 11 N -3.9384 -0.7096 0.0 0 +M V30 12 C 3.8556 -2.1998 0.0 0 +M V30 13 C 5.1565 0.0473 0.0 0 +M V30 14 O 2.5546 -4.4587 0.0 0 +M V30 15 C -5.2512 0.0473 0.0 0 +M V30 16 C 5.1447 -2.9449 0.0 0 +M V30 17 C 6.4457 -0.6859 0.0 0 +M V30 18 C -6.564 -0.6859 0.0 0 +M V30 19 C -5.263 1.5611 0.0 0 +M V30 20 C 6.4339 -2.1761 0.0 0 +M V30 21 O -6.5758 -2.1761 0.0 0 +M V30 22 C -7.8768 0.0709 0.0 0 +M V30 23 C -6.5758 2.3299 0.0 0 +M V30 24 C -7.8886 -2.9212 0.0 0 +M V30 25 C -7.8886 1.5848 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 3 1 CFG=1 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 3 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 2 13 17 +M V30 17 2 15 18 +M V30 18 1 15 19 +M V30 19 2 16 20 +M V30 20 1 18 21 +M V30 21 1 18 22 +M V30 22 2 19 23 +M V30 23 1 21 24 +M V30 24 2 22 25 +M V30 25 1 9 12 +M V30 26 1 17 20 +M V30 27 1 23 25 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 3) +M V30 END COLLECTION +M V30 END CTAB +M END +> +655 + +> +Z16079020 + +> +355.411 + +> +1.811 + +> +2 + +> +79.790 + +> +5 + +> +parp3 + +> + + +> + + +$$$$ +Compound 656 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3114 0.7561 0.0 0 +M V30 3 C 1.2878 0.7561 0.0 0 CFG=2 +M V30 4 N -2.6229 0.0236 0.0 0 +M V30 5 N -1.3232 2.2684 0.0 0 +M V30 6 C 2.5756 0.0236 0.0 0 +M V30 7 C 1.276 2.2684 0.0 0 +M V30 8 C -3.9343 0.7797 0.0 0 +M V30 9 C -2.6347 3.0246 0.0 0 +M V30 10 O 2.5638 -1.465 0.0 0 +M V30 11 N 3.8635 0.7797 0.0 0 +M V30 12 C -3.9462 2.2921 0.0 0 +M V30 13 C -5.2458 0.0472 0.0 0 +M V30 14 O -2.6465 4.5369 0.0 0 +M V30 15 C 5.1513 0.0472 0.0 0 +M V30 16 C -5.2576 3.0482 0.0 0 +M V30 17 C -6.5573 0.8034 0.0 0 +M V30 18 C 6.4391 0.8034 0.0 0 +M V30 19 C 5.1395 -1.4414 0.0 0 +M V30 20 C -6.5691 2.3039 0.0 0 +M V30 21 C 7.727 0.0708 0.0 0 +M V30 22 C 6.4273 -2.1739 0.0 0 +M V30 23 C 7.7151 -1.4178 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 3 1 CFG=1 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 3 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 2 13 17 +M V30 17 2 15 18 +M V30 18 1 15 19 +M V30 19 2 16 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 2 21 23 +M V30 23 1 9 12 +M V30 24 1 17 20 +M V30 25 1 22 23 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 3) +M V30 END COLLECTION +M V30 END CTAB +M END +> +656 + +> +Z16079023 + +> +325.385 + +> +2.326 + +> +2 + +> +70.560 + +> +4 + +> +parp3 + +> + + +> + + +$$$$ +Compound 657 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 29 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.2914 -0.7345 0.0 0 +M V30 3 C -1.3151 -0.7345 0.0 0 +M V30 4 N 2.5828 0.0236 0.0 0 +M V30 5 N 1.2795 -2.2274 0.0 0 +M V30 6 C -2.6302 0.0236 0.0 0 +M V30 7 C 3.8743 -0.7108 0.0 0 +M V30 8 C 2.571 -2.962 0.0 0 +M V30 9 O -2.6421 1.5402 0.0 0 +M V30 10 N -3.9454 -0.7108 0.0 0 +M V30 11 C 3.8624 -2.2037 0.0 0 +M V30 12 C 5.1657 0.0473 0.0 0 +M V30 13 O 2.5591 -4.4548 0.0 0 +M V30 14 C -5.2605 0.0473 0.0 0 +M V30 15 C 5.1539 -2.9383 0.0 0 +M V30 16 C 6.4572 -0.6871 0.0 0 +M V30 17 C -6.5757 -0.6871 0.0 0 +M V30 18 C 6.4453 -2.18 0.0 0 +M V30 19 O -6.5875 -2.18 0.0 0 +M V30 20 N -7.8908 0.071 0.0 0 +M V30 21 C -9.206 -0.6634 0.0 0 +M V30 22 C -9.2178 -2.1563 0.0 0 +M V30 23 C -10.5211 0.0947 0.0 0 +M V30 24 F -7.9264 -2.8909 0.0 0 +M V30 25 C -10.5329 -2.8909 0.0 0 +M V30 26 C -11.8362 -0.6397 0.0 0 +M V30 27 F -10.5448 -4.3838 0.0 0 +M V30 28 C -11.8481 -2.1326 0.0 0 +M V30 29 F -13.1632 -2.8672 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 7 12 +M V30 12 2 8 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 2 12 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 2 17 19 +M V30 19 1 17 20 +M V30 20 1 20 21 +M V30 21 2 21 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 22 25 +M V30 25 2 23 26 +M V30 26 1 25 27 +M V30 27 2 25 28 +M V30 28 1 28 29 +M V30 29 1 8 11 +M V30 30 1 16 18 +M V30 31 1 26 28 +M V30 END BOND +M V30 END CTAB +M END +> +657 + +> +Z16079551 + +> +422.381 + +> +1.392 + +> +3 + +> +99.660 + +> +6 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 658 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.2889 -0.7331 0.0 0 +M V30 3 C -0.0118 1.5136 0.0 0 +M V30 4 N 2.5779 0.0236 0.0 0 +M V30 5 N 1.2771 -2.2231 0.0 0 +M V30 6 C -1.3244 2.2704 0.0 0 +M V30 7 C 3.8669 -0.7095 0.0 0 +M V30 8 C 2.5661 -2.9682 0.0 0 +M V30 9 O -2.637 1.5373 0.0 0 +M V30 10 N -1.3362 3.7841 0.0 0 +M V30 11 C 3.8551 -2.1995 0.0 0 +M V30 12 C 5.1559 0.0473 0.0 0 +M V30 13 O 2.5543 -4.4582 0.0 0 +M V30 14 C -2.5661 4.6829 0.0 0 +M V30 15 C -0.13 4.6829 0.0 0 +M V30 16 C 5.144 -2.9445 0.0 0 +M V30 17 C 6.4449 -0.6858 0.0 0 +M V30 18 C -2.1167 6.1256 0.0 0 +M V30 19 C -0.6031 6.1256 0.0 0 +M V30 20 C 6.433 -2.1877 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 7 12 +M V30 12 2 8 13 +M V30 13 1 10 14 +M V30 14 1 10 15 +M V30 15 1 11 16 +M V30 16 2 12 17 +M V30 17 1 14 18 +M V30 18 1 15 19 +M V30 19 2 16 20 +M V30 20 1 8 11 +M V30 21 1 17 20 +M V30 22 1 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +658 + +> +Z16079625 + +> +289.353 + +> +0.820 + +> +1 + +> +61.770 + +> +3 + +> +parp2 + +> + + +> + + +$$$$ +Compound 659 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -0.7579 -1.2908 0.0 0 +M V30 3 C -0.0236 -2.5817 0.0 0 +M V30 4 C -2.2738 -1.279 0.0 0 +M V30 5 F 1.4685 -2.5698 0.0 0 +M V30 6 C -0.7816 -3.8725 0.0 0 +M V30 7 F -3.0317 0.0355 0.0 0 +M V30 8 C -3.0317 -2.5698 0.0 0 +M V30 9 N -0.0473 -5.1634 0.0 0 +M V30 10 C -2.2975 -3.8607 0.0 0 +M V30 11 C 1.4448 -5.1516 0.0 0 +M V30 12 O 2.179 -3.837 0.0 0 +M V30 13 C 2.179 -6.4424 0.0 0 +M V30 14 N 3.6712 -6.4306 0.0 0 +M V30 15 C 4.5476 -5.1989 0.0 0 +M V30 16 C 4.5476 -7.6385 0.0 0 +M V30 17 O 4.0739 -3.7541 0.0 0 +M V30 18 C 5.9687 -5.649 0.0 0 +M V30 19 O 4.0739 -9.0597 0.0 0 +M V30 20 C 5.9687 -7.1648 0.0 0 +M V30 21 C 7.2596 -4.891 0.0 0 +M V30 22 C 7.2596 -7.8991 0.0 0 +M V30 23 N 7.2477 -3.3751 0.0 0 CHG=1 +M V30 24 C 8.5504 -5.6253 0.0 0 +M V30 25 C 8.5504 -7.1412 0.0 0 +M V30 26 O 5.9332 -2.6172 0.0 0 +M V30 27 O 8.5386 -2.6172 0.0 0 CHG=-1 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 4 8 +M V30 8 1 6 9 +M V30 9 2 6 10 +M V30 10 1 9 11 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 1 15 18 +M V30 18 2 16 19 +M V30 19 1 16 20 +M V30 20 1 18 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 2 21 24 +M V30 24 2 22 25 +M V30 25 2 23 26 +M V30 26 1 23 27 +M V30 27 1 8 10 +M V30 28 2 18 20 +M V30 29 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +659 + +> +Z16107166 + +> +379.247 + +> +2.245 + +> +1 + +> +109.620 + +> +4 + +> +DNA_pk + +> + + +> + + +$$$$ +Compound 660 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4905 0.0 0 +M V30 3 N -1.3249 -2.2357 0.0 0 +M V30 4 C 1.2775 -2.2357 0.0 0 +M V30 5 C -2.638 -1.4668 0.0 0 +M V30 6 O 2.567 -1.4668 0.0 0 +M V30 7 C -3.951 -2.2121 0.0 0 +M V30 8 C -2.6498 0.0473 0.0 0 +M V30 9 C 3.8564 -2.2121 0.0 0 +M V30 10 C -5.2641 -1.4432 0.0 0 +M V30 11 C -3.9629 0.8044 0.0 0 +M V30 12 C 3.8446 -3.7026 0.0 0 +M V30 13 C 5.1458 -1.4432 0.0 0 +M V30 14 C -6.5772 -2.1884 0.0 0 +M V30 15 C -5.276 0.0709 0.0 0 +M V30 16 C 5.134 -4.4361 0.0 0 +M V30 17 C 6.4353 -2.1884 0.0 0 +M V30 18 O -6.589 -3.679 0.0 0 +M V30 19 C -7.8903 -1.4195 0.0 0 +M V30 20 C 6.4234 -3.679 0.0 0 +M V30 21 C 5.4297 -5.8911 0.0 0 +M V30 22 C 7.5236 -4.6726 0.0 0 +M V30 23 C 6.9084 -6.033 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 2 10 15 +M V30 15 1 12 16 +M V30 16 2 13 17 +M V30 17 2 14 18 +M V30 18 1 14 19 +M V30 19 2 16 20 +M V30 20 1 16 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 11 15 +M V30 24 1 17 20 +M V30 25 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +660 + +> +Z16115995 + +> +309.359 + +> +3.599 + +> +1 + +> +55.400 + +> +5 + +> +parp14 + +> + + +> + + +$$$$ +Compound 661 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 31 34 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.307 -0.73 0.0 0 +M V30 3 C -2.6141 0.0235 0.0 0 +M V30 4 C -1.3188 -2.2137 0.0 0 +M V30 5 C -3.9212 -0.7065 0.0 0 +M V30 6 C -2.6259 -2.9556 0.0 0 +M V30 7 N -5.2283 0.0471 0.0 0 +M V30 8 C -3.933 -2.1902 0.0 0 +M V30 9 C -6.5354 -0.6829 0.0 0 +M V30 10 C -5.24 -2.932 0.0 0 +M V30 11 S -7.8424 0.0706 0.0 0 +M V30 12 N -6.5471 -2.1666 0.0 0 +M V30 13 O -5.2518 -4.4158 0.0 0 +M V30 14 C -7.8542 1.5779 0.0 0 CFG=2 +M V30 15 C -7.8542 -2.9085 0.0 0 +M V30 16 C -6.5707 2.3315 0.0 0 +M V30 17 C -9.1613 2.3315 0.0 0 +M V30 18 C -7.866 -4.3922 0.0 0 +M V30 19 C -9.1613 -2.1549 0.0 0 +M V30 20 O -5.2871 1.6014 0.0 0 +M V30 21 N -6.5825 3.8388 0.0 0 +M V30 22 C -9.1731 -5.1341 0.0 0 +M V30 23 C -10.4684 -2.8967 0.0 0 +M V30 24 C -7.8895 4.5924 0.0 0 +M V30 25 C -5.2989 4.5924 0.0 0 +M V30 26 C -10.4801 -4.3687 0.0 0 +M V30 27 C -9.1966 3.8623 0.0 0 +M V30 28 C -7.9013 6.0997 0.0 0 +M V30 29 C -10.5037 4.6159 0.0 0 +M V30 30 C -9.2084 6.8651 0.0 0 +M V30 31 C -10.5155 6.1232 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 14 11 CFG=1 +M V30 14 1 12 15 +M V30 15 1 14 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 1 15 19 +M V30 19 2 16 20 +M V30 20 1 16 21 +M V30 21 1 18 22 +M V30 22 2 19 23 +M V30 23 1 21 24 +M V30 24 1 21 25 +M V30 25 2 22 26 +M V30 26 2 24 27 +M V30 27 1 24 28 +M V30 28 1 27 29 +M V30 29 2 28 30 +M V30 30 2 29 31 +M V30 31 1 6 8 +M V30 32 1 10 12 +M V30 33 1 23 26 +M V30 34 1 30 31 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 14) +M V30 END COLLECTION +M V30 END CTAB +M END +> +661 + +> +Z16111959 + +> +449.953 + +> +5.439 + +> +0 + +> +52.980 + +> +5 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL460853 + +> +0.87 + +$$$$ +Compound 662 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3164 -0.7353 0.0 0 +M V30 3 N -2.6328 0.0237 0.0 0 +M V30 4 C -1.3283 -2.2296 0.0 0 +M V30 5 C -0.0355 -2.9768 0.0 0 +M V30 6 C -2.6447 -2.9768 0.0 0 +M V30 7 C -0.0474 -4.4711 0.0 0 +M V30 8 C -2.6566 -4.4711 0.0 0 +M V30 9 N 1.2452 -5.2183 0.0 0 +M V30 10 C -1.3638 -5.2183 0.0 0 +M V30 11 C -3.973 -5.2183 0.0 0 +M V30 12 C 2.538 -4.4474 0.0 0 +M V30 13 O -5.2895 -4.4474 0.0 0 +M V30 14 N -3.9849 -6.7126 0.0 0 +M V30 15 O 2.5261 -2.9293 0.0 0 +M V30 16 C 3.8307 -5.1946 0.0 0 +M V30 17 O 5.1234 -4.4237 0.0 0 +M V30 18 C 6.4161 -5.1709 0.0 0 +M V30 19 C 6.4043 -6.6652 0.0 0 +M V30 20 C 7.7089 -4.4 0.0 0 +M V30 21 C 7.697 -7.4005 0.0 0 +M V30 22 C 9.0016 -5.1471 0.0 0 +M V30 23 C 8.9897 -6.6415 0.0 0 +M V30 24 C 7.9935 -8.8593 0.0 0 +M V30 25 C 10.0927 -7.6377 0.0 0 +M V30 26 C 9.476 -9.0016 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 1 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 2 12 15 +M V30 15 1 12 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 2 21 23 +M V30 23 1 21 24 +M V30 24 1 23 25 +M V30 25 1 24 26 +M V30 26 1 8 10 +M V30 27 1 22 23 +M V30 28 1 25 26 +M V30 END BOND +M V30 END CTAB +M END +> +662 + +> +Z16115734 + +> +353.372 + +> +1.229 + +> +3 + +> +124.510 + +> +6 + +> +parp14 + +> + + +> + + +$$$$ +Compound 663 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 0.4477 1.4374 0.0 0 +M V30 3 F -0.9897 1.9087 0.0 0 +M V30 4 F 1.8615 0.9897 0.0 0 +M V30 5 C 0.8954 2.8748 0.0 0 +M V30 6 C -0.1178 3.9941 0.0 0 +M V30 7 C 2.3446 3.1929 0.0 0 +M V30 8 N 0.3299 5.4315 0.0 0 +M V30 9 C 2.7923 4.6304 0.0 0 +M V30 10 C 1.7791 5.7497 0.0 0 +M V30 11 N 4.2415 4.9485 0.0 0 +M V30 12 O 2.2268 7.1871 0.0 0 +M V30 13 C 4.6893 6.3859 0.0 0 +M V30 14 O 3.676 7.5052 0.0 0 +M V30 15 C 6.1385 6.704 0.0 0 +M V30 16 C 6.5862 8.1415 0.0 0 +M V30 17 C 8.0001 8.6127 0.0 0 +M V30 18 C 5.6908 9.3668 0.0 0 +M V30 19 C 7.9883 10.1209 0.0 0 +M V30 20 C 9.2843 7.8705 0.0 0 +M V30 21 N 6.5626 10.5922 0.0 0 +M V30 22 C 4.1826 9.3786 0.0 0 +M V30 23 C 9.2725 10.8867 0.0 0 +M V30 24 C 10.5686 8.6363 0.0 0 +M V30 25 C 10.5568 10.1562 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 2 5 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 1 11 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 2 16 18 +M V30 18 2 17 19 +M V30 19 1 17 20 +M V30 20 1 18 21 +M V30 21 1 18 22 +M V30 22 1 19 23 +M V30 23 2 20 24 +M V30 24 2 23 25 +M V30 25 1 9 10 +M V30 26 1 19 21 +M V30 27 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +663 + +> +Z507534416 + +> +349.307 + +> +2.028 + +> +3 + +> +73.990 + +> +4 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 664 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.2924 0.7588 0.0 0 +M V30 3 F 2.0394 -0.5335 0.0 0 +M V30 4 F 0.5335 2.075 0.0 0 +M V30 5 C 2.5848 1.5177 0.0 0 +M V30 6 C 2.573 3.0354 0.0 0 +M V30 7 C 3.8772 0.7707 0.0 0 +M V30 8 N 3.8654 3.7942 0.0 0 +M V30 9 C 5.1697 1.5295 0.0 0 +M V30 10 C 5.1578 3.0591 0.0 0 +M V30 11 N 6.4621 0.7825 0.0 0 +M V30 12 O 6.4503 3.818 0.0 0 +M V30 13 C 7.7545 1.5414 0.0 0 +M V30 14 O 7.7427 3.0591 0.0 0 +M V30 15 C 9.047 0.7944 0.0 0 +M V30 16 C 10.3394 1.5532 0.0 0 +M V30 17 N 11.6318 0.8062 0.0 0 +M V30 18 N 11.7741 -0.6758 0.0 0 +M V30 19 C 12.9954 1.4347 0.0 0 +M V30 20 C 13.2326 -0.9722 0.0 0 +M V30 21 N 13.9914 0.332 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 2 5 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 1 11 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 2 19 21 +M V30 21 1 9 10 +M V30 22 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +664 + +> +Z507530164 + +> +301.225 + +> +-0.650 + +> +2 + +> +88.910 + +> +5 + +> +ATM + +> + + +> + + +$$$$ +Compound 665 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.4718 0.3179 0.0 0 +M V30 3 C -1.9428 1.7544 0.0 0 +M V30 4 C -2.4844 -0.7771 0.0 0 +M V30 5 C -3.4146 2.0723 0.0 0 +M V30 6 C -3.9562 -0.4592 0.0 0 +M V30 7 O -4.1799 3.3793 0.0 0 +M V30 8 C -4.4272 0.9772 0.0 0 +M V30 9 C -5.6518 3.0849 0.0 0 +M V30 10 N -5.8048 1.6013 0.0 0 +M V30 11 O -6.7704 4.0975 0.0 0 +M V30 12 C -7.1118 0.8713 0.0 0 +M V30 13 C -8.4188 1.6249 0.0 0 +M V30 14 C -9.7258 0.8948 0.0 0 +M V30 15 O -9.7376 -0.5887 0.0 0 +M V30 16 N -11.0328 1.6484 0.0 0 +M V30 17 C -12.3398 0.9184 0.0 0 +M V30 18 C -13.6468 1.6719 0.0 0 +M V30 19 C -12.3516 -0.5651 0.0 0 +M V30 20 O -13.6585 3.1791 0.0 0 +M V30 21 N -14.9537 0.9419 0.0 0 +M V30 22 C -13.6585 -1.3069 0.0 0 +M V30 23 C -14.9655 -0.5416 0.0 0 +M V30 24 C -13.6703 -2.7905 0.0 0 +M V30 25 F -13.6821 -4.2741 0.0 0 +M V30 26 F -12.1867 -2.7788 0.0 0 +M V30 27 F -15.1775 -2.7788 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 1 10 12 +M V30 12 1 12 13 +M V30 13 1 13 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 2 17 19 +M V30 19 2 18 20 +M V30 20 1 18 21 +M V30 21 1 19 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 24 25 +M V30 25 1 24 26 +M V30 26 1 24 27 +M V30 27 1 6 8 +M V30 28 1 9 10 +M V30 29 2 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +665 + +> +Z507533466 + +> +401.724 + +> +1.984 + +> +2 + +> +87.740 + +> +5 + +> +parp10 + +> + + +> + + +$$$$ +Compound 666 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.3184 -1.4506 0.0 0 +M V30 3 N 0.7784 -2.4413 0.0 0 +M V30 4 C -1.7573 -1.8988 0.0 0 CFG=1 +M V30 5 N -2.9839 -1.0024 0.0 0 +M V30 6 C -2.229 -3.3141 0.0 0 +M V30 7 C -2.9956 0.5071 0.0 0 +M V30 8 C -4.2104 -1.8752 0.0 0 +M V30 9 C -3.7387 -3.3023 0.0 0 +M V30 10 C -4.3048 1.2619 0.0 0 +M V30 11 C -1.7101 1.2619 0.0 0 +M V30 12 C -4.3166 2.7716 0.0 0 +M V30 13 C -1.7219 2.7716 0.0 0 +M V30 14 N -3.031 3.5382 0.0 0 +M V30 15 C -3.0428 5.0478 0.0 0 +M V30 16 C -1.7573 5.8026 0.0 0 +M V30 17 C -1.7691 7.3123 0.0 0 +M V30 18 C -0.4717 5.0596 0.0 0 +M V30 19 N -0.4835 8.0671 0.0 0 +M V30 20 C 0.8137 5.8144 0.0 0 +M V30 21 C 0.8019 7.3359 0.0 0 +M V30 22 C 2.0993 5.0714 0.0 0 +M V30 23 C 2.0875 8.0907 0.0 0 +M V30 24 C 3.3849 5.8262 0.0 0 +M V30 25 C 3.3731 7.3595 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 4 2 CFG=3 +M V30 4 1 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 1 7 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 2 16 18 +M V30 18 2 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 2 21 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 1 8 9 +M V30 26 1 13 14 +M V30 27 1 20 21 +M V30 28 2 24 25 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 4) +M V30 END COLLECTION +M V30 END CTAB +M END +> +666 + +> +Z1989711061 + +> +338.447 + +> +1.253 + +> +1 + +> +62.460 + +> +4 + +> +ATM + +> + + +> + + +$$$$ +Compound 667 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5098 0.0 0 +M V30 3 N -1.3211 2.2765 0.0 0 +M V30 4 C 1.2739 2.2765 0.0 0 +M V30 5 C -2.6304 1.5334 0.0 0 CFG=2 +M V30 6 N 2.5596 1.5334 0.0 0 +M V30 7 C 1.2621 3.7864 0.0 0 +M V30 8 C -3.9397 2.3001 0.0 0 +M V30 9 C -2.6422 0.0471 0.0 0 +M V30 10 C 3.8454 2.3001 0.0 0 +M V30 11 C 2.8545 0.0825 0.0 0 +M V30 12 C 2.5478 4.5413 0.0 0 +M V30 13 C -5.2491 1.557 0.0 0 +M V30 14 N 3.8336 3.81 0.0 0 +M V30 15 C 4.9424 1.3093 0.0 0 +M V30 16 N 4.329 -0.0589 0.0 0 +M V30 17 C 2.536 6.0512 0.0 0 +M V30 18 C -6.5584 2.3237 0.0 0 +M V30 19 C 6.3933 1.6278 0.0 0 +M V30 20 O 3.7392 6.9477 0.0 0 +M V30 21 C 1.3093 6.9477 0.0 0 +M V30 22 O 6.8415 3.0668 0.0 0 +M V30 23 N 7.3841 0.5308 0.0 0 +M V30 24 C 3.2674 8.3867 0.0 0 +M V30 25 C 1.7575 8.3867 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 5 3 CFG=1 +M V30 5 1 4 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 6 11 +M V30 11 1 7 12 +M V30 12 1 8 13 +M V30 13 1 10 14 +M V30 14 2 10 15 +M V30 15 2 11 16 +M V30 16 1 12 17 +M V30 17 1 13 18 +M V30 18 1 15 19 +M V30 19 1 17 20 +M V30 20 2 17 21 +M V30 21 2 19 22 +M V30 22 1 19 23 +M V30 23 1 20 24 +M V30 24 1 21 25 +M V30 25 2 12 14 +M V30 26 1 15 16 +M V30 27 2 24 25 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 5) +M V30 END COLLECTION +M V30 END CTAB +M END +> +667 + +> +Z319697674 + +> +341.364 + +> +3.125 + +> +2 + +> +115.520 + +> +6 + +> +parp14 + +> + + +> + + +$$$$ +Compound 668 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O 0.7365 -1.2949 0.0 0 +M V30 3 O -0.7603 1.3187 0.0 0 +M V30 4 N -1.3187 -0.7365 0.0 0 +M V30 5 C 1.2949 0.7603 0.0 0 +M V30 6 C 1.283 2.281 0.0 0 +M V30 7 C 2.5898 0.0237 0.0 0 +M V30 8 C 2.578 3.0413 0.0 0 +M V30 9 C 3.8848 0.784 0.0 0 +M V30 10 C 3.8729 2.3047 0.0 0 +M V30 11 C 5.1678 3.065 0.0 0 +M V30 12 C 6.4628 2.3285 0.0 0 +M V30 13 N 7.7577 3.0888 0.0 0 +M V30 14 C 9.0527 2.3522 0.0 0 +M V30 15 O 9.0408 0.8553 0.0 0 +M V30 16 C 10.3476 3.1126 0.0 0 +M V30 17 N 11.6426 2.376 0.0 0 +M V30 18 C 12.9375 3.1363 0.0 0 +M V30 19 O 12.9256 4.657 0.0 0 +M V30 20 C 14.2324 2.3998 0.0 0 +M V30 21 O 15.5274 3.1601 0.0 0 +M V30 22 C 16.8223 2.4235 0.0 0 +M V30 23 C 18.1173 3.1838 0.0 0 +M V30 24 C 16.8105 0.9266 0.0 0 +M V30 25 C 19.4122 2.4473 0.0 0 +M V30 26 C 18.1054 0.19 0.0 0 +M V30 27 C 19.4003 0.9504 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 2 5 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 2 8 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 1 13 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 20 21 +M V30 21 1 21 22 +M V30 22 2 22 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 2 24 26 +M V30 26 2 25 27 +M V30 27 1 9 10 +M V30 28 1 26 27 +M V30 END BOND +M V30 END CTAB +M END +> +668 + +> +Z32377683 + +> +391.441 + +> +0.516 + +> +3 + +> +127.590 + +> +9 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 669 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 30 32 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.4524 0.3188 0.0 0 +M V30 3 F 2.4444 -0.7793 0.0 0 +M V30 4 N 1.9012 1.7595 0.0 0 +M V30 5 C 1.0037 2.9876 0.0 0 +M V30 6 C 3.3182 2.2318 0.0 0 +M V30 7 N 1.8776 4.2157 0.0 0 +M V30 8 C -0.5077 2.9994 0.0 0 +M V30 9 C 3.3064 3.7433 0.0 0 +M V30 10 C 4.6054 1.4997 0.0 0 +M V30 11 C -1.2635 4.3102 0.0 0 +M V30 12 C -1.2635 1.7122 0.0 0 +M V30 13 C 4.5936 4.4991 0.0 0 +M V30 14 C 5.8925 2.2554 0.0 0 +M V30 15 C -2.775 4.322 0.0 0 +M V30 16 C -2.775 1.724 0.0 0 +M V30 17 C 5.8807 3.767 0.0 0 +M V30 18 C -3.5308 3.0348 0.0 0 +M V30 19 N -5.0423 3.0466 0.0 0 +M V30 20 C -5.7981 1.7595 0.0 0 +M V30 21 O -5.0659 0.4723 0.0 0 +M V30 22 C -7.3096 1.7713 0.0 0 CFG=1 +M V30 23 N -8.0654 3.0821 0.0 0 +M V30 24 C -8.0654 0.4841 0.0 0 +M V30 25 C -9.5769 3.0939 0.0 0 +M V30 26 C -7.3332 -0.8029 0.0 0 +M V30 27 O -10.3327 1.8067 0.0 0 +M V30 28 N -10.3327 4.4046 0.0 0 +M V30 29 C -5.8453 -0.7911 0.0 0 +M V30 30 C -8.089 -2.0901 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 4 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 11 15 +M V30 15 2 12 16 +M V30 16 2 13 17 +M V30 17 2 15 18 +M V30 18 1 18 19 +M V30 19 1 19 20 +M V30 20 2 20 21 +M V30 21 1 20 22 +M V30 22 1 22 23 CFG=3 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 1 24 26 +M V30 26 2 25 27 +M V30 27 1 25 28 +M V30 28 1 26 29 +M V30 29 1 26 30 +M V30 30 1 7 9 +M V30 31 1 14 17 +M V30 32 1 16 18 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 22) +M V30 END COLLECTION +M V30 END CTAB +M END +> +669 + +> +Z226416968 + +> +415.436 + +> +3.585 + +> +3 + +> +102.040 + +> +7 + +> +ATM + +> + + +> + + +$$$$ +Compound 670 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4937 0.0 0 +M V30 3 C -1.3278 -2.2288 0.0 0 +M V30 4 C 1.2803 -2.2288 0.0 0 +M V30 5 C -1.3396 -3.7226 0.0 0 +M V30 6 C -2.6437 -1.47 0.0 0 +M V30 7 C 1.2685 -3.7226 0.0 0 +M V30 8 C 2.5726 -1.47 0.0 0 +M V30 9 O -0.0474 -4.4695 0.0 0 +M V30 10 C -2.6556 -4.4695 0.0 0 +M V30 11 C -3.9597 -2.2051 0.0 0 +M V30 12 C 2.5607 -4.4695 0.0 0 +M V30 13 C -2.6674 -5.9633 0.0 0 +M V30 14 C -3.9716 -3.7107 0.0 0 +M V30 15 C 3.853 -3.7107 0.0 0 +M V30 16 C 2.5489 -5.9633 0.0 0 +M V30 17 O -1.3752 -6.7102 0.0 0 +M V30 18 O -3.9834 -6.7102 0.0 0 +M V30 19 C 5.1453 -4.4576 0.0 0 +M V30 20 C 3.8411 -6.7102 0.0 0 +M V30 21 C -3.9953 -8.204 0.0 0 +M V30 22 C 5.1334 -5.9514 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 1 5 10 +M V30 10 2 6 11 +M V30 11 1 7 12 +M V30 12 1 10 13 +M V30 13 2 10 14 +M V30 14 2 12 15 +M V30 15 1 12 16 +M V30 16 2 13 17 +M V30 17 1 13 18 +M V30 18 1 15 19 +M V30 19 2 16 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 1 7 9 +M V30 23 1 11 14 +M V30 24 1 20 22 +M V30 END BOND +M V30 END CTAB +M END +> +670 + +> +Z16190836 + +> +294.301 + +> +3.648 + +> +0 + +> +52.600 + +> +3 + +> +Tankyrase-1 + +> +CHEMBL2431806 + +> +0.86 + +$$$$ +Compound 671 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.3585 -0.6024 0.0 0 +M V30 3 N 1.6538 -2.0554 0.0 0 +M V30 4 N 2.6461 0.1535 0.0 0 +M V30 5 C 3.1304 -2.1972 0.0 0 +M V30 6 C 0.6379 -3.1541 0.0 0 +M V30 7 C 3.7447 -0.8387 0.0 0 CFG=2 +M V30 8 O 3.8747 -3.4848 0.0 0 +M V30 9 C -0.8387 -2.8351 0.0 0 +M V30 10 C 5.1978 -0.5197 0.0 0 +M V30 11 O -1.8546 -3.9337 0.0 0 +M V30 12 C 5.6467 0.9214 0.0 0 +M V30 13 C -3.3313 -3.6148 0.0 0 +M V30 14 C 7.0642 1.3939 0.0 0 +M V30 15 C 4.7489 2.15 0.0 0 +M V30 16 C -3.8038 -2.1736 0.0 0 +M V30 17 C -4.3472 -4.7134 0.0 0 +M V30 18 C 7.0524 2.906 0.0 0 +M V30 19 C 8.3519 0.6497 0.0 0 +M V30 20 N 5.623 3.3785 0.0 0 +M V30 21 C -5.2805 -1.8546 0.0 0 +M V30 22 C -5.8239 -4.3945 0.0 0 +M V30 23 C 8.3401 3.6739 0.0 0 +M V30 24 C 9.6395 1.4175 0.0 0 +M V30 25 C -6.2964 -2.9532 0.0 0 +M V30 26 C 9.6277 2.9296 0.0 0 +M V30 27 C -7.773 -2.6343 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 CFG=1 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 2 12 15 +M V30 15 2 13 16 +M V30 16 1 13 17 +M V30 17 2 14 18 +M V30 18 1 14 19 +M V30 19 1 15 20 +M V30 20 1 16 21 +M V30 21 2 17 22 +M V30 22 1 18 23 +M V30 23 2 19 24 +M V30 24 2 21 25 +M V30 25 2 23 26 +M V30 26 1 25 27 +M V30 27 1 5 7 +M V30 28 1 18 20 +M V30 29 1 22 25 +M V30 30 1 24 26 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 7) +M V30 END COLLECTION +M V30 END CTAB +M END +> +671 + +> +Z16202335 + +> +363.410 + +> +3.423 + +> +2 + +> +74.430 + +> +6 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 672 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.7543 -1.2847 0.0 0 +M V30 3 N -0.0235 -2.5695 0.0 0 +M V30 4 N -2.263 -1.2729 0.0 0 +M V30 5 C 1.4615 -2.5577 0.0 0 +M V30 6 C -3.0173 0.0353 0.0 0 +M V30 7 C 2.1923 -3.8424 0.0 0 +M V30 8 C 2.1923 -1.2493 0.0 0 +M V30 9 C -4.526 0.0471 0.0 0 +M V30 10 O 1.4379 -5.1272 0.0 0 +M V30 11 N 3.6774 -3.8306 0.0 0 +M V30 12 O 1.4379 0.0589 0.0 0 +M V30 13 N 3.6774 -1.2376 0.0 0 +M V30 14 C -5.2804 1.3554 0.0 0 +M V30 15 C -5.2804 -1.2376 0.0 0 +M V30 16 C -6.7891 1.3672 0.0 0 +M V30 17 C -4.5378 2.6637 0.0 0 +M V30 18 C -6.7891 -1.2258 0.0 0 +M V30 19 C -7.5552 0.0825 0.0 0 +M V30 20 N -5.2922 3.9721 0.0 0 +M V30 21 N -4.6911 5.3511 0.0 0 +M V30 22 C -6.7891 4.1371 0.0 0 +M V30 23 C -5.8108 6.3648 0.0 0 +M V30 24 N -7.1073 5.6104 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 9 15 +M V30 15 1 14 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 2 16 19 +M V30 19 1 17 20 +M V30 20 1 20 21 +M V30 21 1 20 22 +M V30 22 2 21 23 +M V30 23 2 22 24 +M V30 24 1 18 19 +M V30 25 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +672 + +> +Z370006730 + +> +331.330 + +> +-2.107 + +> +4 + +> +158.020 + +> +7 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 673 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2939 0.7716 0.0 0 +M V30 3 C 1.2821 2.2911 0.0 0 +M V30 4 C 2.5879 0.0237 0.0 0 +M V30 5 C 2.5761 3.0509 0.0 0 +M V30 6 C 3.8819 0.7953 0.0 0 +M V30 7 C 3.8701 2.3149 0.0 0 +M V30 8 N 5.164 3.0747 0.0 0 +M V30 9 C 6.458 2.3386 0.0 0 +M V30 10 O 6.4462 0.8428 0.0 0 +M V30 11 N 7.752 3.0984 0.0 0 +M V30 12 C 9.046 2.3624 0.0 0 +M V30 13 C 10.34 3.1221 0.0 0 +M V30 14 C 11.634 2.3861 0.0 0 +M V30 15 C 10.3281 4.6417 0.0 0 +M V30 16 C 12.928 3.1459 0.0 0 +M V30 17 C 11.6221 5.4015 0.0 0 +M V30 18 N 12.9161 4.6654 0.0 0 +M V30 19 N 14.222 2.4099 0.0 0 +M V30 20 N 14.3644 0.9259 0.0 0 +M V30 21 C 15.5872 3.039 0.0 0 +M V30 22 C 15.8246 0.6291 0.0 0 +M V30 23 C 16.5844 1.935 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 1 19 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 2 21 23 +M V30 23 1 6 7 +M V30 24 1 17 18 +M V30 25 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +673 + +> +Z362011076 + +> +327.768 + +> +2.860 + +> +2 + +> +71.840 + +> +4 + +> +DNA_pk + +> + + +> + + +$$$$ +Compound 674 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.495 0.0118 0.0 0 +M V30 3 N 2.2307 1.3289 0.0 0 +M V30 4 C 2.2307 -1.2814 0.0 0 +M V30 5 C 3.7258 1.3408 0.0 0 +M V30 6 C 3.7258 -1.2696 0.0 0 +M V30 7 C 1.4713 -2.5748 0.0 0 +M V30 8 N 4.4614 0.0474 0.0 0 +M V30 9 C 4.4614 2.6579 0.0 0 +M V30 10 C 4.4614 -2.5629 0.0 0 +M V30 11 C 2.207 -3.8682 0.0 0 +M V30 12 N 5.9565 2.6697 0.0 0 +M V30 13 C 3.702 -3.8563 0.0 0 +M V30 14 C 6.704 3.9868 0.0 0 +M V30 15 C 6.704 1.3764 0.0 0 +M V30 16 O 5.9328 5.3039 0.0 0 +M V30 17 C 8.1991 3.9987 0.0 0 +M V30 18 N 9.0772 2.7884 0.0 0 +M V30 19 C 9.0772 5.2327 0.0 0 +M V30 20 C 10.5011 3.263 0.0 0 +M V30 21 N 10.5011 4.7818 0.0 0 +M V30 22 C 11.7944 2.5273 0.0 0 +M V30 23 C 11.7944 5.5412 0.0 0 +M V30 24 C 13.0878 3.2867 0.0 0 +M V30 25 C 13.0878 4.8055 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 1 12 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 1 17 18 +M V30 18 2 17 19 +M V30 19 2 18 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 2 22 24 +M V30 24 2 23 25 +M V30 25 1 6 8 +M V30 26 1 11 13 +M V30 27 1 20 21 +M V30 28 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +674 + +> +Z30085377 + +> +333.344 + +> +0.566 + +> +1 + +> +79.070 + +> +3 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 675 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.4234 -0.4507 0.0 0 +M V30 3 C -0.9015 -1.2099 0.0 0 +M V30 4 N 2.7163 0.3202 0.0 0 +M V30 5 C 1.4115 -1.9453 0.0 0 +M V30 6 C -0.0237 -2.4198 0.0 0 +M V30 7 C -2.4198 -1.198 0.0 0 +M V30 8 C 4.0093 -0.4151 0.0 0 +M V30 9 C 2.7045 -2.6926 0.0 0 +M V30 10 C -0.4981 -3.8432 0.0 0 +M V30 11 S 5.3022 0.3558 0.0 0 +M V30 12 N 3.9974 -1.9097 0.0 0 +M V30 13 O 2.6926 -4.1872 0.0 0 +M V30 14 C 6.5952 -0.3795 0.0 0 +M V30 15 C 7.8881 0.3914 0.0 0 +M V30 16 O 7.8763 1.9097 0.0 0 +M V30 17 N 9.1811 -0.3439 0.0 0 +M V30 18 C 10.474 0.427 0.0 0 +M V30 19 C 9.1692 -1.8385 0.0 0 +M V30 20 C 11.767 -0.3084 0.0 0 +M V30 21 C 10.4621 -2.5858 0.0 0 +M V30 22 O 11.7551 -1.803 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 8 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 11 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 5 6 +M V30 23 1 9 12 +M V30 24 1 21 22 +M V30 END BOND +M V30 END CTAB +M END +> +675 + +> +Z16228019 + +> +339.433 + +> +1.357 + +> +1 + +> +71.000 + +> +3 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL2440886 + +> +0.94 + +$$$$ +Compound 676 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.0354 1.5106 0.0 0 +M V30 3 C 1.2982 -0.7081 0.0 0 +M V30 4 N -1.2746 2.384 0.0 0 +M V30 5 N 1.1447 2.4312 0.0 0 +M V30 6 C 1.3218 -2.1951 0.0 0 +M V30 7 N -0.8497 3.8356 0.0 0 +M V30 8 C 0.6373 3.8592 0.0 0 +M V30 9 C 2.6318 2.4784 0.0 0 +M V30 10 O 2.62 -2.9033 0.0 0 +M V30 11 N 0.0236 -2.9623 0.0 0 +M V30 12 N 1.8175 4.7798 0.0 0 +M V30 13 C 3.0567 3.93 0.0 0 +M V30 14 C 3.6468 1.3926 0.0 0 +M V30 15 C -1.2982 -2.2305 0.0 0 +M V30 16 C 4.5083 4.2841 0.0 0 +M V30 17 C 5.0984 1.7467 0.0 0 +M V30 18 C -2.5964 -2.9977 0.0 0 +M V30 19 C -1.3454 -0.7199 0.0 0 +M V30 20 C 5.5233 3.1983 0.0 0 +M V30 21 C -3.9182 -2.2659 0.0 0 +M V30 22 C -2.5728 -4.4847 0.0 0 +M V30 23 C -2.6672 0.0118 0.0 0 +M V30 24 C -3.9536 -0.7553 0.0 0 +M V30 25 C -3.871 -5.2519 0.0 0 +M V30 26 C -1.2746 -5.1929 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 9 14 +M V30 14 1 11 15 +M V30 15 1 13 16 +M V30 16 2 14 17 +M V30 17 2 15 18 +M V30 18 1 15 19 +M V30 19 2 16 20 +M V30 20 1 18 21 +M V30 21 1 18 22 +M V30 22 2 19 23 +M V30 23 2 21 24 +M V30 24 1 22 25 +M V30 25 1 22 26 +M V30 26 2 7 8 +M V30 27 1 12 13 +M V30 28 1 17 20 +M V30 29 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +676 + +> +Z16226182 + +> +365.452 + +> +3.100 + +> +2 + +> +75.080 + +> +5 + +> +ATM + +> + + +> + + +$$$$ +Compound 677 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 0.7375 -1.2966 0.0 0 +M V30 3 C 2.2364 -1.2847 0.0 0 +M V30 4 C -0.0237 -2.5933 0.0 0 +M V30 5 C 2.9859 -2.5814 0.0 0 +M V30 6 C 0.7137 -3.89 0.0 0 +M V30 7 N 4.4491 -2.8788 0.0 0 +M V30 8 C 2.2126 -3.8781 0.0 0 +M V30 9 C 4.5919 -4.3658 0.0 0 +M V30 10 N 3.2119 -4.9844 0.0 0 +M V30 11 S 5.8885 -5.1034 0.0 0 +M V30 12 C 7.1852 -4.342 0.0 0 CFG=2 +M V30 13 C 8.4819 -5.0796 0.0 0 +M V30 14 C 7.1733 -2.8193 0.0 0 +M V30 15 N 9.7786 -4.3182 0.0 0 +M V30 16 N 8.47 -6.5785 0.0 0 +M V30 17 C 11.0753 -5.0558 0.0 0 +M V30 18 C 9.7667 -7.328 0.0 0 +M V30 19 C 11.0634 -6.5547 0.0 0 +M V30 20 C 12.3719 -4.2945 0.0 0 +M V30 21 O 9.7548 -8.8269 0.0 0 +M V30 22 C 12.36 -7.3042 0.0 0 +M V30 23 C 13.6686 -5.032 0.0 0 +M V30 24 C 13.6567 -6.5428 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 1 12 11 CFG=1 +M V30 12 1 12 13 +M V30 13 1 12 14 +M V30 14 2 13 15 +M V30 15 1 13 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 17 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 1 19 22 +M V30 22 2 20 23 +M V30 23 2 22 24 +M V30 24 1 6 8 +M V30 25 2 9 10 +M V30 26 1 18 19 +M V30 27 1 23 24 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 12) +M V30 END COLLECTION +M V30 END CTAB +M END +> +677 + +> +Z16258135 + +> +356.829 + +> +3.536 + +> +2 + +> +70.140 + +> +3 + +> +parp10 + +> + + +> + + +$$$$ +Compound 678 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3113 0.756 0.0 0 +M V30 3 C -0.0118 -1.4885 0.0 0 +M V30 4 N -2.6935 0.1535 0.0 0 +M V30 5 N -1.4767 2.2564 0.0 0 +M V30 6 C 1.2758 -2.2209 0.0 0 +M V30 7 N -3.7095 1.2758 0.0 0 +M V30 8 C -2.9534 2.5753 0.0 0 +M V30 9 C -0.378 3.2724 0.0 0 +M V30 10 N 2.5635 -1.4649 0.0 0 +M V30 11 N 1.264 -3.7095 0.0 0 +M V30 12 C -3.5795 3.9575 0.0 0 +M V30 13 C -0.697 4.7491 0.0 0 +M V30 14 C 3.8512 -2.1973 0.0 0 +M V30 15 C 2.5517 -4.4537 0.0 0 +M V30 16 C -5.0799 4.1229 0.0 0 +M V30 17 C -2.7053 5.1862 0.0 0 +M V30 18 C 3.8394 -3.6858 0.0 0 +M V30 19 C 5.1389 -1.4412 0.0 0 +M V30 20 O 2.5399 -5.9423 0.0 0 +M V30 21 C -5.706 5.5051 0.0 0 +M V30 22 C -3.3314 6.5684 0.0 0 +M V30 23 C 5.1271 -4.4301 0.0 0 +M V30 24 C 6.4266 -2.1737 0.0 0 +M V30 25 C -4.8318 6.7338 0.0 0 +M V30 26 C 6.4148 -3.6622 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 2 12 16 +M V30 16 1 12 17 +M V30 17 2 14 18 +M V30 18 1 14 19 +M V30 19 2 15 20 +M V30 20 1 16 21 +M V30 21 2 17 22 +M V30 22 1 18 23 +M V30 23 2 19 24 +M V30 24 2 21 25 +M V30 25 2 23 26 +M V30 26 2 7 8 +M V30 27 1 15 18 +M V30 28 1 22 25 +M V30 29 1 24 26 +M V30 END BOND +M V30 END CTAB +M END +> +678 + +> +Z16260377 + +> +363.436 + +> +2.107 + +> +1 + +> +72.170 + +> +5 + +> +parp10 + +> + + +> + + +$$$$ +Compound 679 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3139 0.7576 0.0 0 +M V30 3 C 1.2903 0.7576 0.0 0 +M V30 4 C -2.6279 0.0236 0.0 0 +M V30 5 C -1.3258 2.2728 0.0 0 +M V30 6 C 2.5806 0.0236 0.0 0 +M V30 7 C -3.9419 0.7812 0.0 0 +M V30 8 C -2.6397 3.0304 0.0 0 +M V30 9 N 3.8709 0.7812 0.0 0 +M V30 10 C -3.9537 2.2964 0.0 0 +M V30 11 C -5.2559 0.0473 0.0 0 +M V30 12 C 5.1612 0.0473 0.0 0 +M V30 13 C -5.2677 3.0541 0.0 0 +M V30 14 O 5.1493 -1.4441 0.0 0 +M V30 15 N 6.4515 0.8049 0.0 0 +M V30 16 C 7.7418 0.071 0.0 0 +M V30 17 C 9.0321 0.8286 0.0 0 +M V30 18 C 10.3224 0.0947 0.0 0 +M V30 19 C 9.0202 2.3438 0.0 0 +M V30 20 N 11.6127 0.8523 0.0 0 +M V30 21 N 10.3105 -1.3968 0.0 0 +M V30 22 C 10.3105 3.1014 0.0 0 +M V30 23 C 11.6008 2.3675 0.0 0 +M V30 24 C 11.6008 -2.1307 0.0 0 +M V30 25 C 8.9965 -2.1307 0.0 0 +M V30 26 C 11.589 -3.6223 0.0 0 +M V30 27 C 8.9847 -3.6223 0.0 0 +M V30 28 O 10.275 -4.368 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 7 11 +M V30 11 1 9 12 +M V30 12 1 10 13 +M V30 13 2 12 14 +M V30 14 1 12 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 2 20 23 +M V30 23 1 21 24 +M V30 24 1 21 25 +M V30 25 1 24 26 +M V30 26 1 25 27 +M V30 27 1 26 28 +M V30 28 1 8 10 +M V30 29 1 22 23 +M V30 30 1 27 28 +M V30 END BOND +M V30 END CTAB +M END +> +679 + +> +Z336510856 + +> +400.538 + +> +3.190 + +> +2 + +> +66.490 + +> +7 + +> +ATM + +> + + +> + + +$$$$ +Compound 680 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 34 38 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.29 -0.7337 0.0 0 +M V30 3 C -1.3136 -0.7337 0.0 0 +M V30 4 N 2.58 0.0236 0.0 0 +M V30 5 N 1.2781 -2.2249 0.0 0 +M V30 6 C -2.6273 0.0236 0.0 0 +M V30 7 C 3.87 -0.7101 0.0 0 +M V30 8 C 2.5682 -2.9705 0.0 0 +M V30 9 C -0.0355 -2.9705 0.0 0 +M V30 10 O -2.6392 1.5385 0.0 0 +M V30 11 N -3.941 -0.7101 0.0 0 +M V30 12 C 3.8582 -2.2013 0.0 0 +M V30 13 C 5.16 0.0473 0.0 0 +M V30 14 O 2.5563 -4.4618 0.0 0 +M V30 15 C -0.0473 -4.4618 0.0 0 +M V30 16 C -5.2547 0.0473 0.0 0 +M V30 17 C -3.9529 -2.2013 0.0 0 +M V30 18 C 5.1482 -2.9469 0.0 0 +M V30 19 C 6.45 -0.6864 0.0 0 +M V30 20 C -1.361 -5.2074 0.0 0 +M V30 21 C 1.2426 -5.2074 0.0 0 +M V30 22 C -6.5684 -0.6864 0.0 0 +M V30 23 C -5.2665 1.5622 0.0 0 +M V30 24 C -5.2665 -2.9469 0.0 0 +M V30 25 C 6.4382 -2.1894 0.0 0 +M V30 26 C -1.3728 -6.6986 0.0 0 +M V30 27 C 1.2308 -6.6986 0.0 0 +M V30 28 N -6.5802 -2.1776 0.0 0 +M V30 29 C -7.8821 0.071 0.0 0 +M V30 30 C -6.5802 2.3196 0.0 0 +M V30 31 O -5.2784 -4.4381 0.0 0 +M V30 32 C -0.0828 -7.4324 0.0 0 +M V30 33 C -7.8939 1.5858 0.0 0 +M V30 34 F -0.0946 -8.9236 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 2 7 12 +M V30 12 1 7 13 +M V30 13 2 8 14 +M V30 14 1 9 15 +M V30 15 1 11 16 +M V30 16 1 11 17 +M V30 17 1 12 18 +M V30 18 2 13 19 +M V30 19 2 15 20 +M V30 20 1 15 21 +M V30 21 2 16 22 +M V30 22 1 16 23 +M V30 23 1 17 24 +M V30 24 2 18 25 +M V30 25 1 20 26 +M V30 26 2 21 27 +M V30 27 1 22 28 +M V30 28 1 22 29 +M V30 29 2 23 30 +M V30 30 2 24 31 +M V30 31 2 26 32 +M V30 32 2 29 33 +M V30 33 1 32 34 +M V30 34 1 8 12 +M V30 35 1 19 25 +M V30 36 1 24 28 +M V30 37 1 27 32 +M V30 38 1 30 33 +M V30 END BOND +M V30 END CTAB +M END +> +680 + +> +Z16276786 + +> +474.507 + +> +3.646 + +> +1 + +> +82.080 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3105415 + +> +0.91 + +$$$$ +Compound 681 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 30 32 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2884 -0.7446 0.0 0 +M V30 3 N 1.2766 -2.234 0.0 0 +M V30 4 C 2.5768 0.0236 0.0 0 +M V30 5 C 2.565 -2.9669 0.0 0 +M V30 6 C -0.0354 -2.9669 0.0 0 +M V30 7 O 3.8652 -0.721 0.0 0 +M V30 8 C 3.8534 -2.2104 0.0 0 +M V30 9 C 2.5532 -4.4563 0.0 0 +M V30 10 C -1.3475 -2.2104 0.0 0 +M V30 11 C 5.1418 -2.9432 0.0 0 +M V30 12 C 3.8416 -5.1891 0.0 0 +M V30 13 C -2.6595 -2.9432 0.0 0 +M V30 14 C 5.13 -4.4326 0.0 0 +M V30 15 O -2.6714 -4.4326 0.0 0 +M V30 16 N -3.9716 -2.1867 0.0 0 +M V30 17 C -5.2837 -2.9196 0.0 0 +M V30 18 C -6.5958 -2.1631 0.0 0 +M V30 19 C -7.9078 -2.896 0.0 0 +M V30 20 C -6.6076 -0.6501 0.0 0 +M V30 21 C -9.2199 -2.1394 0.0 0 +M V30 22 C -7.9196 0.1182 0.0 0 +M V30 23 C -10.532 -2.8723 0.0 0 +M V30 24 C -9.2317 -0.6264 0.0 0 +M V30 25 O -10.5438 -4.3617 0.0 0 +M V30 26 N -11.844 -2.1158 0.0 0 +M V30 27 C -13.1561 -2.8487 0.0 0 CFG=2 +M V30 28 C -14.4682 -2.0922 0.0 0 +M V30 29 C -13.1679 -4.338 0.0 0 +M V30 30 C -15.7802 -2.825 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 10 13 +M V30 13 2 11 14 +M V30 14 2 13 15 +M V30 15 1 13 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 1 21 23 +M V30 23 2 21 24 +M V30 24 2 23 25 +M V30 25 1 23 26 +M V30 26 1 27 26 CFG=1 +M V30 27 1 27 28 +M V30 28 1 27 29 +M V30 29 1 28 30 +M V30 30 1 7 8 +M V30 31 1 12 14 +M V30 32 1 22 24 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 27) +M V30 END COLLECTION +M V30 END CTAB +M END +> +681 + +> +Z437399410 + +> +409.478 + +> +2.434 + +> +2 + +> +87.740 + +> +8 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 682 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 30 32 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2877 -0.7443 0.0 0 +M V30 3 N 1.2759 -2.2329 0.0 0 +M V30 4 C 2.5755 0.0236 0.0 0 +M V30 5 C 2.5637 -2.9654 0.0 0 +M V30 6 C -0.0354 -2.9654 0.0 0 +M V30 7 O 3.8633 -0.7206 0.0 0 +M V30 8 C 3.8515 -2.2093 0.0 0 +M V30 9 C 2.5519 -4.4541 0.0 0 +M V30 10 C -1.3468 -2.2093 0.0 0 +M V30 11 C 5.1393 -2.9418 0.0 0 +M V30 12 C 3.8397 -5.1866 0.0 0 +M V30 13 C -2.6582 -2.9418 0.0 0 +M V30 14 C 5.1275 -4.4304 0.0 0 +M V30 15 O -2.6701 -4.4304 0.0 0 +M V30 16 N -3.9697 -2.1857 0.0 0 +M V30 17 C -5.2811 -2.9182 0.0 0 +M V30 18 C -6.5925 -2.162 0.0 0 +M V30 19 C -7.9039 -2.8945 0.0 0 +M V30 20 C -6.6043 -0.6498 0.0 0 +M V30 21 C -9.2153 -2.1384 0.0 0 +M V30 22 C -7.9157 0.1181 0.0 0 +M V30 23 N -10.5268 -2.8709 0.0 0 +M V30 24 C -9.2272 -0.6261 0.0 0 +M V30 25 C -11.8382 -2.1148 0.0 0 +M V30 26 O -11.85 -0.6025 0.0 0 +M V30 27 C -13.1496 -2.8473 0.0 0 CFG=2 +M V30 28 C -14.461 -2.0911 0.0 0 +M V30 29 C -13.1614 -4.3359 0.0 0 +M V30 30 C -15.7725 -2.8236 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 10 13 +M V30 13 2 11 14 +M V30 14 2 13 15 +M V30 15 1 13 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 1 21 23 +M V30 23 2 21 24 +M V30 24 1 23 25 +M V30 25 2 25 26 +M V30 26 1 25 27 +M V30 27 1 27 28 +M V30 28 1 27 29 CFG=1 +M V30 29 1 28 30 +M V30 30 1 7 8 +M V30 31 1 12 14 +M V30 32 1 22 24 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 27) +M V30 END COLLECTION +M V30 END CTAB +M END +> +682 + +> +Z437386504 + +> +409.478 + +> +2.734 + +> +2 + +> +87.740 + +> +8 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 683 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -1.3045 -0.7286 0.0 0 +M V30 3 C -2.6091 0.0235 0.0 0 +M V30 4 C -1.3163 -2.2095 0.0 0 +M V30 5 C -3.9137 -0.7051 0.0 0 +M V30 6 C -2.6209 -2.9499 0.0 0 +M V30 7 C -3.9254 -2.186 0.0 0 +M V30 8 C -5.23 -2.9264 0.0 0 +M V30 9 C -6.5346 -2.1625 0.0 0 +M V30 10 O -6.5463 -0.6581 0.0 0 +M V30 11 N -7.8392 -2.9029 0.0 0 +M V30 12 C -9.1438 -2.139 0.0 0 +M V30 13 C -10.4483 -2.8794 0.0 0 +M V30 14 N -11.7529 -2.1155 0.0 0 +M V30 15 C -13.0575 -2.8559 0.0 0 +M V30 16 O -13.0692 -4.3368 0.0 0 +M V30 17 C -14.3621 -2.092 0.0 0 +M V30 18 C -15.6666 -2.8324 0.0 0 +M V30 19 C -14.3738 -0.5876 0.0 0 +M V30 20 C -16.9712 -2.0685 0.0 0 +M V30 21 C -15.6784 -4.3133 0.0 0 +M V30 22 C -15.6784 0.1645 0.0 0 +M V30 23 N -16.983 -0.5641 0.0 0 +M V30 24 C -18.2758 -2.8089 0.0 0 +M V30 25 C -16.983 -5.0537 0.0 0 +M V30 26 O -15.6902 1.6689 0.0 0 +M V30 27 C -18.2876 -4.2898 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 17 18 +M V30 18 2 17 19 +M V30 19 1 18 20 +M V30 20 2 18 21 +M V30 21 1 19 22 +M V30 22 1 20 23 +M V30 23 2 20 24 +M V30 24 1 21 25 +M V30 25 1 22 26 +M V30 26 1 24 27 +M V30 27 1 6 7 +M V30 28 2 22 23 +M V30 29 2 25 27 +M V30 END BOND +M V30 END CTAB +M END +> +683 + +> +Z966207682 + +> +367.374 + +> +3.093 + +> +3 + +> +91.320 + +> +6 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 684 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3092 -0.7312 0.0 0 +M V30 3 N -2.6184 0.0235 0.0 0 +M V30 4 C -1.321 -2.2174 0.0 0 +M V30 5 C -2.6302 1.5333 0.0 0 +M V30 6 C -0.0353 -2.9486 0.0 0 +M V30 7 C -2.6302 -2.9486 0.0 0 +M V30 8 C -3.8568 2.4297 0.0 0 +M V30 9 C -1.4271 2.4297 0.0 0 +M V30 10 C -0.0471 -4.4348 0.0 0 +M V30 11 C 1.2502 -2.1938 0.0 0 +M V30 12 C -2.642 -4.4348 0.0 0 +M V30 13 N -3.4086 3.8686 0.0 0 +M V30 14 N -1.8989 3.8686 0.0 0 +M V30 15 N -1.3563 -5.1778 0.0 0 +M V30 16 C 1.2384 -5.1778 0.0 0 +M V30 17 C 2.5358 -2.925 0.0 0 +M V30 18 O -3.9512 -5.1778 0.0 0 +M V30 19 C -1.0261 5.0953 0.0 0 +M V30 20 C 2.524 -4.423 0.0 0 +M V30 21 C -1.6512 6.4753 0.0 0 CFG=2 +M V30 22 O -0.9081 7.7845 0.0 0 +M V30 23 C -3.1256 6.7937 0.0 0 +M V30 24 C -1.9225 8.905 0.0 0 +M V30 25 C -3.2907 8.2917 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 2 6 11 +M V30 11 1 7 12 +M V30 12 2 8 13 +M V30 13 1 9 14 +M V30 14 1 10 15 +M V30 15 2 10 16 +M V30 16 1 11 17 +M V30 17 1 12 18 +M V30 18 1 14 19 +M V30 19 1 16 20 +M V30 20 1 21 19 CFG=1 +M V30 21 1 21 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 2 12 15 +M V30 26 1 13 14 +M V30 27 2 17 20 +M V30 28 1 24 25 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 21) +M V30 END COLLECTION +M V30 END CTAB +M END +> +684 + +> +Z748557036 + +> +338.361 + +> +2.215 + +> +2 + +> +89.270 + +> +4 + +> +parp14 + +> + + +> + + +$$$$ +Compound 685 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5054 0.0 0 +M V30 3 N -1.3172 2.2581 0.0 0 +M V30 4 C 1.2702 2.2581 0.0 0 +M V30 5 C -1.329 3.7635 0.0 0 +M V30 6 C 1.2584 3.7635 0.0 0 +M V30 7 C 2.5521 1.5171 0.0 0 +M V30 8 N -0.047 4.5163 0.0 0 +M V30 9 C -2.6345 4.5163 0.0 0 +M V30 10 C 2.5404 4.5163 0.0 0 +M V30 11 C 3.8341 2.2699 0.0 0 +M V30 12 C -2.6462 6.0217 0.0 0 +M V30 13 C 3.8223 3.7871 0.0 0 +M V30 14 N -3.9517 6.7744 0.0 0 +M V30 15 C -3.9635 8.2798 0.0 0 +M V30 16 C -5.2572 6.0452 0.0 0 +M V30 17 C -5.269 9.0326 0.0 0 CFG=2 +M V30 18 C -6.5627 6.7979 0.0 0 +M V30 19 O -6.5745 8.3034 0.0 0 +M V30 20 C -5.2807 10.538 0.0 0 +M V30 21 C -6.5862 11.3025 0.0 0 +M V30 22 C -3.9988 11.3025 0.0 0 +M V30 23 C -6.598 12.8079 0.0 0 +M V30 24 C -4.0105 12.8079 0.0 0 +M V30 25 C -5.316 13.5606 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 17 20 CFG=3 +M V30 20 2 20 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 2 22 24 +M V30 24 2 23 25 +M V30 25 1 6 8 +M V30 26 1 11 13 +M V30 27 1 18 19 +M V30 28 1 24 25 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 17) +M V30 END COLLECTION +M V30 END CTAB +M END +> +685 + +> +Z729229542 + +> +335.400 + +> +1.985 + +> +1 + +> +53.930 + +> +4 + +> +parp3 + +> + + +> + + +$$$$ +Compound 686 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.495 0.0 0 +M V30 3 N 1.2814 -2.2425 0.0 0 +M V30 4 C -1.3289 -2.2425 0.0 0 +M V30 5 C 2.5747 -1.4831 0.0 0 +M V30 6 C -1.3407 -3.7375 0.0 0 +M V30 7 C -2.6459 -1.4831 0.0 0 +M V30 8 C 3.868 -2.2306 0.0 0 +M V30 9 C -2.6578 -4.4731 0.0 0 +M V30 10 C -3.9629 -2.2306 0.0 0 +M V30 11 C 5.1613 -1.4712 0.0 0 +M V30 12 O -2.6696 -5.9682 0.0 0 +M V30 13 C -3.9748 -3.7138 0.0 0 +M V30 14 O -5.28 -1.4712 0.0 0 +M V30 15 N 6.4546 -2.2187 0.0 0 +M V30 16 C -3.9867 -6.7038 0.0 0 +M V30 17 C -5.2918 0.0474 0.0 0 +M V30 18 C 6.4428 -3.7138 0.0 0 +M V30 19 C 7.7479 -1.4594 0.0 0 +M V30 20 C 7.7361 -4.4494 0.0 0 +M V30 21 C 9.0413 -2.2069 0.0 0 +M V30 22 C 9.0294 -3.69 0.0 0 +M V30 23 C 7.7242 -5.9444 0.0 0 +M V30 24 C 10.3227 -4.4257 0.0 0 +M V30 25 C 9.0175 -6.6801 0.0 0 +M V30 26 C 10.3108 -5.9207 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 1 9 12 +M V30 12 2 9 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 15 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 1 20 23 +M V30 23 1 22 24 +M V30 24 2 23 25 +M V30 25 2 24 26 +M V30 26 1 10 13 +M V30 27 1 21 22 +M V30 28 1 25 26 +M V30 END BOND +M V30 END CTAB +M END +> +686 + +> +Z364384898 + +> +354.443 + +> +3.568 + +> +1 + +> +50.800 + +> +7 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3629677 + +> +0.86 + +$$$$ +Compound 687 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.6023 1.3819 0.0 0 +M V30 3 N 2.0787 1.5472 0.0 0 +M V30 4 C -0.2952 2.6103 0.0 0 +M V30 5 C 2.9528 0.3425 0.0 0 +M V30 6 N -1.8071 2.6221 0.0 0 +M V30 7 C 0.1535 4.0512 0.0 0 +M V30 8 C 4.4292 0.5078 0.0 0 +M V30 9 C 2.3268 -1.0157 0.0 0 +M V30 10 O -2.2795 4.0631 0.0 0 +M V30 11 C -1.0748 4.9489 0.0 0 +M V30 12 C 5.3032 -0.6968 0.0 0 +M V30 13 C 3.2008 -2.2205 0.0 0 +M V30 14 C -1.0866 6.4608 0.0 0 +M V30 15 N 4.6772 -2.0551 0.0 0 +M V30 16 C -2.3977 7.2167 0.0 0 +M V30 17 C 0.2007 7.2167 0.0 0 +M V30 18 C 5.5513 -3.2599 0.0 0 +M V30 19 O -3.7087 6.4844 0.0 0 +M V30 20 C -2.4095 8.7285 0.0 0 +M V30 21 C 0.1889 8.7285 0.0 0 +M V30 22 C 7.0277 -3.0945 0.0 0 +M V30 23 C 4.9253 -4.6182 0.0 0 +M V30 24 C -5.0198 7.2403 0.0 0 +M V30 25 C -1.122 9.4963 0.0 0 +M V30 26 C 7.9017 -4.2993 0.0 0 +M V30 27 C 5.7993 -5.8229 0.0 0 +M V30 28 C 7.2757 -5.6576 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 1 11 14 +M V30 14 1 12 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 1 16 20 +M V30 20 2 17 21 +M V30 21 2 18 22 +M V30 22 1 18 23 +M V30 23 1 19 24 +M V30 24 2 20 25 +M V30 25 1 22 26 +M V30 26 2 23 27 +M V30 27 2 26 28 +M V30 28 1 10 11 +M V30 29 1 13 15 +M V30 30 1 21 25 +M V30 31 1 27 28 +M V30 END BOND +M V30 END CTAB +M END +> +687 + +> +Z806754404 + +> +377.436 + +> +2.852 + +> +1 + +> +67.600 + +> +5 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL2443138 + +> +0.93 + +$$$$ +Compound 688 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 32 35 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 0.1426 -1.4854 0.0 0 +M V30 3 C -1.4854 0.3208 0.0 0 +M V30 4 C 1.4379 -2.2222 0.0 0 +M V30 5 C -1.2478 -2.0915 0.0 0 +M V30 6 C -2.246 -0.9744 0.0 0 +M V30 7 N 1.426 -3.7196 0.0 0 +M V30 8 N 2.7332 -1.4617 0.0 0 +M V30 9 C 2.7213 -4.4564 0.0 0 +M V30 10 C 4.0286 -2.1985 0.0 0 +M V30 11 S 2.7095 -5.9537 0.0 0 +M V30 12 C 4.0167 -3.6958 0.0 0 +M V30 13 C 5.3239 -1.4379 0.0 0 +M V30 14 C 4.0048 -6.7024 0.0 0 +M V30 15 C 5.312 -4.4326 0.0 0 +M V30 16 C 6.6192 -2.1747 0.0 0 +M V30 17 C 3.9929 -8.1998 0.0 0 +M V30 18 C 6.6074 -3.6721 0.0 0 +M V30 19 O 2.6738 -8.9366 0.0 0 +M V30 20 N 5.2883 -8.9366 0.0 0 +M V30 21 C 5.2764 -10.434 0.0 0 +M V30 22 C 3.9573 -11.1826 0.0 0 +M V30 23 C 6.5717 -11.1826 0.0 0 +M V30 24 C 3.9454 -12.68 0.0 0 +M V30 25 C 6.5598 -12.68 0.0 0 +M V30 26 C 2.6263 -13.4168 0.0 0 +M V30 27 C 5.2407 -13.4168 0.0 0 +M V30 28 C 7.8552 -13.4168 0.0 0 +M V30 29 O 1.3072 -12.6562 0.0 0 +M V30 30 N 2.6144 -14.9142 0.0 0 +M V30 31 O 9.1505 -12.6562 0.0 0 +M V30 32 N 7.8433 -14.9142 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 4 7 +M V30 7 2 4 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 11 14 +M V30 14 2 12 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 2 17 19 +M V30 19 1 17 20 +M V30 20 1 20 21 +M V30 21 2 21 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 2 23 25 +M V30 25 1 24 26 +M V30 26 2 24 27 +M V30 27 1 25 28 +M V30 28 2 26 29 +M V30 29 1 26 30 +M V30 30 2 28 31 +M V30 31 1 28 32 +M V30 32 1 5 6 +M V30 33 1 10 12 +M V30 34 2 16 18 +M V30 35 1 25 27 +M V30 END BOND +M V30 END CTAB +M END +> +688 + +> +Z16380411 + +> +463.532 + +> +2.350 + +> +3 + +> +141.060 + +> +7 + +> +parp14 + +> + + +> + + +$$$$ +Compound 689 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4964 0.0 0 +M V30 3 N -1.3301 -2.2328 0.0 0 +M V30 4 C 1.2826 -2.2328 0.0 0 +M V30 5 N -1.342 -3.7292 0.0 0 +M V30 6 C 1.2707 -3.7292 0.0 0 +M V30 7 C 2.5772 -1.4726 0.0 0 +M V30 8 C -0.0475 -4.4656 0.0 0 +M V30 9 C 2.5653 -4.4656 0.0 0 +M V30 10 C 3.8717 -2.209 0.0 0 +M V30 11 C -0.0593 -5.962 0.0 0 +M V30 12 C 3.8598 -3.7054 0.0 0 +M V30 13 C 1.2351 -6.7102 0.0 0 +M V30 14 O 2.5297 -5.9501 0.0 0 +M V30 15 N 1.2232 -8.2067 0.0 0 +M V30 16 C 2.5178 -8.943 0.0 0 +M V30 17 C 2.5059 -10.4395 0.0 0 +M V30 18 C 3.8005 -11.1877 0.0 0 +M V30 19 C 1.1876 -11.1877 0.0 0 +M V30 20 C 3.7886 -12.6842 0.0 0 +M V30 21 C 1.1757 -12.6842 0.0 0 +M V30 22 C 2.4703 -13.4205 0.0 0 +M V30 23 C 2.4584 -14.917 0.0 0 +M V30 24 N 1.1401 -15.6533 0.0 0 +M V30 25 C -0.2494 -15.0238 0.0 0 +M V30 26 C 0.9738 -17.1379 0.0 0 +M V30 27 N -1.2707 -16.1284 0.0 0 +M V30 28 C -0.5106 -17.4348 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 11 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 2 20 22 +M V30 22 1 22 23 +M V30 23 1 23 24 +M V30 24 1 24 25 +M V30 25 1 24 26 +M V30 26 2 25 27 +M V30 27 2 26 28 +M V30 28 1 6 8 +M V30 29 1 10 12 +M V30 30 1 21 22 +M V30 31 1 27 28 +M V30 END BOND +M V30 END CTAB +M END +> +689 + +> +Z203871418 + +> +373.408 + +> +0.094 + +> +2 + +> +88.380 + +> +6 + +> +parp10 + +> + + +> + + +$$$$ +Compound 690 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3106 0.7556 0.0 0 +M V30 3 N -1.3224 2.267 0.0 0 +M V30 4 C -2.6212 0.0236 0.0 0 +M V30 5 C -0.0354 3.0227 0.0 0 +M V30 6 N -2.633 -1.4641 0.0 0 +M V30 7 C -0.0472 4.534 0.0 0 +M V30 8 C 1.2515 2.2906 0.0 0 +M V30 9 N -1.4287 -2.3378 0.0 0 +M V30 10 N -3.861 -2.3378 0.0 0 +M V30 11 C 1.2397 5.2897 0.0 0 +M V30 12 C 2.5386 3.0463 0.0 0 +M V30 13 N -1.9009 -3.7547 0.0 0 +M V30 14 C -3.4123 -3.7547 0.0 0 +M V30 15 C 1.2279 6.801 0.0 0 +M V30 16 C 2.5267 4.5576 0.0 0 +M V30 17 C -4.3097 -4.9591 0.0 0 +M V30 18 O -0.0826 7.5685 0.0 0 +M V30 19 C 2.5149 7.5685 0.0 0 +M V30 20 C -5.8092 -4.7938 0.0 0 +M V30 21 C -3.7075 -6.3169 0.0 0 +M V30 22 C -6.7066 -5.9981 0.0 0 +M V30 23 C -6.435 -3.4123 0.0 0 +M V30 24 C -4.6049 -7.5213 0.0 0 +M V30 25 C -6.1044 -7.356 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 2 9 13 +M V30 13 2 10 14 +M V30 14 1 11 15 +M V30 15 2 11 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 1 15 19 +M V30 19 2 17 20 +M V30 20 1 17 21 +M V30 21 1 20 22 +M V30 22 1 20 23 +M V30 23 2 21 24 +M V30 24 2 22 25 +M V30 25 1 12 16 +M V30 26 1 13 14 +M V30 27 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +690 + +> +Z16526211 + +> +335.360 + +> +2.737 + +> +1 + +> +89.770 + +> +5 + +> +parp14 + +> + + +> + + +$$$$ +Compound 691 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3147 0.7699 0.0 0 +M V30 3 C 1.291 0.7699 0.0 0 +M V30 4 N -1.4806 2.2742 0.0 0 +M V30 5 N -2.7006 0.1658 0.0 0 +M V30 6 C 2.5821 0.0236 0.0 0 +M V30 7 N -2.9612 2.594 0.0 0 +M V30 8 N -3.7192 1.291 0.0 0 +M V30 9 C -3.0204 -1.291 0.0 0 +M V30 10 N 3.8732 0.7936 0.0 0 +M V30 11 N 2.5703 -1.4687 0.0 0 +M V30 12 C -1.9188 -2.286 0.0 0 +M V30 13 C -4.4655 -1.7411 0.0 0 +M V30 14 C 5.1643 0.0473 0.0 0 +M V30 15 C 3.8614 -2.2149 0.0 0 +M V30 16 C -2.2386 -3.7429 0.0 0 +M V30 17 C -0.4974 -1.8122 0.0 0 +M V30 18 C -4.7853 -3.1981 0.0 0 +M V30 19 C 5.1525 -1.445 0.0 0 +M V30 20 C 6.4554 0.8172 0.0 0 +M V30 21 O 3.8495 -3.7074 0.0 0 +M V30 22 C -3.6837 -4.193 0.0 0 +M V30 23 C 6.4435 -2.1912 0.0 0 +M V30 24 C 7.7465 0.071 0.0 0 +M V30 25 C 7.7346 -1.4213 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 1 12 17 +M V30 17 2 13 18 +M V30 18 2 14 19 +M V30 19 1 14 20 +M V30 20 2 15 21 +M V30 21 2 16 22 +M V30 22 1 19 23 +M V30 23 2 20 24 +M V30 24 2 23 25 +M V30 25 2 7 8 +M V30 26 1 15 19 +M V30 27 1 18 22 +M V30 28 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +691 + +> +Z16534238 + +> +350.398 + +> +1.734 + +> +1 + +> +85.060 + +> +4 + +> +parp3 + +> + + +> + + +$$$$ +Compound 692 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 29 32 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0117 -1.4836 0.0 0 +M V30 3 C 1.2717 -2.2255 0.0 0 +M V30 4 C -1.3188 -2.2255 0.0 0 +M V30 5 C 2.5552 -1.4601 0.0 0 +M V30 6 C 1.2599 -3.7092 0.0 0 +M V30 7 C -1.3306 -3.7092 0.0 0 +M V30 8 O 3.8387 -2.2019 0.0 0 +M V30 9 N 2.5434 0.0471 0.0 0 +M V30 10 C -0.0471 -4.4392 0.0 0 +M V30 11 C 3.8269 0.8007 0.0 0 +M V30 12 C 1.2364 0.8007 0.0 0 +M V30 13 C 3.8152 2.3079 0.0 0 +M V30 14 C 1.2246 2.3079 0.0 0 +M V30 15 N 2.5081 3.0733 0.0 0 +M V30 16 C 2.4963 4.5806 0.0 0 +M V30 17 O 3.7798 5.3342 0.0 0 +M V30 18 C 1.1893 5.3342 0.0 0 +M V30 19 C -0.1177 4.5923 0.0 0 +M V30 20 C 1.1775 6.8414 0.0 0 +M V30 21 C -1.4248 5.3459 0.0 0 +M V30 22 C -0.1295 7.595 0.0 0 +M V30 23 N -2.7318 4.6041 0.0 0 +M V30 24 C -1.4365 6.8532 0.0 0 +M V30 25 C -4.0389 5.3577 0.0 0 +M V30 26 S -2.7436 7.6068 0.0 0 +M V30 27 O -5.3459 4.6159 0.0 0 +M V30 28 C -4.0507 6.865 0.0 0 CFG=1 +M V30 29 C -5.3577 7.6186 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 9 11 +M V30 11 1 9 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 1 21 23 +M V30 23 2 21 24 +M V30 24 1 23 25 +M V30 25 1 24 26 +M V30 26 2 25 27 +M V30 27 1 25 28 +M V30 28 1 28 29 CFG=3 +M V30 29 1 7 10 +M V30 30 1 14 15 +M V30 31 1 22 24 +M V30 32 1 26 28 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 28) +M V30 END COLLECTION +M V30 END CTAB +M END +> +692 + +> +Z52112630 + +> +429.920 + +> +2.880 + +> +1 + +> +69.720 + +> +2 + +> +parp1 + +> + + +> + + +$$$$ +Compound 693 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3132 -0.7335 0.0 0 +M V30 3 N -1.3251 -2.2243 0.0 0 +M V30 4 C -2.6265 0.0236 0.0 0 +M V30 5 C -0.0354 -2.9578 0.0 0 +M V30 6 C -2.6384 1.538 0.0 0 +M V30 7 C -3.9398 -0.7098 0.0 0 +M V30 8 C -0.0473 -4.4486 0.0 0 +M V30 9 C 1.2541 -2.2006 0.0 0 +M V30 10 C -3.9517 2.2953 0.0 0 +M V30 11 C -5.2531 0.0473 0.0 0 +M V30 12 C 1.2423 -5.1821 0.0 0 +M V30 13 C 2.5437 -2.9342 0.0 0 +M V30 14 C -5.265 1.5617 0.0 0 +M V30 15 C -3.9635 3.8097 0.0 0 +M V30 16 N 1.2304 -6.6729 0.0 0 +M V30 17 C 2.5319 -4.4249 0.0 0 +M V30 18 N -2.6739 4.5669 0.0 0 +M V30 19 C 0.0 -7.5484 0.0 0 +M V30 20 C 2.4372 -7.5484 0.0 0 +M V30 21 C -1.3132 3.9635 0.0 0 +M V30 22 C -2.5319 6.0695 0.0 0 +M V30 23 N 0.4495 -8.9682 0.0 0 +M V30 24 C 1.964 -8.9682 0.0 0 +M V30 25 N -0.3194 5.0875 0.0 0 +M V30 26 C -1.0766 6.389 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 2 10 14 +M V30 14 1 10 15 +M V30 15 1 12 16 +M V30 16 2 12 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 1 16 20 +M V30 20 1 18 21 +M V30 21 1 18 22 +M V30 22 2 19 23 +M V30 23 2 20 24 +M V30 24 2 21 25 +M V30 25 2 22 26 +M V30 26 1 11 14 +M V30 27 1 13 17 +M V30 28 1 23 24 +M V30 29 1 25 26 +M V30 END BOND +M V30 END CTAB +M END +> +693 + +> +Z510051912 + +> +343.382 + +> +2.221 + +> +1 + +> +64.740 + +> +5 + +> +parp14 + +> + + +> + + +$$$$ +Compound 694 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 30 32 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3161 -0.7351 0.0 0 +M V30 3 C 1.2924 -0.7351 0.0 0 +M V30 4 N -2.7034 -0.1067 0.0 0 +M V30 5 N -1.4821 -2.2172 0.0 0 +M V30 6 C 2.5848 0.0237 0.0 0 +M V30 7 N -3.7231 -1.2094 0.0 0 +M V30 8 N -2.9642 -2.5136 0.0 0 +M V30 9 C -0.3794 -3.2132 0.0 0 +M V30 10 O 2.5729 1.5414 0.0 0 +M V30 11 N 3.8772 -0.7114 0.0 0 +M V30 12 C -0.6995 -4.6716 0.0 0 +M V30 13 C 1.0434 -2.7389 0.0 0 +M V30 14 C 5.1696 0.0474 0.0 0 +M V30 15 C 0.4031 -5.6676 0.0 0 +M V30 16 C -2.1461 -5.1222 0.0 0 +M V30 17 C 2.1461 -3.7349 0.0 0 +M V30 18 C 5.1578 1.5651 0.0 0 +M V30 19 C 6.462 -0.6877 0.0 0 +M V30 20 C 1.8259 -5.1933 0.0 0 +M V30 21 C 6.4502 2.3239 0.0 0 +M V30 22 C 7.7545 0.0711 0.0 0 +M V30 23 C 2.9286 -6.1893 0.0 0 +M V30 24 C 6.4383 3.8416 0.0 0 +M V30 25 C 7.7426 1.5888 0.0 0 +M V30 26 C 9.0469 -0.6639 0.0 0 +M V30 27 O 7.7308 4.6005 0.0 0 +M V30 28 N 5.1222 4.6005 0.0 0 +M V30 29 O 10.3393 0.0948 0.0 0 +M V30 30 N 9.035 -2.1579 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 1 11 14 +M V30 14 1 12 15 +M V30 15 1 12 16 +M V30 16 2 13 17 +M V30 17 2 14 18 +M V30 18 1 14 19 +M V30 19 2 15 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 1 20 23 +M V30 23 1 21 24 +M V30 24 2 21 25 +M V30 25 1 22 26 +M V30 26 2 24 27 +M V30 27 1 24 28 +M V30 28 2 26 29 +M V30 29 1 26 30 +M V30 30 2 7 8 +M V30 31 1 17 20 +M V30 32 1 22 25 +M V30 END BOND +M V30 END CTAB +M END +> +694 + +> +Z16587308 + +> +425.464 + +> +0.579 + +> +3 + +> +158.880 + +> +7 + +> +parp14 + +> + + +> + + +$$$$ +Compound 695 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 35 38 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.3049 0.7523 0.0 0 +M V30 3 C -2.6098 0.0117 0.0 0 +M V30 4 C -1.3166 2.2571 0.0 0 +M V30 5 C -3.9147 0.7641 0.0 0 +M V30 6 C -2.6215 3.0095 0.0 0 +M V30 7 C -5.2196 0.0235 0.0 0 +M V30 8 C -3.9265 2.2689 0.0 0 +M V30 9 O -5.2314 -1.4577 0.0 0 +M V30 10 N -6.5245 0.7758 0.0 0 +M V30 11 N -5.2314 3.0212 0.0 0 +M V30 12 C -6.5363 2.2806 0.0 0 +M V30 13 C -7.8295 0.0352 0.0 0 +M V30 14 S -7.8412 3.033 0.0 0 +M V30 15 C -9.1344 0.7876 0.0 0 +M V30 16 C -7.8412 -1.4459 0.0 0 +M V30 17 C -9.1461 2.3041 0.0 0 +M V30 18 C -10.4393 0.047 0.0 0 +M V30 19 C -9.1461 -2.1866 0.0 0 +M V30 20 C -10.451 3.0565 0.0 0 +M V30 21 C -10.0396 1.105 0.0 0 +M V30 22 C -10.451 -1.4342 0.0 0 +M V30 23 O -11.756 2.3276 0.0 0 +M V30 24 N -10.4628 4.5613 0.0 0 +M V30 25 C -11.756 -2.1748 0.0 0 +M V30 26 C -11.7677 5.3137 0.0 0 +M V30 27 C -9.1814 5.3137 0.0 0 +M V30 28 C -11.7677 -3.6561 0.0 0 +M V30 29 C -13.0609 -1.4107 0.0 0 +M V30 30 C -13.0726 4.5848 0.0 0 +M V30 31 C -11.7795 6.8184 0.0 0 +M V30 32 C -13.0726 -4.3849 0.0 0 +M V30 33 C -14.3775 5.3372 0.0 0 +M V30 34 C -13.0844 7.5708 0.0 0 +M V30 35 C -14.3893 6.8419 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 1 10 12 +M V30 12 1 10 13 +M V30 13 1 12 14 +M V30 14 2 13 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 2 16 19 +M V30 19 1 17 20 +M V30 20 1 17 21 +M V30 21 2 18 22 +M V30 22 2 20 23 +M V30 23 1 20 24 +M V30 24 1 22 25 +M V30 25 1 24 26 +M V30 26 1 24 27 +M V30 27 1 25 28 +M V30 28 1 25 29 +M V30 29 2 26 30 +M V30 30 1 26 31 +M V30 31 1 28 32 +M V30 32 1 30 33 +M V30 33 2 31 34 +M V30 34 2 33 35 +M V30 35 1 6 8 +M V30 36 2 11 12 +M V30 37 1 19 22 +M V30 38 1 34 35 +M V30 END BOND +M V30 END CTAB +M END +> +695 + +> +Z16661687 + +> +506.059 + +> +7.395 + +> +0 + +> +52.980 + +> +7 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL445683 + +> +0.85 + +$$$$ +Compound 696 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 36 39 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2881 0.7681 0.0 0 +M V30 3 C 1.2763 2.2809 0.0 0 +M V30 4 C 2.5763 0.0236 0.0 0 +M V30 5 C 2.5645 3.0372 0.0 0 +M V30 6 C 3.8645 0.7918 0.0 0 +M V30 7 C 2.5527 4.5499 0.0 0 +M V30 8 C 3.8527 2.3045 0.0 0 +M V30 9 O 1.2409 5.3181 0.0 0 +M V30 10 N 3.8408 5.3181 0.0 0 +M V30 11 N 5.1408 3.0608 0.0 0 +M V30 12 C 5.129 4.5736 0.0 0 +M V30 13 C 3.829 6.8308 0.0 0 +M V30 14 S 6.4172 5.3417 0.0 0 +M V30 15 C 5.1172 7.5872 0.0 0 +M V30 16 C 2.5172 7.5872 0.0 0 +M V30 17 C 7.7054 4.5972 0.0 0 +M V30 18 C 5.1054 9.0999 0.0 0 +M V30 19 C 2.5054 9.0999 0.0 0 +M V30 20 C 8.9936 5.3654 0.0 0 +M V30 21 C 7.6936 3.1081 0.0 0 +M V30 22 C 3.7936 9.8563 0.0 0 +M V30 23 O 8.9817 6.8781 0.0 0 +M V30 24 N 10.2817 4.6208 0.0 0 +M V30 25 C 3.7818 11.369 0.0 0 +M V30 26 C 11.5699 5.389 0.0 0 +M V30 27 C 10.2699 3.1318 0.0 0 +M V30 28 C 2.4699 12.1254 0.0 0 +M V30 29 C 5.0699 12.1254 0.0 0 +M V30 30 C 12.8581 4.6445 0.0 0 +M V30 31 C 2.4581 13.6381 0.0 0 +M V30 32 C 14.1463 5.4127 0.0 0 +M V30 33 C 12.8463 3.1554 0.0 0 +M V30 34 C 15.4344 4.6681 0.0 0 +M V30 35 C 14.1344 2.4227 0.0 0 +M V30 36 C 15.4226 3.179 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 1 10 12 +M V30 12 1 10 13 +M V30 13 1 12 14 +M V30 14 2 13 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 2 16 19 +M V30 19 1 17 20 +M V30 20 1 17 21 +M V30 21 2 18 22 +M V30 22 2 20 23 +M V30 23 1 20 24 +M V30 24 1 22 25 +M V30 25 1 24 26 +M V30 26 1 24 27 +M V30 27 1 25 28 +M V30 28 1 25 29 +M V30 29 1 26 30 +M V30 30 1 28 31 +M V30 31 2 30 32 +M V30 32 1 30 33 +M V30 33 1 32 34 +M V30 34 2 33 35 +M V30 35 2 34 36 +M V30 36 1 6 8 +M V30 37 2 11 12 +M V30 38 1 19 22 +M V30 39 1 35 36 +M V30 END BOND +M V30 END CTAB +M END +> +696 + +> +Z16661610 + +> +520.085 + +> +7.469 + +> +0 + +> +52.980 + +> +8 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL445683 + +> +0.87 + +$$$$ +Compound 697 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.4935 0.0118 0.0 0 +M V30 3 N 2.3707 -1.1972 0.0 0 +M V30 4 C 2.3707 1.2446 0.0 0 +M V30 5 C 3.7931 -0.723 0.0 0 +M V30 6 C 3.7931 0.7941 0.0 0 +M V30 7 C 5.0851 -1.4579 0.0 0 +M V30 8 C 5.0851 1.5528 0.0 0 +M V30 9 C 6.3772 -0.6993 0.0 0 +M V30 10 C 6.3772 0.8178 0.0 0 +M V30 11 C 7.6692 1.5765 0.0 0 CFG=1 +M V30 12 N 8.9613 0.8416 0.0 0 +M V30 13 C 7.6574 3.0937 0.0 0 +M V30 14 C 10.2533 1.6002 0.0 0 +M V30 15 O 10.2415 3.1174 0.0 0 +M V30 16 C 11.5454 0.8653 0.0 0 +M V30 17 N 11.6876 -0.6163 0.0 0 +M V30 18 C 12.9085 1.4935 0.0 0 +M V30 19 C 10.5615 -1.612 0.0 0 +M V30 20 C 13.1456 -0.9127 0.0 0 +M V30 21 N 13.9042 0.3911 0.0 0 +M V30 22 C 10.8578 -3.07 0.0 0 +M V30 23 C 9.1154 -1.1379 0.0 0 +M V30 24 C 9.7318 -4.0657 0.0 0 +M V30 25 C 7.9893 -2.1336 0.0 0 +M V30 26 C 8.2856 -3.5916 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 2 8 10 +M V30 10 1 10 11 +M V30 11 1 11 12 CFG=3 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 2 16 18 +M V30 18 1 17 19 +M V30 19 1 17 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 1 19 23 +M V30 23 1 22 24 +M V30 24 2 23 25 +M V30 25 2 24 26 +M V30 26 2 5 6 +M V30 27 1 9 10 +M V30 28 2 20 21 +M V30 29 1 25 26 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 11) +M V30 END COLLECTION +M V30 END CTAB +M END +> +697 + +> +Z510556462 + +> +346.383 + +> +2.718 + +> +2 + +> +76.020 + +> +4 + +> +parp15 + +> + + +> + + +$$$$ +Compound 698 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.7553 -1.2864 0.0 0 +M V30 3 C -1.0149 1.1211 0.0 0 +M V30 4 N -2.2305 -0.9677 0.0 0 +M V30 5 C -0.1534 -2.6436 0.0 0 +M V30 6 C -2.3957 0.5192 0.0 0 +M V30 7 O -0.9087 -3.93 0.0 0 +M V30 8 C 1.2982 -2.9386 0.0 0 +M V30 9 C -3.7058 1.2746 0.0 0 +M V30 10 C 0.0826 -5.0276 0.0 0 +M V30 11 C 1.4398 -4.4139 0.0 0 +M V30 12 O -3.7176 2.7852 0.0 0 +M V30 13 N -5.0158 0.5428 0.0 0 +M V30 14 C -6.3258 1.2982 0.0 0 CFG=2 +M V30 15 C -7.6358 0.5664 0.0 0 +M V30 16 C -6.3376 2.8088 0.0 0 +M V30 17 C -8.9458 1.3218 0.0 0 +M V30 18 C -7.6476 -0.9205 0.0 0 +M V30 19 C -10.2558 0.59 0.0 0 +M V30 20 C -8.9576 -1.6522 0.0 0 +M V30 21 C -10.2676 -0.8969 0.0 0 +M V30 22 C -11.6957 1.0621 0.0 0 +M V30 23 N -11.7075 -1.3454 0.0 0 +M V30 24 C -12.5926 -0.1416 0.0 0 +M V30 25 O -14.1033 -0.1298 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 1 14 13 CFG=3 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 1 15 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 2 19 21 +M V30 21 1 19 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 2 24 25 +M V30 25 1 4 6 +M V30 26 2 10 11 +M V30 27 1 20 21 +M V30 28 1 23 24 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 14) +M V30 END COLLECTION +M V30 END CTAB +M END +> +698 + +> +Z510545558 + +> +353.395 + +> +2.641 + +> +2 + +> +84.230 + +> +4 + +> +parp14 + +> + + +> + + +$$$$ +Compound 699 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 29 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.5151 0.0 0 +M V30 3 C 1.2902 -0.7338 0.0 0 +M V30 4 C 1.2783 2.2726 0.0 0 +M V30 5 C -1.3257 2.2726 0.0 0 +M V30 6 C 1.2783 -2.2253 0.0 0 +M V30 7 O 2.5685 1.5387 0.0 0 +M V30 8 C 1.2665 3.7877 0.0 0 +M V30 9 C -1.3375 3.7877 0.0 0 +M V30 10 C 2.5685 -2.9591 0.0 0 +M V30 11 C -0.0355 -2.9591 0.0 0 +M V30 12 C 3.8587 2.2963 0.0 0 +M V30 13 C -0.0473 4.5453 0.0 0 +M V30 14 C -2.6514 4.5453 0.0 0 +M V30 15 C 2.5567 -4.4506 0.0 0 +M V30 16 C -0.0473 -4.4506 0.0 0 +M V30 17 C 5.149 1.5624 0.0 0 +M V30 18 N -3.9653 3.8114 0.0 0 +M V30 19 C 1.2428 -5.1963 0.0 0 +M V30 20 C 6.4392 2.32 0.0 0 +M V30 21 C 5.1371 0.071 0.0 0 +M V30 22 C -5.2792 4.5689 0.0 0 CFG=2 +M V30 23 C 7.7294 1.5861 0.0 0 +M V30 24 C 6.4273 -0.6628 0.0 0 +M V30 25 C -5.291 6.0841 0.0 0 +M V30 26 C -6.593 3.8351 0.0 0 +M V30 27 C 7.7175 0.0946 0.0 0 +M V30 28 O -4.0008 6.8416 0.0 0 +M V30 29 C -7.9069 4.5926 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 1 7 12 +M V30 12 2 8 13 +M V30 13 1 9 14 +M V30 14 1 10 15 +M V30 15 2 11 16 +M V30 16 1 12 17 +M V30 17 1 14 18 +M V30 18 2 15 19 +M V30 19 2 17 20 +M V30 20 1 17 21 +M V30 21 1 22 18 CFG=1 +M V30 22 1 20 23 +M V30 23 2 21 24 +M V30 24 1 22 25 +M V30 25 1 22 26 +M V30 26 2 23 27 +M V30 27 1 25 28 +M V30 28 1 26 29 +M V30 29 1 9 13 +M V30 30 1 16 19 +M V30 31 1 24 27 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 22) +M V30 END COLLECTION +M V30 END CTAB +M END +> +699 + +> +Z86134003 + +> +391.503 + +> +4.950 + +> +2 + +> +50.720 + +> +11 + +> +ATM + +> + + +> + + +$$$$ +Compound 700 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 33 36 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.3051 0.7525 0.0 0 +M V30 3 C -2.6103 0.0117 0.0 0 +M V30 4 C -1.3169 2.2575 0.0 0 +M V30 5 C -3.9154 0.7642 0.0 0 +M V30 6 C -2.622 3.0101 0.0 0 +M V30 7 C -5.2206 0.0235 0.0 0 +M V30 8 C -3.9272 2.2693 0.0 0 +M V30 9 O -5.2324 -1.458 0.0 0 +M V30 10 N -6.5258 0.776 0.0 0 +M V30 11 N -5.2324 3.0218 0.0 0 +M V30 12 C -6.5375 2.281 0.0 0 +M V30 13 C -7.8309 0.0352 0.0 0 +M V30 14 S -7.8427 3.0336 0.0 0 +M V30 15 C -9.1361 0.7878 0.0 0 +M V30 16 C -7.8427 -1.4462 0.0 0 +M V30 17 C -7.8544 4.5386 0.0 0 +M V30 18 F -9.1478 2.2928 0.0 0 +M V30 19 C -10.4412 0.047 0.0 0 +M V30 20 C -9.1478 -2.187 0.0 0 +M V30 21 C -6.5728 5.2911 0.0 0 +M V30 22 C -10.453 -1.4345 0.0 0 +M V30 23 O -5.2911 4.5621 0.0 0 +M V30 24 N -6.5845 6.7962 0.0 0 +M V30 25 F -11.7582 -2.1752 0.0 0 +M V30 26 C -5.3029 7.5487 0.0 0 +M V30 27 C -7.8897 7.5487 0.0 0 +M V30 28 C -5.3147 9.0538 0.0 0 +M V30 29 C -7.9015 9.0538 0.0 0 +M V30 30 C -6.6198 9.8181 0.0 0 +M V30 31 C -6.6316 11.3231 0.0 0 +M V30 32 O -5.3499 12.0756 0.0 0 +M V30 33 N -7.9367 12.0756 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 1 10 12 +M V30 12 1 10 13 +M V30 13 1 12 14 +M V30 14 2 13 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 15 19 +M V30 19 2 16 20 +M V30 20 1 17 21 +M V30 21 2 19 22 +M V30 22 2 21 23 +M V30 23 1 21 24 +M V30 24 1 22 25 +M V30 25 1 24 26 +M V30 26 1 24 27 +M V30 27 1 26 28 +M V30 28 1 27 29 +M V30 29 1 28 30 +M V30 30 1 30 31 +M V30 31 2 31 32 +M V30 32 1 31 33 +M V30 33 1 6 8 +M V30 34 2 11 12 +M V30 35 1 20 22 +M V30 36 1 29 30 +M V30 END BOND +M V30 END CTAB +M END +> +700 + +> +Z16708902 + +> +492.926 + +> +2.270 + +> +1 + +> +96.070 + +> +4 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL461264 + +> +0.85 + +$$$$ +Compound 701 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.4196 0.4732 0.0 0 +M V30 3 C -0.8991 1.2303 0.0 0 +M V30 4 N 2.7091 -0.2602 0.0 0 +M V30 5 C 1.4078 1.9874 0.0 0 +M V30 6 C -0.0236 2.4607 0.0 0 +M V30 7 C -2.4133 1.2421 0.0 0 +M V30 8 C 3.9986 0.4968 0.0 0 +M V30 9 C 2.6973 2.7446 0.0 0 +M V30 10 C -0.4968 3.904 0.0 0 +M V30 11 S 5.2881 -0.2366 0.0 0 +M V30 12 N 3.9868 2.0111 0.0 0 +M V30 13 O 2.6854 4.2589 0.0 0 +M V30 14 C 6.5776 0.5205 0.0 0 +M V30 15 C 5.2763 2.7683 0.0 0 +M V30 16 C 7.8671 -0.2129 0.0 0 +M V30 17 O 7.8553 -1.7035 0.0 0 +M V30 18 N 9.1566 0.5441 0.0 0 +M V30 19 C 10.4461 -0.1892 0.0 0 +M V30 20 C 9.1448 2.0584 0.0 0 +M V30 21 C 11.7357 0.5678 0.0 0 +M V30 22 C 10.4343 2.8156 0.0 0 +M V30 23 O 11.7238 2.0821 0.0 0 +M V30 24 C 13.0252 -0.1656 0.0 0 +M V30 25 C 10.4225 4.3299 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 8 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 11 14 +M V30 14 1 12 15 +M V30 15 1 14 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 21 24 +M V30 24 1 22 25 +M V30 25 1 5 6 +M V30 26 1 9 12 +M V30 27 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +701 + +> +Z16722690 + +> +381.513 + +> +2.667 + +> +0 + +> +62.210 + +> +3 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL2440886 + +> +0.88 + +$$$$ +Compound 702 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 -1.4866 0.0 0 +M V30 3 N -1.3214 -2.2299 0.0 0 +M V30 4 N 1.2742 -2.2299 0.0 0 +M V30 5 C -2.631 -1.463 0.0 0 +M V30 6 C 2.5602 -1.463 0.0 0 +M V30 7 C -3.9406 -2.2063 0.0 0 +M V30 8 C 3.8463 -2.2063 0.0 0 +M V30 9 N -5.2503 -1.4394 0.0 0 +M V30 10 C 5.1323 -1.4394 0.0 0 +M V30 11 C -5.2621 0.0707 0.0 0 +M V30 12 C -6.5599 -2.1827 0.0 0 +M V30 13 C 6.4891 -2.0411 0.0 0 +M V30 14 C 5.2739 0.0589 0.0 0 +M V30 15 C -6.5717 0.8258 0.0 0 +M V30 16 C -7.8695 -1.4158 0.0 0 +M V30 17 C 7.4802 -0.9202 0.0 0 +M V30 18 C 6.9375 -3.4569 0.0 0 +M V30 19 N 6.7251 0.3775 0.0 0 +M V30 20 C -7.8813 0.0943 0.0 0 +M V30 21 C -6.5835 2.336 0.0 0 +M V30 22 C 8.9314 -1.2152 0.0 0 +M V30 23 C 8.3887 -3.7519 0.0 0 +M V30 24 C -9.191 0.8494 0.0 0 +M V30 25 C -7.8931 3.0912 0.0 0 +M V30 26 C 9.3797 -2.631 0.0 0 +M V30 27 C -9.2028 2.3596 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 1 9 12 +M V30 12 1 10 13 +M V30 13 2 10 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 2 13 17 +M V30 17 1 13 18 +M V30 18 1 14 19 +M V30 19 2 15 20 +M V30 20 1 15 21 +M V30 21 1 17 22 +M V30 22 2 18 23 +M V30 23 1 20 24 +M V30 24 2 21 25 +M V30 25 2 22 26 +M V30 26 2 24 27 +M V30 27 1 16 20 +M V30 28 1 17 19 +M V30 29 1 23 26 +M V30 30 1 25 27 +M V30 END BOND +M V30 END CTAB +M END +> +702 + +> +Z332571766 + +> +362.468 + +> +3.893 + +> +3 + +> +60.160 + +> +6 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL559043 + +> +0.86 + +$$$$ +Compound 703 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.2363 -0.8477 0.0 0 +M V30 3 C 1.1774 -0.8948 0.0 0 +M V30 4 N -2.7081 -0.4945 0.0 0 +M V30 5 C -0.8124 -2.2724 0.0 0 +M V30 6 C 0.6711 -2.296 0.0 0 +M V30 7 C 2.661 -0.9184 0.0 0 +M V30 8 C -3.7442 -1.5777 0.0 0 +M V30 9 C -1.8485 -3.3557 0.0 0 +M V30 10 C 1.8485 -3.1908 0.0 0 +M V30 11 C 3.0848 -2.3431 0.0 0 +M V30 12 S -5.216 -1.2245 0.0 0 +M V30 13 N -3.3203 -3.0024 0.0 0 +M V30 14 O -1.4247 -4.7804 0.0 0 +M V30 15 C -5.6634 0.2237 0.0 0 +M V30 16 C -4.3565 -4.0857 0.0 0 +M V30 17 C -7.1353 0.5769 0.0 0 +M V30 18 O -8.1714 -0.5063 0.0 0 +M V30 19 N -7.5827 2.0252 0.0 0 +M V30 20 C -9.0545 2.3784 0.0 0 +M V30 21 C -6.5701 3.1319 0.0 0 +M V30 22 C -9.5019 3.8266 0.0 0 +M V30 23 C -7.0175 4.5802 0.0 0 +M V30 24 O -8.4893 4.9334 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 12 15 +M V30 15 1 13 16 +M V30 16 1 15 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 5 6 +M V30 25 1 9 13 +M V30 26 1 10 11 +M V30 27 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +703 + +> +Z16746675 + +> +365.470 + +> +1.694 + +> +0 + +> +62.210 + +> +3 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL2440673 + +> +0.93 + +$$$$ +Compound 704 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.4201 0.4733 0.0 0 +M V30 3 C -0.8994 1.2307 0.0 0 +M V30 4 N 2.71 -0.2603 0.0 0 +M V30 5 C 1.4082 1.9881 0.0 0 +M V30 6 C -0.0236 2.4615 0.0 0 +M V30 7 C -2.4142 1.2426 0.0 0 +M V30 8 C 4.0 0.497 0.0 0 +M V30 9 C 2.6982 2.7455 0.0 0 +M V30 10 C -0.497 3.9053 0.0 0 +M V30 11 S 5.2899 -0.2366 0.0 0 +M V30 12 N 3.9881 2.0118 0.0 0 +M V30 13 O 2.6863 4.2603 0.0 0 +M V30 14 C 6.5798 0.5207 0.0 0 +M V30 15 C 5.2781 2.7692 0.0 0 +M V30 16 C 7.8698 -0.213 0.0 0 +M V30 17 C 5.2662 4.284 0.0 0 +M V30 18 O 7.858 -1.7041 0.0 0 +M V30 19 N 9.1597 0.5443 0.0 0 +M V30 20 C 10.4497 -0.1893 0.0 0 +M V30 21 C 11.7396 0.568 0.0 0 +M V30 22 O 13.0296 -0.1656 0.0 0 +M V30 23 C 14.3195 0.5917 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 8 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 11 14 +M V30 14 1 12 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 1 19 20 +M V30 20 1 20 21 +M V30 21 1 21 22 +M V30 22 1 22 23 +M V30 23 1 5 6 +M V30 24 1 9 12 +M V30 END BOND +M V30 END CTAB +M END +> +704 + +> +Z16780475 + +> +355.476 + +> +2.044 + +> +1 + +> +71.000 + +> +7 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL2440886 + +> +0.91 + +$$$$ +Compound 705 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4931 0.0 0 +M V30 3 N -1.3272 -2.2396 0.0 0 +M V30 4 N 1.2798 -2.2396 0.0 0 +M V30 5 C -2.6426 -1.4812 0.0 0 +M V30 6 C 2.5715 -1.4812 0.0 0 +M V30 7 C -3.9579 -2.2278 0.0 0 +M V30 8 C -2.6544 0.0355 0.0 0 +M V30 9 C 3.8631 -2.2278 0.0 0 CFG=2 +M V30 10 C -5.2733 -1.4694 0.0 0 +M V30 11 C -3.9698 -3.7209 0.0 0 +M V30 12 C -3.9698 0.7939 0.0 0 +M V30 13 O 3.8513 -3.7209 0.0 0 +M V30 14 C 5.1548 -1.4694 0.0 0 +M V30 15 C -5.2852 0.0474 0.0 0 +M V30 16 N 6.4465 -2.2159 0.0 0 +M V30 17 C -6.6005 0.8058 0.0 0 +M V30 18 C 6.4346 -3.7091 0.0 0 +M V30 19 C 7.7382 -1.4575 0.0 0 +M V30 20 C 7.7263 -4.4438 0.0 0 +M V30 21 C 9.0298 -2.2041 0.0 0 +M V30 22 C 9.018 -3.6854 0.0 0 +M V30 23 C 7.7145 -5.9369 0.0 0 +M V30 24 C 10.3097 -4.4201 0.0 0 +M V30 25 C 9.0061 -6.6716 0.0 0 +M V30 26 C 10.2978 -5.9132 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 9 13 CFG=1 +M V30 13 1 9 14 +M V30 14 2 10 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 16 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 1 20 23 +M V30 23 1 22 24 +M V30 24 2 23 25 +M V30 25 2 24 26 +M V30 26 1 12 15 +M V30 27 1 21 22 +M V30 28 1 25 26 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 9) +M V30 END COLLECTION +M V30 END CTAB +M END +> +705 + +> +Z409564088 + +> +353.458 + +> +2.926 + +> +3 + +> +64.600 + +> +5 + +> +ATM + +> + + +> + + +$$$$ +Compound 706 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.3188 -1.4525 0.0 0 +M V30 3 N -1.7005 -2.0548 0.0 0 +M V30 4 C 0.6731 -2.5508 0.0 0 +M V30 5 C -1.5588 -3.531 0.0 0 +M V30 6 C -3.0113 -1.299 0.0 0 +M V30 7 C -0.0944 -3.838 0.0 0 +M V30 8 C 2.1611 -2.539 0.0 0 +M V30 9 O -2.6807 -4.5229 0.0 0 +M V30 10 C -4.3222 -2.0312 0.0 0 +M V30 11 C 0.6495 -5.1252 0.0 0 +M V30 12 C 2.9051 -3.8262 0.0 0 +M V30 13 C -5.633 -1.2754 0.0 0 +M V30 14 C 2.1375 -5.1134 0.0 0 +M V30 15 C -6.9439 -2.0075 0.0 0 +M V30 16 N -8.2547 -1.2517 0.0 0 +M V30 17 C -8.2665 0.2598 0.0 0 +M V30 18 C -9.5656 -1.9839 0.0 0 +M V30 19 C -9.5774 1.0156 0.0 0 +M V30 20 C -10.8764 -1.2281 0.0 0 +M V30 21 C -10.8882 0.2716 0.0 0 +M V30 22 C -12.1991 1.0274 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 10 13 +M V30 13 2 11 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 21 22 +M V30 22 1 5 7 +M V30 23 1 12 14 +M V30 24 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +706 + +> +Z138290104 + +> +300.395 + +> +3.608 + +> +0 + +> +40.620 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL598088 + +> +0.88 + +$$$$ +Compound 707 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 31 35 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.5055 0.1659 0.0 0 +M V30 3 C -2.4065 -1.0432 0.0 0 +M V30 4 C -2.1339 1.553 0.0 0 +M V30 5 O -1.8019 -2.4065 0.0 0 +M V30 6 C -3.9121 -0.8772 0.0 0 +M V30 7 C -3.6394 1.7189 0.0 0 +M V30 8 C -0.32 -2.5488 0.0 0 +M V30 9 C -4.5404 0.5097 0.0 0 +M V30 10 C 0.2845 -3.9121 0.0 0 +M V30 11 N -0.486 -5.2043 0.0 0 +M V30 12 N 1.7426 -4.2085 0.0 0 +M V30 13 N 0.5097 -6.3068 0.0 0 +M V30 14 C 1.8849 -5.6904 0.0 0 +M V30 15 C 2.8452 -3.189 0.0 0 +M V30 16 S 3.1771 -6.4254 0.0 0 +M V30 17 C 3.2956 -1.7426 0.0 0 +M V30 18 C 4.3033 -2.8689 0.0 0 +M V30 19 C 4.4693 -5.6667 0.0 0 CFG=2 +M V30 20 C 5.7615 -6.4017 0.0 0 +M V30 21 C 4.4574 -4.1492 0.0 0 +M V30 22 N 7.0537 -5.6429 0.0 0 +M V30 23 N 5.7496 -7.8954 0.0 0 +M V30 24 C 8.3459 -6.378 0.0 0 +M V30 25 C 7.0418 -8.6423 0.0 0 +M V30 26 C 8.334 -7.8717 0.0 0 +M V30 27 C 9.6381 -5.6192 0.0 0 +M V30 28 O 7.03 -10.136 0.0 0 +M V30 29 C 9.6262 -8.6186 0.0 0 +M V30 30 C 10.9303 -6.3542 0.0 0 +M V30 31 C 10.9184 -7.848 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 8 10 +M V30 10 2 10 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 1 12 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 15 18 +M V30 18 1 19 16 CFG=1 +M V30 19 1 19 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 1 20 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 2 24 26 +M V30 26 1 24 27 +M V30 27 2 25 28 +M V30 28 1 26 29 +M V30 29 2 27 30 +M V30 30 2 29 31 +M V30 31 1 7 9 +M V30 32 2 13 14 +M V30 33 1 17 18 +M V30 34 1 25 26 +M V30 35 1 30 31 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 19) +M V30 END COLLECTION +M V30 END CTAB +M END +> +707 + +> +Z16825128 + +> +453.945 + +> +2.683 + +> +1 + +> +81.400 + +> +7 + +> +parp10 + +> + + +> + + +$$$$ +Compound 708 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.2962 -0.7373 0.0 0 +M V30 3 F 0.5351 -2.0335 0.0 0 +M V30 4 F 2.0454 0.5827 0.0 0 +M V30 5 C 2.5924 -1.4865 0.0 0 +M V30 6 C 2.5805 -2.9849 0.0 0 +M V30 7 C 3.8887 -0.7135 0.0 0 +M V30 8 C 3.8768 -3.7222 0.0 0 +M V30 9 C 5.1849 -1.4627 0.0 0 +M V30 10 C 5.173 -2.9611 0.0 0 +M V30 11 N 6.4692 -3.6984 0.0 0 +M V30 12 C 7.7655 -2.9373 0.0 0 +M V30 13 O 7.7536 -1.4151 0.0 0 +M V30 14 N 9.0617 -3.6746 0.0 0 +M V30 15 C 10.3579 -2.9135 0.0 0 +M V30 16 C 11.6542 -3.6508 0.0 0 CFG=2 +M V30 17 O 11.6423 -5.1492 0.0 0 +M V30 18 C 12.9504 -2.8897 0.0 0 +M V30 19 N 14.2467 -3.627 0.0 0 +M V30 20 C 15.5429 -2.8659 0.0 0 +M V30 21 C 14.2348 -5.1254 0.0 0 +M V30 22 C 16.8391 -3.6032 0.0 0 +M V30 23 C 15.531 -5.8746 0.0 0 +M V30 24 C 16.8272 -5.1016 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 2 5 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 2 8 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 16 17 CFG=1 +M V30 17 1 16 18 +M V30 18 1 18 19 +M V30 19 1 19 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 9 10 +M V30 25 1 23 24 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 16) +M V30 END COLLECTION +M V30 END CTAB +M END +> +708 + +> +Z409948072 + +> +345.360 + +> +3.240 + +> +3 + +> +64.600 + +> +6 + +> +ATM + +> + + +> + + +$$$$ +Compound 709 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4955 0.0 0 +M V30 3 N -1.3293 -2.2432 0.0 0 +M V30 4 N 1.2818 -2.2432 0.0 0 +M V30 5 C -2.6468 -1.4836 0.0 0 +M V30 6 C 2.5755 -1.4836 0.0 0 +M V30 7 C -3.9642 -2.2313 0.0 0 +M V30 8 C 3.8693 -2.2313 0.0 0 CFG=2 +M V30 9 N -5.3529 -1.6023 0.0 0 +M V30 10 C -4.1304 -3.715 0.0 0 +M V30 11 O 3.8574 -3.7269 0.0 0 +M V30 12 C 5.163 -1.4717 0.0 0 +M V30 13 N -6.3737 -2.7061 0.0 0 +M V30 14 C -5.614 -4.0117 0.0 0 +M V30 15 N 6.4568 -2.2195 0.0 0 +M V30 16 C 6.4449 -3.715 0.0 0 +M V30 17 C 7.7505 -1.4599 0.0 0 +M V30 18 C 7.7386 -4.4509 0.0 0 +M V30 19 C 9.0442 -2.2076 0.0 0 +M V30 20 C 9.0324 -3.6912 0.0 0 +M V30 21 C 7.7267 -5.9464 0.0 0 +M V30 22 C 10.3261 -4.4271 0.0 0 +M V30 23 C 9.0205 -6.6823 0.0 0 +M V30 24 C 10.3142 -5.9226 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 8 11 CFG=1 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 12 15 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 1 18 21 +M V30 21 1 20 22 +M V30 22 2 21 23 +M V30 23 2 22 24 +M V30 24 1 13 14 +M V30 25 1 19 20 +M V30 26 1 23 24 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 8) +M V30 END COLLECTION +M V30 END CTAB +M END +> +709 + +> +Z511021348 + +> +329.397 + +> +0.812 + +> +4 + +> +93.280 + +> +6 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 710 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.0944 1.012 0.0 0 +M V30 3 N 0.9297 2.5067 0.0 0 +M V30 4 N 2.542 0.7178 0.0 0 +M V30 5 C -0.3765 3.2598 0.0 0 +M V30 6 C 2.283 3.1304 0.0 0 +M V30 7 C 3.2716 2.0241 0.0 0 +M V30 8 C -1.6829 2.5302 0.0 0 +M V30 9 C -0.3883 4.7662 0.0 0 +M V30 10 C -2.9892 3.2834 0.0 0 +M V30 11 C -1.6946 5.5194 0.0 0 +M V30 12 N -4.2955 2.5537 0.0 0 +M V30 13 C -3.0009 4.7898 0.0 0 +M V30 14 C -5.6018 3.3069 0.0 0 +M V30 15 O -5.6135 4.8133 0.0 0 +M V30 16 N -6.9081 2.5773 0.0 0 +M V30 17 C -8.2144 3.3305 0.0 0 +M V30 18 C -9.5207 2.6008 0.0 0 +M V30 19 N -10.827 3.354 0.0 0 +M V30 20 C -9.5325 1.118 0.0 0 +M V30 21 C -10.8388 4.8604 0.0 0 +M V30 22 C -12.1333 2.6243 0.0 0 +M V30 23 C -12.1451 5.6135 0.0 0 +M V30 24 C -13.4396 3.3775 0.0 0 +M V30 25 C -13.4514 4.8839 0.0 0 +M V30 26 C -12.1569 7.1199 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 1 10 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 1 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 19 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 1 23 26 +M V30 26 1 6 7 +M V30 27 1 11 13 +M V30 28 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +710 + +> +Z433450946 + +> +359.466 + +> +1.893 + +> +3 + +> +76.710 + +> +5 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 711 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 31 34 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.4163 -0.4485 0.0 0 +M V30 3 C -0.897 -1.2038 0.0 0 +M V30 4 N 2.7028 0.3068 0.0 0 +M V30 5 C 1.4045 -1.9356 0.0 0 +M V30 6 C -0.0236 -2.4077 0.0 0 +M V30 7 C -2.4077 -1.192 0.0 0 +M V30 8 C 3.9893 -0.4249 0.0 0 +M V30 9 C 2.691 -2.6792 0.0 0 +M V30 10 C -0.4957 -3.8241 0.0 0 +M V30 11 N 3.9775 -1.912 0.0 0 +M V30 12 C 5.2758 0.3304 0.0 0 +M V30 13 O 2.6792 -4.1663 0.0 0 +M V30 14 S 5.264 1.8412 0.0 0 +M V30 15 C 6.5505 2.5966 0.0 0 +M V30 16 C 6.5387 4.1073 0.0 0 +M V30 17 N 7.7426 5.0043 0.0 0 +M V30 18 C 5.3112 5.0043 0.0 0 +M V30 19 C 7.2705 6.4443 0.0 0 +M V30 20 S 5.7597 6.4443 0.0 0 +M V30 21 C 8.1439 7.6718 0.0 0 +M V30 22 C 9.6192 7.5301 0.0 0 +M V30 23 O 10.2212 6.1728 0.0 0 +M V30 24 N 10.4926 8.7576 0.0 0 +M V30 25 C 9.8671 10.1385 0.0 0 +M V30 26 C 10.7405 11.366 0.0 0 +M V30 27 C 8.3681 10.3038 0.0 0 +M V30 28 C 10.1149 12.747 0.0 0 +M V30 29 C 12.2158 11.2244 0.0 0 +M V30 30 C 7.7426 11.6847 0.0 0 +M V30 31 C 8.616 12.9122 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 8 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 2 16 18 +M V30 18 2 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 21 22 +M V30 22 2 22 23 +M V30 23 1 22 24 +M V30 24 1 24 25 +M V30 25 2 25 26 +M V30 26 1 25 27 +M V30 27 1 26 28 +M V30 28 1 26 29 +M V30 29 2 27 30 +M V30 30 2 28 31 +M V30 31 1 5 6 +M V30 32 1 9 11 +M V30 33 1 19 20 +M V30 34 1 30 31 +M V30 END BOND +M V30 END CTAB +M END +> +711 + +> +Z16858332 + +> +470.631 + +> +2.338 + +> +2 + +> +83.450 + +> +7 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 712 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2943 0.7718 0.0 0 +M V30 3 C 1.2825 2.2919 0.0 0 +M V30 4 C 2.5887 0.0237 0.0 0 +M V30 5 C 2.5769 3.0519 0.0 0 +M V30 6 C -0.0356 3.0519 0.0 0 +M V30 7 C 3.8831 0.7956 0.0 0 +M V30 8 C 3.8713 2.3037 0.0 0 +M V30 9 O 5.1775 0.0475 0.0 0 +M V30 10 N 5.1657 3.0637 0.0 0 +M V30 11 C 5.1657 -1.4487 0.0 0 +M V30 12 C 6.4601 2.3156 0.0 0 +M V30 13 O 6.4482 0.8193 0.0 0 +M V30 14 C 7.7544 3.0756 0.0 0 +M V30 15 S 9.0488 2.3275 0.0 0 +M V30 16 C 10.3432 3.0875 0.0 0 +M V30 17 C 11.6376 2.3394 0.0 0 +M V30 18 N 12.932 3.0994 0.0 0 +M V30 19 N 11.6258 0.8431 0.0 0 +M V30 20 C 14.2264 2.3512 0.0 0 +M V30 21 C 12.9202 0.095 0.0 0 +M V30 22 S 15.6515 2.8262 0.0 0 +M V30 23 C 14.2146 0.855 0.0 0 +M V30 24 O 12.9083 -1.4012 0.0 0 +M V30 25 C 16.5302 1.615 0.0 0 +M V30 26 C 15.6396 0.4037 0.0 0 +M V30 27 C 18.0265 1.6269 0.0 0 +M V30 28 C 16.0908 -1.0212 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 2 20 23 +M V30 23 2 21 24 +M V30 24 1 22 25 +M V30 25 1 23 26 +M V30 26 1 25 27 +M V30 27 1 26 28 +M V30 28 1 7 8 +M V30 29 1 21 23 +M V30 30 2 25 26 +M V30 END BOND +M V30 END CTAB +M END +> +712 + +> +Z16858566 + +> +437.963 + +> +3.254 + +> +2 + +> +79.790 + +> +6 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 713 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 30 33 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.4197 -0.4495 0.0 0 +M V30 3 C -0.8991 -1.2068 0.0 0 +M V30 4 N 2.7094 0.3076 0.0 0 +M V30 5 C 1.4079 -1.9403 0.0 0 +M V30 6 C -0.0236 -2.4136 0.0 0 +M V30 7 C -2.4136 -1.1949 0.0 0 +M V30 8 C 3.999 -0.4259 0.0 0 +M V30 9 C 2.6975 -2.6857 0.0 0 +M V30 10 C -0.4969 -3.8334 0.0 0 +M V30 11 N 3.9872 -1.9167 0.0 0 +M V30 12 C 5.2887 0.3312 0.0 0 +M V30 13 O 2.6857 -4.1765 0.0 0 +M V30 14 S 5.2768 1.8457 0.0 0 +M V30 15 C 6.5665 2.6029 0.0 0 +M V30 16 C 6.5546 4.1173 0.0 0 +M V30 17 N 7.8443 4.8746 0.0 0 +M V30 18 N 5.2413 4.8746 0.0 0 +M V30 19 C 7.8325 6.389 0.0 0 +M V30 20 C 5.2295 6.389 0.0 0 +M V30 21 N 6.5191 7.1581 0.0 0 +M V30 22 N 9.1221 7.1581 0.0 0 +M V30 23 N 3.9162 7.1581 0.0 0 +M V30 24 C 10.4117 6.4127 0.0 0 +M V30 25 C 10.3999 4.9219 0.0 0 +M V30 26 C 11.7014 7.1817 0.0 0 +M V30 27 C 11.6895 4.1883 0.0 0 +M V30 28 C 9.0866 4.1883 0.0 0 +M V30 29 C 12.991 6.4363 0.0 0 +M V30 30 C 12.9792 4.9455 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 8 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 2 19 21 +M V30 21 1 19 22 +M V30 22 1 20 23 +M V30 23 1 22 24 +M V30 24 2 24 25 +M V30 25 1 24 26 +M V30 26 1 25 27 +M V30 27 1 25 28 +M V30 28 2 26 29 +M V30 29 2 27 30 +M V30 30 1 5 6 +M V30 31 1 9 11 +M V30 32 1 20 21 +M V30 33 1 29 30 +M V30 END BOND +M V30 END CTAB +M END +> +713 + +> +Z16858168 + +> +439.557 + +> +3.697 + +> +3 + +> +118.180 + +> +6 + +> +parp15 + +> + + +> + + +$$$$ +Compound 714 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 29 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.4393 -0.4483 0.0 0 +M V30 3 C 0.873 -1.2034 0.0 0 +M V30 4 N -2.7489 0.3067 0.0 0 +M V30 5 C -1.4511 -1.9349 0.0 0 +M V30 6 C -0.0235 -2.4068 0.0 0 +M V30 7 C 2.3596 -1.1916 0.0 0 +M V30 8 C -4.0585 -0.4247 0.0 0 +M V30 9 C -2.7607 -2.6781 0.0 0 +M V30 10 C 0.4247 -3.8226 0.0 0 +M V30 11 N -4.0703 -1.9113 0.0 0 +M V30 12 C -5.3681 0.3303 0.0 0 +M V30 13 O -2.7725 -4.1647 0.0 0 +M V30 14 S -6.6777 -0.4011 0.0 0 +M V30 15 C -7.9873 0.3539 0.0 0 CFG=2 +M V30 16 C -9.2969 -0.3775 0.0 0 +M V30 17 C -7.9991 1.8641 0.0 0 +M V30 18 O -9.3087 -1.8641 0.0 0 +M V30 19 N -10.6065 0.3775 0.0 0 +M V30 20 C -11.9161 -0.3539 0.0 0 +M V30 21 C -13.2257 0.4011 0.0 0 +M V30 22 C -11.9279 -1.8405 0.0 0 +M V30 23 O -13.2375 1.9113 0.0 0 +M V30 24 C -14.5353 -0.3303 0.0 0 +M V30 25 C -13.2375 -2.5838 0.0 0 +M V30 26 C -11.9515 2.6781 0.0 0 +M V30 27 C -14.5471 -1.8169 0.0 0 +M V30 28 O -13.2493 -4.0703 0.0 0 +M V30 29 C -14.5589 -4.8018 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 8 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 12 14 +M V30 14 1 15 14 CFG=3 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 1 19 20 +M V30 20 2 20 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 21 24 +M V30 24 2 22 25 +M V30 25 1 23 26 +M V30 26 2 24 27 +M V30 27 1 25 28 +M V30 28 1 28 29 +M V30 29 1 5 6 +M V30 30 1 9 11 +M V30 31 1 25 27 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 15) +M V30 END COLLECTION +M V30 END CTAB +M END +> +714 + +> +Z16858199 + +> +433.544 + +> +2.178 + +> +2 + +> +89.020 + +> +7 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 715 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 29 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.3166 -0.7473 0.0 0 +M V30 3 C -1.3285 -2.2419 0.0 0 +M V30 4 C -2.6333 0.0237 0.0 0 +M V30 5 C -2.6452 -2.9773 0.0 0 +M V30 6 C -0.0355 -2.9773 0.0 0 +M V30 7 C -3.95 -0.7235 0.0 0 +M V30 8 C -3.9618 -2.2181 0.0 0 +M V30 9 N 1.2573 -2.2181 0.0 0 +M V30 10 C -0.0474 -4.4719 0.0 0 +M V30 11 Cl -5.2667 0.0474 0.0 0 +M V30 12 C 2.5503 -2.9536 0.0 0 +M V30 13 O 2.5384 -4.4482 0.0 0 +M V30 14 C 3.8432 -2.1944 0.0 0 +M V30 15 S 5.1362 -2.9299 0.0 0 +M V30 16 C 3.8314 -0.6761 0.0 0 +M V30 17 C 6.4291 -2.1707 0.0 0 +M V30 18 C 7.7221 -2.9061 0.0 0 +M V30 19 N 9.015 -2.147 0.0 0 +M V30 20 N 7.7102 -4.4007 0.0 0 +M V30 21 C 10.308 -2.8824 0.0 0 +M V30 22 C 9.0032 -5.148 0.0 0 +M V30 23 S 11.7314 -2.4079 0.0 0 +M V30 24 C 10.2961 -4.377 0.0 0 +M V30 25 O 8.9913 -6.6426 0.0 0 +M V30 26 C 12.6092 -3.6178 0.0 0 +M V30 27 C 11.7196 -4.8278 0.0 0 +M V30 28 C 14.1038 -3.606 0.0 0 +M V30 29 C 12.1703 -6.2512 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 9 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 17 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 2 21 24 +M V30 24 2 22 25 +M V30 25 1 23 26 +M V30 26 1 24 27 +M V30 27 1 26 28 +M V30 28 1 27 29 +M V30 29 1 7 8 +M V30 30 1 22 24 +M V30 31 2 26 27 +M V30 END BOND +M V30 END CTAB +M END +> +715 + +> +Z16858636 + +> +470.436 + +> +4.411 + +> +2 + +> +70.560 + +> +6 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 716 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 0.2955 1.4777 0.0 0 +M V30 3 C -1.5014 -0.1418 0.0 0 +M V30 4 N 1.5841 2.2344 0.0 0 +M V30 5 C -1.0167 2.2344 0.0 0 +M V30 6 C -2.128 1.2413 0.0 0 +M V30 7 C -2.258 -1.4304 0.0 0 +M V30 8 C 1.5723 3.7476 0.0 0 +M V30 9 C -1.0285 3.7476 0.0 0 +M V30 10 C -3.6057 1.5605 0.0 0 +M V30 11 N 0.26 4.5042 0.0 0 +M V30 12 C 2.8609 4.5042 0.0 0 +M V30 13 O -2.3408 4.5042 0.0 0 +M V30 14 S 4.1496 3.7713 0.0 0 +M V30 15 C 5.4382 4.5279 0.0 0 CFG=2 +M V30 16 C 6.7268 3.7949 0.0 0 +M V30 17 C 5.4264 6.0411 0.0 0 +M V30 18 O 6.715 2.3053 0.0 0 +M V30 19 N 8.0155 4.5515 0.0 0 +M V30 20 C 9.3041 3.8185 0.0 0 +M V30 21 C 10.7937 3.8304 0.0 0 +M V30 22 C 10.0371 2.5299 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 8 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 12 14 +M V30 14 1 15 14 CFG=1 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 1 19 20 +M V30 20 1 20 21 +M V30 21 1 20 22 +M V30 22 1 5 6 +M V30 23 1 9 11 +M V30 24 1 21 22 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 15) +M V30 END COLLECTION +M V30 END CTAB +M END +> +716 + +> +Z16858219 + +> +337.460 + +> +1.379 + +> +2 + +> +70.560 + +> +5 + +> +parp2 + +> + + +> + + +$$$$ +Compound 717 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 0.2956 1.4782 0.0 0 +M V30 3 C -1.5018 -0.1419 0.0 0 +M V30 4 N 1.5846 2.2468 0.0 0 +M V30 5 C -1.017 2.2468 0.0 0 +M V30 6 C -2.1286 1.2417 0.0 0 +M V30 7 C -2.2587 -1.4309 0.0 0 +M V30 8 C 1.5728 3.7605 0.0 0 +M V30 9 C -1.0288 3.7605 0.0 0 +M V30 10 C -3.6068 1.561 0.0 0 +M V30 11 N 0.2601 4.5174 0.0 0 +M V30 12 C 2.8618 4.5174 0.0 0 +M V30 13 O -2.3415 4.5174 0.0 0 +M V30 14 S 4.1508 3.7724 0.0 0 +M V30 15 C 5.4398 4.5292 0.0 0 CFG=2 +M V30 16 C 6.7288 3.7842 0.0 0 +M V30 17 C 5.428 6.0429 0.0 0 +M V30 18 O 6.717 2.2941 0.0 0 +M V30 19 N 8.0178 4.541 0.0 0 +M V30 20 C 9.3068 3.796 0.0 0 +M V30 21 C 9.295 2.306 0.0 0 +M V30 22 C 10.5958 4.5529 0.0 0 +M V30 23 F 7.9823 1.561 0.0 0 +M V30 24 C 10.584 1.561 0.0 0 +M V30 25 C 11.8848 3.8078 0.0 0 +M V30 26 F 10.5722 0.0709 0.0 0 +M V30 27 C 11.873 2.3296 0.0 0 +M V30 28 F 13.162 1.5846 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 8 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 12 14 +M V30 14 1 15 14 CFG=1 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 1 19 20 +M V30 20 2 20 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 21 24 +M V30 24 2 22 25 +M V30 25 1 24 26 +M V30 26 2 24 27 +M V30 27 1 27 28 +M V30 28 1 5 6 +M V30 29 1 9 11 +M V30 30 1 25 27 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 15) +M V30 END COLLECTION +M V30 END CTAB +M END +> +717 + +> +Z16858226 + +> +427.464 + +> +2.736 + +> +2 + +> +70.560 + +> +5 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 718 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.4401 -0.4485 0.0 0 +M V30 3 C 0.8735 -1.204 0.0 0 +M V30 4 N -2.7503 0.3069 0.0 0 +M V30 5 C -1.4519 -1.9358 0.0 0 +M V30 6 C -0.0236 -2.408 0.0 0 +M V30 7 C 2.3608 -1.1922 0.0 0 +M V30 8 C -4.0606 -0.4249 0.0 0 +M V30 9 C -2.7621 -2.6795 0.0 0 +M V30 10 C 0.4249 -3.8245 0.0 0 +M V30 11 N -4.0724 -1.9122 0.0 0 +M V30 12 C -5.3709 0.3305 0.0 0 +M V30 13 O -2.7739 -4.1668 0.0 0 +M V30 14 S -6.6811 -0.4013 0.0 0 +M V30 15 C -7.9914 0.3541 0.0 0 CFG=2 +M V30 16 C -9.3017 -0.3777 0.0 0 +M V30 17 C -8.0032 1.865 0.0 0 +M V30 18 O -9.3135 -1.865 0.0 0 +M V30 19 N -10.6119 0.3777 0.0 0 +M V30 20 C -11.9222 -0.3541 0.0 0 +M V30 21 C -11.934 -1.8414 0.0 0 +M V30 22 C -13.2325 0.4013 0.0 0 +M V30 23 S -10.6474 -2.5851 0.0 0 +M V30 24 C -13.2443 -2.5851 0.0 0 +M V30 25 C -14.5427 -0.3305 0.0 0 +M V30 26 C -10.6592 -4.0724 0.0 0 +M V30 27 C -14.5546 -1.8178 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 8 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 12 14 +M V30 14 1 15 14 CFG=3 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 1 19 20 +M V30 20 2 20 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 21 24 +M V30 24 2 22 25 +M V30 25 1 23 26 +M V30 26 2 24 27 +M V30 27 1 5 6 +M V30 28 1 9 11 +M V30 29 1 25 27 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 15) +M V30 END COLLECTION +M V30 END CTAB +M END +> +718 + +> +Z16858237 + +> +419.584 + +> +3.026 + +> +2 + +> +70.560 + +> +6 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 719 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4971 0.0 0 +M V30 3 N -1.3307 -2.2338 0.0 0 +M V30 4 C 1.2832 -2.2338 0.0 0 +M V30 5 N -1.3426 -3.7309 0.0 0 +M V30 6 C 1.2713 -3.7309 0.0 0 +M V30 7 C 2.5784 -1.4733 0.0 0 +M V30 8 C -0.0475 -4.4795 0.0 0 +M V30 9 C 2.5665 -4.4795 0.0 0 +M V30 10 C 3.8735 -2.21 0.0 0 +M V30 11 C -0.0594 -5.9766 0.0 0 +M V30 12 C 3.8616 -3.719 0.0 0 +M V30 13 O -1.3783 -6.7252 0.0 0 +M V30 14 N 1.2357 -6.7252 0.0 0 +M V30 15 C 1.2238 -8.2224 0.0 0 +M V30 16 C 2.5308 -5.9529 0.0 0 +M V30 17 C 2.519 -8.9591 0.0 0 +M V30 18 C 3.826 -6.7015 0.0 0 +M V30 19 N 3.8141 -8.1986 0.0 0 +M V30 20 C 5.1093 -8.9353 0.0 0 +M V30 21 O 6.4044 -8.1748 0.0 0 +M V30 22 C 5.0974 -10.4324 0.0 0 +M V30 23 C 6.3925 -11.181 0.0 0 +M V30 24 C 3.7785 -11.181 0.0 0 +M V30 25 C 6.3806 -12.6781 0.0 0 +M V30 26 C 3.7666 -12.6781 0.0 0 +M V30 27 C 5.0617 -13.4148 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 2 20 21 +M V30 21 1 20 22 +M V30 22 2 22 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 2 24 26 +M V30 26 2 25 27 +M V30 27 1 6 8 +M V30 28 1 10 12 +M V30 29 1 18 19 +M V30 30 1 26 27 +M V30 END BOND +M V30 END CTAB +M END +> +719 + +> +Z26979657 + +> +362.382 + +> +0.070 + +> +1 + +> +82.080 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3105422 + +> +0.87 + +$$$$ +Compound 720 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 32 35 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3125 -0.7331 0.0 0 +M V30 3 C 1.2889 -0.7331 0.0 0 +M V30 4 N -1.4781 -2.2112 0.0 0 +M V30 5 N -2.696 -0.1064 0.0 0 +M V30 6 C 2.5778 0.0236 0.0 0 +M V30 7 N -2.9562 -2.5068 0.0 0 +M V30 8 C -3.169 1.3362 0.0 0 +M V30 9 C -3.713 -1.2061 0.0 0 +M V30 10 O 2.5659 1.5372 0.0 0 +M V30 11 N 3.8667 -0.7094 0.0 0 +M V30 12 C -4.6471 1.6554 0.0 0 +M V30 13 C -2.1757 2.4595 0.0 0 +M V30 14 N -5.1911 -0.8868 0.0 0 +M V30 15 C 5.1556 0.0472 0.0 0 +M V30 16 C -5.6641 0.5557 0.0 0 +M V30 17 C -5.1201 3.0981 0.0 0 +M V30 18 C -2.6487 3.9022 0.0 0 +M V30 19 C -6.208 -1.9865 0.0 0 +M V30 20 C 6.4445 -0.6858 0.0 0 +M V30 21 O -7.1422 0.875 0.0 0 +M V30 22 C -4.1268 4.2214 0.0 0 +M V30 23 C -7.6861 -1.6673 0.0 0 +M V30 24 C 7.7334 0.0709 0.0 0 +M V30 25 C 6.4327 -2.1757 0.0 0 +M V30 26 C -8.7031 -2.767 0.0 0 +M V30 27 C 9.0223 -0.6621 0.0 0 +M V30 28 C 7.7216 -2.9089 0.0 0 +M V30 29 C 9.0105 -2.1521 0.0 0 +M V30 30 C 10.2994 -2.8852 0.0 0 +M V30 31 O 11.5883 -2.1284 0.0 0 +M V30 32 O 10.2876 -4.3752 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 1 9 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 1 12 17 +M V30 17 2 13 18 +M V30 18 1 14 19 +M V30 19 1 15 20 +M V30 20 2 16 21 +M V30 21 2 17 22 +M V30 22 1 19 23 +M V30 23 2 20 24 +M V30 24 1 20 25 +M V30 25 1 23 26 +M V30 26 1 24 27 +M V30 27 2 25 28 +M V30 28 2 27 29 +M V30 29 1 29 30 +M V30 30 2 30 31 +M V30 31 1 30 32 +M V30 32 2 7 9 +M V30 33 1 14 16 +M V30 34 1 18 22 +M V30 35 1 28 29 +M V30 END BOND +M V30 END CTAB +M END +> +720 + +> +Z16873486 + +> +451.498 + +> +2.257 + +> +2 + +> +117.420 + +> +8 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 721 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 29 32 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.4226 -0.4505 0.0 0 +M V30 3 C -0.901 -1.2092 0.0 0 +M V30 4 C 2.7149 0.3082 0.0 0 +M V30 5 C 1.4108 -1.9443 0.0 0 +M V30 6 C -0.0237 -2.4185 0.0 0 +M V30 7 O 2.7031 1.8257 0.0 0 +M V30 8 N 4.0072 -0.4268 0.0 0 +M V30 9 N 2.7031 -2.6794 0.0 0 +M V30 10 C 3.9954 -1.9206 0.0 0 +M V30 11 C 5.2876 -2.6557 0.0 0 +M V30 12 S 6.5799 -1.8969 0.0 0 +M V30 13 C 7.8722 -2.6319 0.0 0 +M V30 14 N 8.0145 -4.1139 0.0 0 +M V30 15 N 9.2356 -2.0036 0.0 0 +M V30 16 N 9.4727 -4.4103 0.0 0 +M V30 17 C 9.532 -0.5216 0.0 0 +M V30 18 C 10.2315 -3.1062 0.0 0 +M V30 19 C 8.4057 0.4979 0.0 0 +M V30 20 C 10.9547 -0.0474 0.0 0 +M V30 21 C 11.7135 -2.9402 0.0 0 CFG=1 +M V30 22 C 8.7021 1.9799 0.0 0 +M V30 23 C 11.2511 1.4345 0.0 0 +M V30 24 N 12.5908 -4.1495 0.0 0 +M V30 25 C 12.3181 -1.5531 0.0 0 +M V30 26 C 10.1248 2.4541 0.0 0 +M V30 27 C 14.0728 -3.9835 0.0 0 +M V30 28 C 11.9625 -5.5129 0.0 0 +M V30 29 F 10.4212 3.9361 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 1 8 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 15 18 +M V30 18 2 17 19 +M V30 19 1 17 20 +M V30 20 1 18 21 +M V30 21 1 19 22 +M V30 22 2 20 23 +M V30 23 1 21 24 CFG=3 +M V30 24 1 21 25 +M V30 25 2 22 26 +M V30 26 1 24 27 +M V30 27 1 24 28 +M V30 28 1 26 29 +M V30 29 1 5 6 +M V30 30 2 9 10 +M V30 31 2 16 18 +M V30 32 1 23 26 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 21) +M V30 END COLLECTION +M V30 END CTAB +M END +> +721 + +> +Z16890277 + +> +430.522 + +> +1.674 + +> +1 + +> +75.410 + +> +6 + +> +parp2, parp1, parp10 + +> + + +> + + +$$$$ +Compound 722 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.9895 1.1191 0.0 0 +M V30 3 N 2.4621 0.9778 0.0 0 +M V30 4 C 0.6715 2.5917 0.0 0 +M V30 5 C 3.0629 2.3561 0.0 0 +M V30 6 C 3.1925 -0.3062 0.0 0 +M V30 7 C 1.9556 3.3575 0.0 0 +M V30 8 C -0.6361 3.3575 0.0 0 +M V30 9 O 4.512 2.6742 0.0 0 +M V30 10 C 1.9438 4.8654 0.0 0 +M V30 11 C -0.6479 4.8654 0.0 0 +M V30 12 C 0.6361 5.6194 0.0 0 +M V30 13 C -1.9556 5.6194 0.0 0 +M V30 14 O -1.9673 7.1273 0.0 0 +M V30 15 N -3.2632 4.889 0.0 0 +M V30 16 C -4.5709 5.6429 0.0 0 CFG=2 +M V30 17 C -5.8785 4.9125 0.0 0 +M V30 18 C -4.5827 7.1509 0.0 0 +M V30 19 C -7.1862 5.6665 0.0 0 +M V30 20 C -8.4939 4.9361 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 2 10 12 +M V30 12 1 11 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 16 15 CFG=3 +M V30 16 1 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 1 5 7 +M V30 21 1 11 12 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 16) +M V30 END COLLECTION +M V30 END CTAB +M END +> +722 + +> +Z26707888 + +> +274.315 + +> +2.583 + +> +1 + +> +66.480 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL597890 + +> +0.86 + +$$$$ +Compound 723 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.0945 1.0122 0.0 0 +M V30 3 N 0.9298 2.5069 0.0 0 +M V30 4 N 2.5422 0.7179 0.0 0 +M V30 5 C -0.3766 3.2602 0.0 0 +M V30 6 C 2.2833 3.1307 0.0 0 +M V30 7 C 3.2837 2.0244 0.0 0 +M V30 8 C -1.683 2.5187 0.0 0 +M V30 9 C -0.3884 4.7667 0.0 0 +M V30 10 C -2.9895 3.272 0.0 0 +M V30 11 C -1.6948 5.52 0.0 0 +M V30 12 N -4.2959 2.5305 0.0 0 +M V30 13 C -3.0013 4.7785 0.0 0 +M V30 14 C -5.6024 3.2837 0.0 0 +M V30 15 O -5.6142 4.7903 0.0 0 +M V30 16 N -6.9088 2.5422 0.0 0 +M V30 17 C -8.2153 3.2955 0.0 0 +M V30 18 C -9.5217 2.554 0.0 0 +M V30 19 C -10.8282 3.3073 0.0 0 +M V30 20 N -12.1346 2.5658 0.0 0 +M V30 21 C -13.4411 3.319 0.0 0 +M V30 22 C -12.1464 1.0828 0.0 0 +M V30 23 C -14.7475 2.5775 0.0 0 +M V30 24 C -13.4529 0.353 0.0 0 +M V30 25 C -14.7593 1.0945 0.0 0 +M V30 26 C -16.0658 0.3648 0.0 0 +M V30 27 O -17.3722 1.1181 0.0 0 +M V30 28 N -16.0775 -1.1181 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 1 10 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 1 18 19 +M V30 19 1 19 20 +M V30 20 1 20 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 1 25 26 +M V30 26 2 26 27 +M V30 27 1 26 28 +M V30 28 1 6 7 +M V30 29 1 11 13 +M V30 30 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +723 + +> +Z433453616 + +> +388.464 + +> +-1.158 + +> +4 + +> +119.800 + +> +7 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 724 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4489 1.4412 0.0 0 +M V30 3 N -0.4489 2.6698 0.0 0 +M V30 4 C 1.8665 1.9137 0.0 0 +M V30 5 C 0.4252 3.8984 0.0 0 +M V30 6 C -1.961 2.6816 0.0 0 +M V30 7 C 1.8547 3.4259 0.0 0 +M V30 8 C 3.1542 1.1813 0.0 0 +M V30 9 O -0.0472 5.3397 0.0 0 +M V30 10 C -2.7289 3.9929 0.0 0 +M V30 11 C 3.1424 4.1938 0.0 0 +M V30 12 C 4.4419 1.9374 0.0 0 +M V30 13 C -4.241 4.0048 0.0 0 +M V30 14 C 4.43 3.4495 0.0 0 +M V30 15 C 5.7295 1.2049 0.0 0 +M V30 16 C -4.9971 5.3161 0.0 0 +M V30 17 O 5.7177 -0.2835 0.0 0 +M V30 18 N 7.0172 1.961 0.0 0 +M V30 19 C 8.3049 1.2286 0.0 0 CFG=2 +M V30 20 C 9.5926 1.9846 0.0 0 +M V30 21 C 8.2931 -0.2598 0.0 0 +M V30 22 C 10.8803 1.2522 0.0 0 +M V30 23 C 12.1679 2.0083 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 10 13 +M V30 13 2 11 14 +M V30 14 1 12 15 +M V30 15 1 13 16 +M V30 16 2 15 17 +M V30 17 1 15 18 +M V30 18 1 19 18 CFG=3 +M V30 19 1 19 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 22 23 +M V30 23 1 5 7 +M V30 24 1 12 14 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 19) +M V30 END COLLECTION +M V30 END CTAB +M END +> +724 + +> +Z26706985 + +> +316.395 + +> +4.170 + +> +1 + +> +66.480 + +> +7 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL597888 + +> +0.89 + +$$$$ +Compound 725 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.0926 1.0103 0.0 0 +M V30 3 N 0.9281 2.5024 0.0 0 +M V30 4 N 2.5376 0.7166 0.0 0 +M V30 5 C -0.3759 3.266 0.0 0 +M V30 6 C 2.2791 3.125 0.0 0 +M V30 7 C 3.2778 2.0207 0.0 0 +M V30 8 C -1.68 2.5259 0.0 0 +M V30 9 C -0.3876 4.7698 0.0 0 +M V30 10 C -2.984 3.2895 0.0 0 +M V30 11 C -1.6917 5.5217 0.0 0 +M V30 12 N -4.2881 2.5494 0.0 0 +M V30 13 C -2.9958 4.7933 0.0 0 +M V30 14 C -5.5922 3.313 0.0 0 +M V30 15 O -5.6039 4.8168 0.0 0 +M V30 16 N -6.8963 2.5729 0.0 0 +M V30 17 C -8.2003 3.3365 0.0 0 CFG=2 +M V30 18 C -9.5044 2.5963 0.0 0 +M V30 19 C -8.2121 4.8403 0.0 0 +M V30 20 N -10.8085 3.36 0.0 0 +M V30 21 C -10.973 4.852 0.0 0 +M V30 22 C -12.183 2.7608 0.0 0 +M V30 23 O -9.8804 5.8624 0.0 0 +M V30 24 C -12.4415 5.1692 0.0 0 +M V30 25 C -13.1934 3.8769 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 1 10 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 17 16 CFG=3 +M V30 17 1 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 20 21 +M V30 21 1 20 22 +M V30 22 2 21 23 +M V30 23 1 21 24 +M V30 24 1 22 25 +M V30 25 1 6 7 +M V30 26 1 11 13 +M V30 27 1 24 25 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 17) +M V30 END COLLECTION +M V30 END CTAB +M END +> +725 + +> +Z603495396 + +> +345.396 + +> +-0.121 + +> +3 + +> +93.780 + +> +5 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 726 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.0132 1.1192 0.0 0 +M V30 3 C 1.3549 0.6244 0.0 0 +M V30 4 C -2.5095 0.9779 0.0 0 +M V30 5 C -0.2827 2.4271 0.0 0 +M V30 6 C 1.1899 2.1207 0.0 0 +M V30 7 N -3.5228 2.0972 0.0 0 +M V30 8 C -3.2636 -0.3063 0.0 0 +M V30 9 C -4.9013 1.4963 0.0 0 +M V30 10 N -4.7363 0.0117 0.0 0 +M V30 11 C -2.6627 -1.6612 0.0 0 +M V30 12 S -6.2091 2.2621 0.0 0 +M V30 13 S -3.4285 -2.9455 0.0 0 +M V30 14 C -1.2135 -1.9558 0.0 0 +M V30 15 C -7.5169 1.5198 0.0 0 +M V30 16 C -2.4388 -4.0412 0.0 0 +M V30 17 C -1.0721 -3.4285 0.0 0 +M V30 18 C -8.8247 2.2857 0.0 0 +M V30 19 O -8.8365 3.7938 0.0 0 +M V30 20 N -10.1325 1.5434 0.0 0 +M V30 21 C -11.4404 2.3092 0.0 0 +M V30 22 O -11.4521 3.8173 0.0 0 +M V30 23 N -12.7482 1.567 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 4 7 +M V30 7 2 4 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 1 8 11 +M V30 11 1 9 12 +M V30 12 1 11 13 +M V30 13 2 11 14 +M V30 14 1 12 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 20 21 +M V30 21 2 21 22 +M V30 22 1 21 23 +M V30 23 1 5 6 +M V30 24 1 9 10 +M V30 25 2 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +726 + +> +Z16940924 + +> +364.466 + +> +2.852 + +> +3 + +> +100.870 + +> +5 + +> +DNA_pk + +> + + +> + + +$$$$ +Compound 727 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3076 0.7657 0.0 0 +M V30 3 N -2.6152 0.0235 0.0 0 +M V30 4 C -1.3194 2.2736 0.0 0 +M V30 5 C -2.627 -1.4607 0.0 0 CFG=2 +M V30 6 C -0.0353 3.0276 0.0 0 +M V30 7 C -2.627 3.0276 0.0 0 +M V30 8 C -3.9347 -2.1911 0.0 0 +M V30 9 C -1.3429 -2.1911 0.0 0 +M V30 10 C -0.0471 4.5355 0.0 0 +M V30 11 C -2.6388 4.5355 0.0 0 +M V30 12 C -3.9465 -3.6755 0.0 0 +M V30 13 C -5.2423 -1.4372 0.0 0 +M V30 14 C -1.3547 5.2894 0.0 0 +M V30 15 C -5.2541 -4.4059 0.0 0 +M V30 16 C -6.55 -2.1676 0.0 0 +M V30 17 O -1.3665 6.7974 0.0 0 +M V30 18 N -6.5617 -3.6519 0.0 0 +M V30 19 C -2.6741 7.5513 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 5 3 CFG=1 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 2 10 14 +M V30 14 1 12 15 +M V30 15 2 13 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 1 17 19 +M V30 19 1 11 14 +M V30 20 1 16 18 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 5) +M V30 END COLLECTION +M V30 END CTAB +M END +> +727 + +> +Z27995192 + +> +256.300 + +> +1.842 + +> +1 + +> +51.220 + +> +4 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1975121 + +> +0.86 + +$$$$ +Compound 728 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 0.8733 1.2274 0.0 0 +M V30 3 C 0.6019 -1.3572 0.0 0 +M V30 4 N 2.3604 1.2392 0.0 0 +M V30 5 N 0.4012 2.6673 0.0 0 +M V30 6 C -0.295 -2.5611 0.0 0 +M V30 7 N 2.8089 2.6791 0.0 0 +M V30 8 C 1.6051 3.5643 0.0 0 +M V30 9 C -1.0386 3.1394 0.0 0 +M V30 10 O -1.7939 -2.3958 0.0 0 +M V30 11 N 0.3068 -3.9183 0.0 0 +M V30 12 C 1.5933 5.075 0.0 0 +M V30 13 C -0.5901 -5.1222 0.0 0 +M V30 14 C 1.7821 -4.06 0.0 0 +M V30 15 C 0.2832 5.8303 0.0 0 +M V30 16 C 2.8797 5.8303 0.0 0 +M V30 17 C 0.0118 -6.4794 0.0 0 CFG=1 +M V30 18 C 2.384 -5.4172 0.0 0 +M V30 19 C 0.2714 7.341 0.0 0 +M V30 20 C 2.8679 7.341 0.0 0 +M V30 21 C -0.8851 -7.6833 0.0 0 +M V30 22 C 1.487 -6.6211 0.0 0 +M V30 23 O -1.0386 8.0964 0.0 0 +M V30 24 C 1.5579 8.0964 0.0 0 +M V30 25 O -0.2832 -9.0405 0.0 0 +M V30 26 N -2.384 -7.518 0.0 0 +M V30 27 C -1.0504 9.607 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 1 8 12 +M V30 12 1 11 13 +M V30 13 1 11 14 +M V30 14 2 12 15 +M V30 15 1 12 16 +M V30 16 1 13 17 +M V30 17 1 14 18 +M V30 18 1 15 19 +M V30 19 2 16 20 +M V30 20 1 17 21 CFG=3 +M V30 21 1 17 22 +M V30 22 1 19 23 +M V30 23 2 19 24 +M V30 24 2 21 25 +M V30 25 1 21 26 +M V30 26 1 23 27 +M V30 27 2 7 8 +M V30 28 1 18 22 +M V30 29 1 20 24 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 17) +M V30 END COLLECTION +M V30 END CTAB +M END +> +728 + +> +Z97462498 + +> +389.472 + +> +0.652 + +> +1 + +> +103.340 + +> +6 + +> +parp15 + +> + + +> + + +$$$$ +Compound 729 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3112 -0.7324 0.0 0 +M V30 3 N -2.6225 0.0236 0.0 0 +M V30 4 C -1.323 -2.2208 0.0 0 +M V30 5 C -2.6343 1.5357 0.0 0 CFG=2 +M V30 6 C -0.0354 -2.9532 0.0 0 +M V30 7 C -2.6343 -2.9532 0.0 0 +M V30 8 C -1.3466 2.2917 0.0 0 +M V30 9 C -3.9455 2.2917 0.0 0 +M V30 10 C -0.0472 -4.4417 0.0 0 +M V30 11 C 1.2521 -2.1972 0.0 0 +M V30 12 C -2.6461 -4.4417 0.0 0 +M V30 13 N -1.3585 3.8038 0.0 0 +M V30 14 C -3.9573 3.8038 0.0 0 +M V30 15 N -1.3585 -5.1859 0.0 0 +M V30 16 C 1.2403 -5.1859 0.0 0 +M V30 17 C 2.5398 -2.9296 0.0 0 +M V30 18 O -3.9573 -5.1859 0.0 0 +M V30 19 C -2.6697 4.5716 0.0 0 +M V30 20 C -0.2598 4.8197 0.0 0 +M V30 21 C 2.5279 -4.4299 0.0 0 +M V30 22 N -2.3744 6.0482 0.0 0 +M V30 23 C -0.8859 6.2018 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 5 3 CFG=1 +M V30 5 1 4 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 6 11 +M V30 11 1 7 12 +M V30 12 1 8 13 +M V30 13 1 9 14 +M V30 14 1 10 15 +M V30 15 2 10 16 +M V30 16 1 11 17 +M V30 17 1 12 18 +M V30 18 1 13 19 +M V30 19 1 13 20 +M V30 20 1 16 21 +M V30 21 2 19 22 +M V30 22 2 20 23 +M V30 23 2 12 15 +M V30 24 1 14 19 +M V30 25 2 17 21 +M V30 26 1 22 23 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 5) +M V30 END COLLECTION +M V30 END CTAB +M END +> +729 + +> +Z978951726 + +> +308.335 + +> +2.005 + +> +2 + +> +80.040 + +> +2 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 730 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 29 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.289 0.7568 0.0 0 +M V30 3 N 2.578 0.0236 0.0 0 +M V30 4 C 1.2772 2.2705 0.0 0 CFG=1 +M V30 5 C 3.8671 0.7805 0.0 0 +M V30 6 C 2.5662 -1.4664 0.0 0 +M V30 7 O 2.5662 3.0274 0.0 0 +M V30 8 C -0.0354 3.0274 0.0 0 +M V30 9 C 3.8552 2.2942 0.0 0 +M V30 10 C 5.1561 0.0473 0.0 0 +M V30 11 C 1.2535 -2.2114 0.0 0 +M V30 12 C 5.1443 3.0511 0.0 0 +M V30 13 C 6.4451 0.8041 0.0 0 +M V30 14 O -0.0591 -1.4545 0.0 0 +M V30 15 N 1.2417 -3.7015 0.0 0 +M V30 16 C 6.4333 2.3178 0.0 0 +M V30 17 C -0.0709 -4.4347 0.0 0 +M V30 18 C -0.0827 -5.9248 0.0 0 +M V30 19 C -1.3836 -3.6778 0.0 0 +M V30 20 C -1.3954 -6.6698 0.0 0 +M V30 21 C -2.6963 -4.411 0.0 0 +M V30 22 C -1.4072 -8.1599 0.0 0 +M V30 23 C -2.7081 -5.9011 0.0 0 +M V30 24 O -0.1182 -8.8931 0.0 0 +M V30 25 N -2.7199 -8.8931 0.0 0 +M V30 26 C -2.7318 -10.3832 0.0 0 +M V30 27 C -4.0326 -8.1362 0.0 0 +M V30 28 C -4.0444 -11.1164 0.0 0 +M V30 29 C -4.0444 -6.6225 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 4 8 CFG=3 +M V30 8 2 5 9 +M V30 9 1 5 10 +M V30 10 1 6 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 2 11 14 +M V30 14 1 11 15 +M V30 15 2 12 16 +M V30 16 1 15 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 1 20 22 +M V30 22 2 20 23 +M V30 23 2 22 24 +M V30 24 1 22 25 +M V30 25 1 25 26 +M V30 26 1 25 27 +M V30 27 1 26 28 +M V30 28 1 27 29 +M V30 29 1 7 9 +M V30 30 1 13 16 +M V30 31 1 21 23 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 4) +M V30 END COLLECTION +M V30 END CTAB +M END +> +730 + +> +Z217622688 + +> +395.452 + +> +2.715 + +> +1 + +> +78.950 + +> +6 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL2407971 + +> +0.88 + +$$$$ +Compound 731 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 34 37 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.0117 -1.4863 0.0 0 +M V30 3 C -1.3094 0.7667 0.0 0 +M V30 4 O -1.2386 -2.3593 0.0 0 +M V30 5 N 1.1914 -2.3593 0.0 0 +M V30 6 C -1.3212 2.2767 0.0 0 +M V30 7 C -0.7903 -3.7749 0.0 0 +M V30 8 N 0.7195 -3.7749 0.0 0 +M V30 9 O -0.0353 3.0317 0.0 0 +M V30 10 N -2.6306 3.0317 0.0 0 +M V30 11 C -1.6869 -4.9781 0.0 0 +M V30 12 C -2.6424 4.5417 0.0 0 +M V30 13 C -3.94 2.3003 0.0 0 +M V30 14 N -1.0852 -6.3347 0.0 0 +M V30 15 C -3.9518 5.3084 0.0 0 +M V30 16 C -1.3566 5.3084 0.0 0 +M V30 17 C -1.8402 -7.6206 0.0 0 +M V30 18 C 0.3656 -6.6297 0.0 0 +M V30 19 N -3.9636 6.8184 0.0 0 +M V30 20 N -5.2613 4.5652 0.0 0 +M V30 21 O -0.0707 4.5652 0.0 0 +M V30 22 N -1.3684 6.8184 0.0 0 +M V30 23 O -3.3384 -7.7621 0.0 0 +M V30 24 C -0.8493 -8.7177 0.0 0 +M V30 25 C 0.5072 -8.1042 0.0 0 +M V30 26 C -2.6778 7.5734 0.0 0 +M V30 27 C -5.273 7.5734 0.0 0 +M V30 28 O -2.6896 9.0834 0.0 0 +M V30 29 C -5.2848 9.0834 0.0 0 +M V30 30 C -6.5943 9.8383 0.0 0 +M V30 31 C -3.999 9.8383 0.0 0 +M V30 32 C -6.6061 11.3483 0.0 0 +M V30 33 C -4.0108 11.3483 0.0 0 +M V30 34 C -5.3202 12.1033 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 10 12 +M V30 12 1 10 13 +M V30 13 1 11 14 +M V30 14 2 12 15 +M V30 15 1 12 16 +M V30 16 1 14 17 +M V30 17 1 14 18 +M V30 18 1 15 19 +M V30 19 1 15 20 +M V30 20 2 16 21 +M V30 21 1 16 22 +M V30 22 2 17 23 +M V30 23 1 17 24 +M V30 24 1 18 25 +M V30 25 1 19 26 +M V30 26 1 19 27 +M V30 27 2 26 28 +M V30 28 1 27 29 +M V30 29 2 29 30 +M V30 30 1 29 31 +M V30 31 1 30 32 +M V30 32 2 31 33 +M V30 33 2 32 34 +M V30 34 2 7 8 +M V30 35 1 22 26 +M V30 36 1 24 25 +M V30 37 1 33 34 +M V30 END BOND +M V30 END CTAB +M END +> +731 + +> +Z16988315 + +> +485.516 + +> +-2.762 + +> +2 + +> +154.970 + +> +8 + +> +ATM + +> + + +> + + +$$$$ +Compound 732 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.506 0.0 0 +M V30 3 N -1.3177 2.259 0.0 0 +M V30 4 C 1.2707 2.259 0.0 0 +M V30 5 C -1.3295 3.765 0.0 0 +M V30 6 C 1.2589 3.765 0.0 0 +M V30 7 C 2.5531 1.5177 0.0 0 +M V30 8 N -0.047 4.518 0.0 0 +M V30 9 C -2.6355 4.518 0.0 0 +M V30 10 C 2.5414 4.518 0.0 0 +M V30 11 C 3.8356 2.2708 0.0 0 +M V30 12 O -3.9415 3.7885 0.0 0 +M V30 13 N -2.6473 6.024 0.0 0 +M V30 14 C 3.8238 3.7885 0.0 0 +M V30 15 C -1.3648 6.7771 0.0 0 +M V30 16 C -1.3765 8.2831 0.0 0 +M V30 17 C -2.6826 9.0361 0.0 0 +M V30 18 C -0.0941 9.0361 0.0 0 +M V30 19 C -2.6943 10.5421 0.0 0 +M V30 20 C -0.1058 10.5421 0.0 0 +M V30 21 C -1.4118 11.3069 0.0 0 +M V30 22 C -4.0003 11.3069 0.0 0 +M V30 23 O -4.0121 12.8129 0.0 0 +M V30 24 C -5.3181 13.5659 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 2 19 21 +M V30 21 1 19 22 +M V30 22 1 22 23 +M V30 23 1 23 24 +M V30 24 1 6 8 +M V30 25 1 11 14 +M V30 26 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +732 + +> +Z1149166617 + +> +323.346 + +> +2.002 + +> +2 + +> +79.790 + +> +5 + +> +parp15, Tankyrase1 + +> + + +> + + +$$$$ +Compound 733 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 0.9987 1.1295 0.0 0 +M V30 3 C 0.7371 -1.2959 0.0 0 +M V30 4 C 2.366 0.5231 0.0 0 +M V30 5 C 2.1995 -0.9749 0.0 0 +M V30 6 C 3.6619 1.284 0.0 0 +M V30 7 N 4.9579 0.5469 0.0 0 +M V30 8 C 6.2539 1.3078 0.0 0 +M V30 9 O 6.242 2.8297 0.0 0 +M V30 10 C 7.5498 0.5706 0.0 0 +M V30 11 N 8.8458 1.3316 0.0 0 +M V30 12 N 7.5379 -0.9273 0.0 0 +M V30 13 C 10.1417 0.5944 0.0 0 +M V30 14 C 8.8339 -1.6764 0.0 0 +M V30 15 C 10.1298 -0.9036 0.0 0 +M V30 16 C 11.4377 1.3554 0.0 0 +M V30 17 O 8.822 -3.1745 0.0 0 +M V30 18 C 11.4258 -1.6526 0.0 0 +M V30 19 C 12.7337 0.6182 0.0 0 +M V30 20 C 12.7218 -0.8798 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 2 3 5 +M V30 5 1 4 6 +M V30 6 1 6 7 +M V30 7 1 7 8 +M V30 8 2 8 9 +M V30 9 1 8 10 +M V30 10 2 10 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 2 13 15 +M V30 15 1 13 16 +M V30 16 2 14 17 +M V30 17 1 15 18 +M V30 18 2 16 19 +M V30 19 2 18 20 +M V30 20 1 4 5 +M V30 21 1 14 15 +M V30 22 1 19 20 +M V30 END BOND +M V30 END CTAB +M END +> +733 + +> +Z1149164350 + +> +285.321 + +> +1.850 + +> +2 + +> +70.560 + +> +3 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 734 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5045 0.0 0 +M V30 3 N -1.3164 2.2567 0.0 0 +M V30 4 C 1.2694 2.2567 0.0 0 +M V30 5 C -1.3281 3.7612 0.0 0 +M V30 6 C 1.2576 3.7612 0.0 0 +M V30 7 C 2.5505 1.528 0.0 0 +M V30 8 N -0.047 4.5252 0.0 0 +M V30 9 C -2.6328 4.5252 0.0 0 +M V30 10 C 2.5388 4.5252 0.0 0 +M V30 11 C 3.8317 2.2802 0.0 0 +M V30 12 O -2.6446 6.0297 0.0 0 +M V30 13 N -3.9375 3.7847 0.0 0 +M V30 14 C 3.82 3.7847 0.0 0 +M V30 15 C -5.2422 4.5487 0.0 0 +M V30 16 C -6.5469 3.8082 0.0 0 +M V30 17 N -7.8516 4.5722 0.0 0 +M V30 18 C -9.1562 3.8317 0.0 0 +M V30 19 O -9.168 2.3507 0.0 0 +M V30 20 C -10.4609 4.5957 0.0 0 +M V30 21 C -10.4727 6.1002 0.0 0 +M V30 22 C -11.7656 3.8552 0.0 0 +M V30 23 N -11.7774 6.8525 0.0 0 +M V30 24 C -13.0703 4.6192 0.0 0 +M V30 25 C -13.082 6.1237 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 2 20 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 2 22 24 +M V30 24 2 23 25 +M V30 25 1 6 8 +M V30 26 1 11 14 +M V30 27 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +734 + +> +Z1149164411 + +> +337.333 + +> +0.592 + +> +3 + +> +112.550 + +> +5 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 735 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2937 -0.7477 0.0 0 +M V30 3 N 2.5874 0.0237 0.0 0 +M V30 4 C 1.2818 -2.2432 0.0 0 +M V30 5 C 3.8811 -0.724 0.0 0 +M V30 6 C 2.5755 -2.9791 0.0 0 +M V30 7 C -0.0356 -2.9791 0.0 0 +M V30 8 N 3.8693 -2.2195 0.0 0 +M V30 9 C 5.1749 0.0474 0.0 0 +M V30 10 C 2.5637 -4.4746 0.0 0 +M V30 11 C -0.0474 -4.4746 0.0 0 +M V30 12 O 5.163 1.5667 0.0 0 +M V30 13 N 6.4686 -0.7002 0.0 0 +M V30 14 C 1.2462 -5.2223 0.0 0 +M V30 15 C 7.7623 0.0712 0.0 0 +M V30 16 C 9.056 -0.6765 0.0 0 CFG=1 +M V30 17 N 10.3498 0.0949 0.0 0 +M V30 18 C 9.0442 -2.172 0.0 0 +M V30 19 C 11.6435 -0.6527 0.0 0 +M V30 20 C 10.3379 1.6141 0.0 0 +M V30 21 C 12.9372 0.1186 0.0 0 +M V30 22 C 11.6316 -2.1482 0.0 0 +M V30 23 C 14.231 -0.629 0.0 0 +M V30 24 C 12.9254 -2.8841 0.0 0 +M V30 25 C 14.2191 -2.1245 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 16 17 CFG=1 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 17 20 +M V30 20 2 19 21 +M V30 21 1 19 22 +M V30 22 1 21 23 +M V30 23 2 22 24 +M V30 24 2 23 25 +M V30 25 1 6 8 +M V30 26 1 11 14 +M V30 27 1 24 25 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 16) +M V30 END COLLECTION +M V30 END CTAB +M END +> +735 + +> +Z1149165276 + +> +336.388 + +> +2.920 + +> +2 + +> +73.800 + +> +5 + +> +parp3 + +> + + +> + + +$$$$ +Compound 736 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 0.1422 1.5055 0.0 0 +M V30 3 C -1.4818 -0.2963 0.0 0 +M V30 4 C -1.2447 2.1338 0.0 0 +M V30 5 C 1.4344 2.2643 0.0 0 +M V30 6 C -2.2405 1.0195 0.0 0 +M V30 7 C -2.1101 -1.6596 0.0 0 +M V30 8 N 2.7266 1.5292 0.0 0 +M V30 9 C -3.6157 -1.8019 0.0 0 +M V30 10 C 4.0188 2.288 0.0 0 +M V30 11 O 4.0069 3.8054 0.0 0 +M V30 12 C 5.311 1.553 0.0 0 +M V30 13 N 6.6032 2.3117 0.0 0 +M V30 14 N 5.2991 0.0592 0.0 0 +M V30 15 C 7.8954 1.5767 0.0 0 +M V30 16 C 6.5913 -0.6875 0.0 0 +M V30 17 C 7.8835 0.0829 0.0 0 +M V30 18 C 9.1876 2.3354 0.0 0 +M V30 19 O 6.5795 -2.1813 0.0 0 +M V30 20 C 9.1757 -0.6638 0.0 0 +M V30 21 C 10.4797 1.6004 0.0 0 +M V30 22 C 10.4679 0.1066 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 1 5 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 2 10 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 15 17 +M V30 17 1 15 18 +M V30 18 2 16 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 2 20 22 +M V30 22 1 4 6 +M V30 23 1 16 17 +M V30 24 1 21 22 +M V30 END BOND +M V30 END CTAB +M END +> +736 + +> +Z1149165310 + +> +313.374 + +> +2.878 + +> +2 + +> +70.560 + +> +4 + +> +Tankyrase1, parp3 + +> + + +> + + +$$$$ +Compound 737 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5069 0.0 0 +M V30 3 N -1.3185 2.2721 0.0 0 +M V30 4 C 1.2714 2.2721 0.0 0 +M V30 5 C -1.3303 3.7791 0.0 0 +M V30 6 C 1.2597 3.7791 0.0 0 +M V30 7 C 2.5547 1.5304 0.0 0 +M V30 8 N -0.047 4.5325 0.0 0 +M V30 9 C -2.6371 4.5325 0.0 0 +M V30 10 C 2.5429 4.5325 0.0 0 +M V30 11 C 3.8379 2.2957 0.0 0 +M V30 12 O -2.6489 6.0395 0.0 0 +M V30 13 N -3.9439 3.8026 0.0 0 +M V30 14 C 3.8262 3.8026 0.0 0 +M V30 15 C -5.2507 4.5561 0.0 0 +M V30 16 C -6.5575 3.8262 0.0 0 +M V30 17 N -6.7223 2.3545 0.0 0 +M V30 18 N -7.9349 4.4501 0.0 0 +M V30 19 N -8.1939 2.0602 0.0 0 +M V30 20 C -8.9474 3.3552 0.0 0 +M V30 21 C -8.4058 5.8864 0.0 0 +M V30 22 C -10.419 3.6731 0.0 0 +M V30 23 C -9.8775 6.2043 0.0 0 +M V30 24 C -10.8899 5.1094 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 18 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 6 8 +M V30 25 1 11 14 +M V30 26 2 19 20 +M V30 27 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +737 + +> +Z1149166864 + +> +324.337 + +> +-0.175 + +> +2 + +> +101.270 + +> +3 + +> +parp15, Tankyrase1, parp3 + +> + + +> + + +$$$$ +Compound 738 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.5139 0.0 0 +M V30 3 C -1.3247 2.2709 0.0 0 +M V30 4 C 1.2773 2.2709 0.0 0 +M V30 5 C -2.6375 1.5375 0.0 0 +M V30 6 C -1.3365 3.7848 0.0 0 +M V30 7 C 1.2655 3.7848 0.0 0 +M V30 8 O -3.9504 2.2945 0.0 0 +M V30 9 N -2.6494 0.0473 0.0 0 +M V30 10 C -0.0473 4.5536 0.0 0 +M V30 11 C -3.9622 -0.686 0.0 0 +M V30 12 C -1.3601 -0.686 0.0 0 +M V30 13 C -3.9741 -2.1762 0.0 0 +M V30 14 C -1.372 -2.1762 0.0 0 +M V30 15 N -2.6848 -2.9096 0.0 0 +M V30 16 C -2.6967 -4.3999 0.0 0 +M V30 17 O -4.0095 -5.145 0.0 0 +M V30 18 C -1.4074 -5.145 0.0 0 +M V30 19 C -0.1182 -4.388 0.0 0 +M V30 20 C -1.4193 -6.6353 0.0 0 +M V30 21 O -0.1301 -2.8741 0.0 0 +M V30 22 C 1.1709 -5.1332 0.0 0 +M V30 23 C -0.1301 -7.3804 0.0 0 +M V30 24 C 1.1591 -2.1171 0.0 0 +M V30 25 C 1.1591 -6.6235 0.0 0 +M V30 26 C 1.1472 -0.6032 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 9 11 +M V30 11 1 9 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 19 22 +M V30 22 2 20 23 +M V30 23 1 21 24 +M V30 24 2 22 25 +M V30 25 1 24 26 +M V30 26 1 7 10 +M V30 27 1 14 15 +M V30 28 1 23 25 +M V30 END BOND +M V30 END CTAB +M END +> +738 + +> +Z288801502 + +> +356.391 + +> +2.905 + +> +0 + +> +49.850 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL455549 + +> +0.88 + +$$$$ +Compound 739 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5071 0.0 0 +M V30 3 N -1.3187 2.2725 0.0 0 +M V30 4 C 1.2716 2.2725 0.0 0 +M V30 5 C -1.3305 3.7796 0.0 0 +M V30 6 C 1.2598 3.7796 0.0 0 +M V30 7 C 2.5551 1.5307 0.0 0 +M V30 8 N -0.047 4.5332 0.0 0 +M V30 9 C -2.6375 4.5332 0.0 0 +M V30 10 C 2.5433 4.5332 0.0 0 +M V30 11 C 3.8385 2.296 0.0 0 +M V30 12 O -2.6493 6.0404 0.0 0 +M V30 13 N -3.9445 3.8032 0.0 0 +M V30 14 C 3.8267 3.8032 0.0 0 +M V30 15 C -5.2515 4.5568 0.0 0 +M V30 16 C -6.5585 3.8267 0.0 0 +M V30 17 C -7.8655 4.5803 0.0 0 +M V30 18 C -6.5703 2.3431 0.0 0 +M V30 19 C -9.1725 3.8503 0.0 0 +M V30 20 C -7.8772 1.6013 0.0 0 +M V30 21 O -10.4795 4.6039 0.0 0 +M V30 22 N -9.1842 2.3667 0.0 0 +M V30 23 C -11.7865 3.8738 0.0 0 +M V30 24 C -13.0935 4.6274 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 1 19 21 +M V30 21 2 19 22 +M V30 22 1 21 23 +M V30 23 1 23 24 +M V30 24 1 6 8 +M V30 25 1 11 14 +M V30 26 1 20 22 +M V30 END BOND +M V30 END CTAB +M END +> +739 + +> +Z1149165532 + +> +324.334 + +> +2.056 + +> +2 + +> +92.680 + +> +5 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 740 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 30 33 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3834 -0.603 0.0 0 +M V30 3 C 0.9932 -1.0996 0.0 0 +M V30 4 S -2.696 0.1537 0.0 0 +M V30 5 N -1.2415 -2.0811 0.0 0 +M V30 6 N 0.2364 -2.3885 0.0 0 +M V30 7 N 2.4713 -0.9341 0.0 0 +M V30 8 C -4.0085 -0.5794 0.0 0 +M V30 9 C 3.3463 -2.1402 0.0 0 +M V30 10 C -5.321 0.1773 0.0 0 +M V30 11 C 4.8244 -1.9747 0.0 0 +M V30 12 N -6.6336 -0.5557 0.0 0 +M V30 13 N -5.3329 1.6909 0.0 0 +M V30 14 C 5.6994 -3.1808 0.0 0 +M V30 15 C -7.9461 0.201 0.0 0 +M V30 16 C -6.6454 2.4476 0.0 0 +M V30 17 C 7.1775 -3.0152 0.0 0 +M V30 18 C 5.0727 -4.5406 0.0 0 +M V30 19 C -7.9579 1.7145 0.0 0 +M V30 20 C -9.3887 -0.2483 0.0 0 +M V30 21 O -6.6572 3.9612 0.0 0 +M V30 22 C 8.0525 -4.2213 0.0 0 +M V30 23 C 5.9477 -5.7467 0.0 0 +M V30 24 S -9.4005 2.1875 0.0 0 +M V30 25 C -10.2874 0.9814 0.0 0 +M V30 26 O 9.5306 -4.0558 0.0 0 +M V30 27 C 7.4258 -5.5812 0.0 0 +M V30 28 C 10.4056 -5.2619 0.0 0 +M V30 29 O 8.3008 -6.7873 0.0 0 +M V30 30 C 9.7789 -6.6217 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 1 4 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 1 10 13 +M V30 13 1 11 14 +M V30 14 1 12 15 +M V30 15 1 13 16 +M V30 16 2 14 17 +M V30 17 1 14 18 +M V30 18 2 15 19 +M V30 19 1 15 20 +M V30 20 2 16 21 +M V30 21 1 17 22 +M V30 22 2 18 23 +M V30 23 1 19 24 +M V30 24 2 20 25 +M V30 25 1 22 26 +M V30 26 2 22 27 +M V30 27 1 26 28 +M V30 28 1 27 29 +M V30 29 1 29 30 +M V30 30 1 5 6 +M V30 31 1 16 19 +M V30 32 1 23 27 +M V30 33 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +740 + +> +Z17061015 + +> +461.581 + +> +2.519 + +> +2 + +> +97.730 + +> +9 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 741 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.3654 1.4619 0.0 0 +M V30 3 C 1.4265 -0.4008 0.0 0 CFG=2 +M V30 4 N -1.7684 2.0396 0.0 0 +M V30 5 N 0.5894 2.6173 0.0 0 +M V30 6 C 1.7684 -1.8391 0.0 0 +M V30 7 C 2.4994 0.6484 0.0 0 +M V30 8 N -1.6741 3.5486 0.0 0 +M V30 9 C 2.0749 2.6762 0.0 0 +M V30 10 C -0.2122 3.9023 0.0 0 +M V30 11 O 3.1949 -2.24 0.0 0 +M V30 12 N 0.672 -2.8648 0.0 0 +M V30 13 C 2.7705 4.0084 0.0 0 +M V30 14 C 2.853 1.4147 0.0 0 +M V30 15 N 0.4833 5.2346 0.0 0 +M V30 16 C 1.9688 5.2935 0.0 0 +M V30 17 C 4.256 4.0674 0.0 0 +M V30 18 C 4.3385 1.4737 0.0 0 +M V30 19 C -0.3183 6.5196 0.0 0 +M V30 20 O 2.6644 6.6257 0.0 0 +M V30 21 C 5.0341 2.8059 0.0 0 +M V30 22 C 0.3772 7.8519 0.0 0 +M V30 23 C -1.8273 6.4843 0.0 0 +M V30 24 C -0.4244 9.1369 0.0 0 +M V30 25 C 1.8627 7.9108 0.0 0 +M V30 26 C -2.629 7.7693 0.0 0 +M V30 27 C -1.9335 9.1016 0.0 0 +M V30 28 C -4.1381 7.734 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 3 1 CFG=1 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 3 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 1 5 10 +M V30 10 2 6 11 +M V30 11 1 6 12 +M V30 12 2 9 13 +M V30 13 1 9 14 +M V30 14 1 10 15 +M V30 15 1 13 16 +M V30 16 1 13 17 +M V30 17 2 14 18 +M V30 18 1 15 19 +M V30 19 2 16 20 +M V30 20 2 17 21 +M V30 21 2 19 22 +M V30 22 1 19 23 +M V30 23 1 22 24 +M V30 24 1 22 25 +M V30 25 2 23 26 +M V30 26 2 24 27 +M V30 27 1 26 28 +M V30 28 2 8 10 +M V30 29 1 15 16 +M V30 30 1 18 21 +M V30 31 1 26 27 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 3) +M V30 END COLLECTION +M V30 END CTAB +M END +> +741 + +> +Z17064424 + +> +393.462 + +> +2.605 + +> +1 + +> +94.110 + +> +3 + +> +parp14 + +> + + +> + + +$$$$ +Compound 742 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 0.7375 -1.2966 0.0 0 +M V30 3 C -0.0237 -2.5933 0.0 0 +M V30 4 C 2.2364 -1.2847 0.0 0 +M V30 5 N 0.7137 -3.89 0.0 0 +M V30 6 C 2.9859 -2.5814 0.0 0 +M V30 7 C 2.2126 -3.8781 0.0 0 +M V30 8 N 4.4491 -2.8788 0.0 0 +M V30 9 N 3.2119 -4.9844 0.0 0 +M V30 10 C 4.5919 -4.3658 0.0 0 +M V30 11 S 5.8885 -5.1034 0.0 0 +M V30 12 C 7.1852 -4.342 0.0 0 CFG=2 +M V30 13 C 8.4819 -5.0796 0.0 0 +M V30 14 C 7.1733 -2.8193 0.0 0 +M V30 15 N 9.7786 -4.3182 0.0 0 +M V30 16 N 8.47 -6.5785 0.0 0 +M V30 17 C 11.0753 -5.0558 0.0 0 +M V30 18 C 9.7667 -7.328 0.0 0 +M V30 19 C 11.0634 -6.5547 0.0 0 +M V30 20 C 12.3719 -4.2945 0.0 0 +M V30 21 O 9.7548 -8.8269 0.0 0 +M V30 22 C 12.36 -7.3042 0.0 0 +M V30 23 C 13.6686 -5.032 0.0 0 +M V30 24 C 13.6567 -6.5428 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 6 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 1 10 11 +M V30 11 1 12 11 CFG=1 +M V30 12 1 12 13 +M V30 13 1 12 14 +M V30 14 2 13 15 +M V30 15 1 13 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 17 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 1 19 22 +M V30 22 2 20 23 +M V30 23 2 22 24 +M V30 24 1 6 7 +M V30 25 2 9 10 +M V30 26 1 18 19 +M V30 27 1 23 24 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 12) +M V30 END COLLECTION +M V30 END CTAB +M END +> +742 + +> +Z237516006 + +> +357.817 + +> +2.633 + +> +2 + +> +83.030 + +> +3 + +> +parp10 + +> + + +> + + +$$$$ +Compound 743 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -1.4358 -0.4472 0.0 0 +M V30 3 C -2.5539 0.5649 0.0 0 +M V30 4 C -1.7536 -1.8948 0.0 0 +M V30 5 C -3.9897 0.1176 0.0 0 +M V30 6 C -3.1894 -2.342 0.0 0 +M V30 7 C -4.3075 -1.3299 0.0 0 +M V30 8 C -5.1078 1.1298 0.0 0 +M V30 9 C -6.5437 0.6826 0.0 0 CFG=2 +M V30 10 N -7.7677 1.577 0.0 0 +M V30 11 C -7.0144 -0.7296 0.0 0 +M V30 12 C -7.7794 3.0835 0.0 0 +M V30 13 C -8.9917 0.7061 0.0 0 +M V30 14 C -8.5209 -0.7179 0.0 0 +M V30 15 O -6.4966 3.8485 0.0 0 +M V30 16 C -9.0858 3.8485 0.0 0 +M V30 17 C -9.0976 5.355 0.0 0 +M V30 18 N -10.404 6.1082 0.0 0 +M V30 19 C -7.8147 6.1082 0.0 0 +M V30 20 N -10.4157 7.6147 0.0 0 +M V30 21 C -7.8265 7.6147 0.0 0 +M V30 22 C -6.5319 5.3667 0.0 0 +M V30 23 C -9.1329 8.3679 0.0 0 +M V30 24 C -6.5437 8.3679 0.0 0 +M V30 25 C -5.249 6.12 0.0 0 +M V30 26 O -9.1447 9.8744 0.0 0 +M V30 27 C -5.2608 7.6382 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 9 8 CFG=1 +M V30 9 1 9 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 1 10 13 +M V30 13 1 11 14 +M V30 14 2 12 15 +M V30 15 1 12 16 +M V30 16 1 16 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 1 19 22 +M V30 22 1 20 23 +M V30 23 1 21 24 +M V30 24 2 22 25 +M V30 25 2 23 26 +M V30 26 2 24 27 +M V30 27 1 6 7 +M V30 28 1 13 14 +M V30 29 1 21 23 +M V30 30 1 25 27 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 9) +M V30 END COLLECTION +M V30 END CTAB +M END +> +743 + +> +Z511988090 + +> +365.401 + +> +1.933 + +> +1 + +> +61.770 + +> +4 + +> +parp3 + +> + + +> + + +$$$$ +Compound 744 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3106 0.7675 0.0 0 +M V30 3 C 1.287 0.7675 0.0 0 +M V30 4 N -1.4759 2.2671 0.0 0 +M V30 5 N -2.6921 0.1653 0.0 0 +M V30 6 C 2.5741 0.0236 0.0 0 +M V30 7 N -2.9519 2.5859 0.0 0 +M V30 8 C -3.7076 1.287 0.0 0 +M V30 9 C -3.011 -1.287 0.0 0 +M V30 10 N 3.8611 0.7911 0.0 0 +M V30 11 N 2.5623 -1.4641 0.0 0 +M V30 12 C -5.2072 1.1453 0.0 0 +M V30 13 C -1.9128 -2.2789 0.0 0 +M V30 14 C 5.1482 0.0472 0.0 0 +M V30 15 C 3.8493 -2.208 0.0 0 +M V30 16 C -6.1046 2.3733 0.0 0 +M V30 17 C 5.1364 -1.4405 0.0 0 +M V30 18 C 6.4353 0.8147 0.0 0 +M V30 19 O 3.8375 -3.6958 0.0 0 +M V30 20 C -7.6042 2.2316 0.0 0 +M V30 21 C -5.5024 3.7549 0.0 0 +M V30 22 C 6.4234 -2.1844 0.0 0 +M V30 23 C 7.7223 0.0708 0.0 0 +M V30 24 C -8.5016 3.4597 0.0 0 +M V30 25 C -6.3998 4.9829 0.0 0 +M V30 26 C 7.7105 -1.4169 0.0 0 +M V30 27 C -7.8994 4.8412 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 2 14 17 +M V30 17 1 14 18 +M V30 18 2 15 19 +M V30 19 2 16 20 +M V30 20 1 16 21 +M V30 21 1 17 22 +M V30 22 2 18 23 +M V30 23 1 20 24 +M V30 24 2 21 25 +M V30 25 2 22 26 +M V30 26 2 24 27 +M V30 27 2 7 8 +M V30 28 1 15 17 +M V30 29 1 23 26 +M V30 30 1 25 27 +M V30 END BOND +M V30 END CTAB +M END +> +744 + +> +Z17182329 + +> +377.463 + +> +1.846 + +> +1 + +> +72.170 + +> +6 + +> +parp15, parp3 + +> + + +> + + +$$$$ +Compound 745 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O -0.7527 -1.2819 0.0 0 +M V30 3 O 0.7409 1.3054 0.0 0 +M V30 4 C -1.3054 0.7527 0.0 0 +M V30 5 C 1.2819 -0.7291 0.0 0 +M V30 6 C -1.3172 2.2581 0.0 0 +M V30 7 C -2.6109 0.0235 0.0 0 +M V30 8 C 1.2701 -2.211 0.0 0 +M V30 9 C -2.6227 3.0108 0.0 0 +M V30 10 C -3.9164 0.7762 0.0 0 +M V30 11 O 2.5521 -2.9402 0.0 0 +M V30 12 C -3.9282 2.2698 0.0 0 +M V30 13 C -5.2336 3.0226 0.0 0 +M V30 14 N -6.5391 2.2816 0.0 0 +M V30 15 C -7.8446 3.0343 0.0 0 +M V30 16 O -7.8564 4.5397 0.0 0 +M V30 17 N -9.1501 2.2934 0.0 0 +M V30 18 C -10.4556 3.0461 0.0 0 +M V30 19 C -11.761 2.3051 0.0 0 +M V30 20 C -10.4673 4.5515 0.0 0 +M V30 21 C -13.0665 3.0578 0.0 0 +M V30 22 C -11.7728 5.3042 0.0 0 +M V30 23 F -14.372 2.3169 0.0 0 +M V30 24 C -13.0783 4.5633 0.0 0 +M V30 25 C -14.3838 5.316 0.0 0 +M V30 26 C -15.6893 4.5868 0.0 0 +M V30 27 C -14.3955 6.8214 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 12 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 17 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 1 21 23 +M V30 23 2 21 24 +M V30 24 1 24 25 +M V30 25 1 25 26 +M V30 26 1 25 27 +M V30 27 1 10 12 +M V30 28 1 22 24 +M V30 END BOND +M V30 END CTAB +M END +> +745 + +> +Z1845785044 + +> +394.460 + +> +2.633 + +> +3 + +> +95.500 + +> +7 + +> +DNA_pk + +> + + +> + + +$$$$ +Compound 746 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -0.0117 -1.4845 0.0 0 +M V30 3 C -1.3195 -2.215 0.0 0 +M V30 4 C 1.2724 -2.215 0.0 0 +M V30 5 N -2.757 -1.7437 0.0 0 +M V30 6 C -1.3313 -3.6995 0.0 0 +M V30 7 C 1.2606 -3.6995 0.0 0 +M V30 8 C -3.6524 -2.9455 0.0 0 +M V30 9 N -2.7687 -4.1472 0.0 0 +M V30 10 C -0.0471 -4.43 0.0 0 +M V30 11 C -5.1605 -2.9337 0.0 0 +M V30 12 C -5.9145 -4.2179 0.0 0 +M V30 13 C -5.9145 -1.6259 0.0 0 +M V30 14 C -7.4226 -4.2061 0.0 0 +M V30 15 C -7.4226 -1.6141 0.0 0 +M V30 16 C -8.1767 -2.8983 0.0 0 +M V30 17 N -9.6848 -2.8866 0.0 0 +M V30 18 C -10.4389 -1.5787 0.0 0 +M V30 19 O -9.7084 -0.2709 0.0 0 +M V30 20 C -11.947 -1.567 0.0 0 +M V30 21 N -12.7128 -0.2592 0.0 0 +M V30 22 N -12.1119 1.1192 0.0 0 +M V30 23 C -14.2091 -0.0942 0.0 0 +M V30 24 C -13.2312 2.1325 0.0 0 +M V30 25 N -14.5272 1.3785 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 6 10 +M V30 10 1 8 11 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 2 13 15 +M V30 15 2 14 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 20 21 +M V30 21 1 21 22 +M V30 22 1 21 23 +M V30 23 2 22 24 +M V30 24 2 23 25 +M V30 25 1 7 10 +M V30 26 1 8 9 +M V30 27 1 15 16 +M V30 28 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +746 + +> +Z1151470077 + +> +336.323 + +> +2.126 + +> +2 + +> +88.490 + +> +4 + +> +DNA_pk + +> + + +> + + +$$$$ +Compound 747 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2945 -0.7363 0.0 0 +M V30 3 C 2.5891 0.0237 0.0 0 +M V30 4 C 1.2827 -2.2328 0.0 0 +M V30 5 C 3.8837 -0.7126 0.0 0 +M V30 6 C 2.5773 1.544 0.0 0 +M V30 7 C 2.5773 -2.9692 0.0 0 +M V30 8 C 3.8719 -2.2091 0.0 0 +M V30 9 F 2.5654 3.0642 0.0 0 +M V30 10 F 4.0738 1.5558 0.0 0 +M V30 11 F 1.057 1.5558 0.0 0 +M V30 12 S 5.1665 -2.9455 0.0 0 +M V30 13 C 6.4611 -2.1853 0.0 0 CFG=2 +M V30 14 C 7.7557 -2.9217 0.0 0 +M V30 15 C 6.4492 -0.6651 0.0 0 +M V30 16 N 9.0502 -2.1616 0.0 0 +M V30 17 N 7.7438 -4.4182 0.0 0 +M V30 18 C 10.3448 -2.8979 0.0 0 +M V30 19 C 9.0384 -5.1665 0.0 0 +M V30 20 C 10.333 -4.3945 0.0 0 +M V30 21 C 11.6394 -2.1378 0.0 0 +M V30 22 O 9.0265 -6.663 0.0 0 +M V30 23 C 11.6276 -5.1427 0.0 0 +M V30 24 C 12.934 -2.8742 0.0 0 +M V30 25 C 12.9222 -4.3826 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 1 6 10 +M V30 10 1 6 11 +M V30 11 1 8 12 +M V30 12 1 13 12 CFG=1 +M V30 13 1 13 14 +M V30 14 1 13 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 1 20 23 +M V30 23 2 21 24 +M V30 24 2 23 25 +M V30 25 1 7 8 +M V30 26 1 19 20 +M V30 27 1 24 25 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 13) +M V30 END COLLECTION +M V30 END CTAB +M END +> +747 + +> +Z17289122 + +> +384.803 + +> +4.255 + +> +1 + +> +41.460 + +> +4 + +> +parp2 + +> + + +> + + +$$$$ +Compound 748 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5038 0.0 0 +M V30 3 N -1.3158 2.2558 0.0 0 +M V30 4 C 1.2688 2.2558 0.0 0 +M V30 5 C -1.3276 3.7596 0.0 0 +M V30 6 O 1.2571 3.7596 0.0 0 +M V30 7 C -0.0469 4.5116 0.0 0 +M V30 8 C -2.6317 4.5116 0.0 0 +M V30 9 C -0.0587 6.0154 0.0 0 +M V30 10 C -2.6435 6.0154 0.0 0 +M V30 11 C -1.3628 6.7674 0.0 0 +M V30 12 C -3.9476 6.7674 0.0 0 CFG=1 +M V30 13 N -5.2517 6.0389 0.0 0 +M V30 14 C -3.9594 8.2712 0.0 0 +M V30 15 C -6.5559 6.7909 0.0 0 +M V30 16 O -6.5676 8.2947 0.0 0 +M V30 17 N -7.86 6.0624 0.0 0 +M V30 18 C -9.1642 6.8144 0.0 0 +M V30 19 C -10.4683 6.0859 0.0 0 +M V30 20 C -11.7724 6.8379 0.0 0 +M V30 21 C -13.0766 6.1094 0.0 0 +M V30 22 O -14.3807 6.8614 0.0 0 +M V30 23 C -15.6848 6.1329 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 7 9 +M V30 9 2 8 10 +M V30 10 2 9 11 +M V30 11 1 10 12 +M V30 12 1 12 13 CFG=1 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 17 18 +M V30 18 1 18 19 +M V30 19 1 19 20 +M V30 20 1 20 21 +M V30 21 1 21 22 +M V30 22 1 22 23 +M V30 23 1 6 7 +M V30 24 1 10 11 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 12) +M V30 END COLLECTION +M V30 END CTAB +M END +> +748 + +> +Z437925620 + +> +321.372 + +> +0.575 + +> +3 + +> +88.690 + +> +7 + +> +parp2 + +> + + +> + + +$$$$ +Compound 749 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 0.4487 -1.4172 0.0 0 +M V30 3 C 1.2046 0.8975 0.0 0 +M V30 4 N 1.9369 -1.4054 0.0 0 +M V30 5 N -0.4487 -2.6219 0.0 0 +M V30 6 C 2.4093 0.0236 0.0 0 +M V30 7 C 1.1928 2.4093 0.0 0 +M V30 8 C -1.9487 -2.4565 0.0 0 +M V30 9 C 2.4801 3.177 0.0 0 +M V30 10 O -2.5746 -1.0747 0.0 0 +M V30 11 C -2.8463 -3.6612 0.0 0 +M V30 12 C 3.7675 2.4329 0.0 0 +M V30 13 C 2.4683 4.6887 0.0 0 +M V30 14 C -4.3462 -3.4958 0.0 0 +M V30 15 C -2.2439 -5.0194 0.0 0 +M V30 16 C 5.0548 3.2006 0.0 0 +M V30 17 C 3.7557 5.4446 0.0 0 +M V30 18 C -5.2438 -4.7005 0.0 0 +M V30 19 C -3.1415 -6.2241 0.0 0 +M V30 20 C 5.043 4.7123 0.0 0 +M V30 21 C -6.7437 -4.5352 0.0 0 +M V30 22 C -4.6415 -6.0587 0.0 0 +M V30 23 F 6.3304 5.4682 0.0 0 +M V30 24 N -8.2437 -4.3698 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 1 5 8 +M V30 8 1 7 9 +M V30 9 2 8 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 2 11 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 2 13 17 +M V30 17 1 14 18 +M V30 18 2 15 19 +M V30 19 2 16 20 +M V30 20 1 18 21 +M V30 21 2 18 22 +M V30 22 1 20 23 +M V30 23 3 21 24 +M V30 24 1 4 6 +M V30 25 1 17 20 +M V30 26 1 19 22 +M V30 END BOND +M V30 END CTAB +M END +> +749 + +> +Z89633553 + +> +337.371 + +> +3.651 + +> +1 + +> +65.780 + +> +4 + +> +parp14 + +> + + +> + + +$$$$ +Compound 750 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 0.1418 1.5012 0.0 0 +M V30 3 C -1.4775 -0.2955 0.0 0 +M V30 4 N -1.2411 2.1277 0.0 0 +M V30 5 N 1.4302 2.2577 0.0 0 +M V30 6 C -2.2341 1.0165 0.0 0 +M V30 7 C -2.2341 -1.5839 0.0 0 +M V30 8 C 2.7187 1.5248 0.0 0 +M V30 9 C -3.7471 1.0283 0.0 0 +M V30 10 C -3.7471 -1.5721 0.0 0 CFG=2 +M V30 11 O 2.7069 0.0354 0.0 0 +M V30 12 C 4.0072 2.2813 0.0 0 +M V30 13 C -4.5036 -0.26 0.0 0 +M V30 14 C -4.5036 -2.8605 0.0 0 +M V30 15 C 5.2956 1.5485 0.0 0 +M V30 16 C -6.0167 -2.8487 0.0 0 +M V30 17 C 6.5841 2.305 0.0 0 +M V30 18 O 6.5722 3.818 0.0 0 +M V30 19 O 7.8725 1.5721 0.0 0 +M V30 20 C 9.161 2.3286 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 1 10 14 CFG=1 +M V30 14 1 12 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 1 4 6 +M V30 21 1 10 13 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 10) +M V30 END COLLECTION +M V30 END CTAB +M END +> +750 + +> +Z512860604 + +> +296.385 + +> +3.275 + +> +1 + +> +68.290 + +> +6 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL448447 + +> +0.95 + +$$$$ +Compound 751 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 29 32 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5069 0.0 0 +M V30 3 N -1.3185 2.2721 0.0 0 +M V30 4 C 1.2714 2.2721 0.0 0 +M V30 5 C -1.3303 3.779 0.0 0 +M V30 6 C 1.2596 3.779 0.0 0 +M V30 7 C 2.5546 1.5304 0.0 0 +M V30 8 N -0.047 4.5325 0.0 0 +M V30 9 C -2.637 4.5325 0.0 0 +M V30 10 C 2.5429 4.5325 0.0 0 +M V30 11 C 3.8379 2.2956 0.0 0 +M V30 12 O -2.6488 6.0394 0.0 0 +M V30 13 N -3.9438 3.8026 0.0 0 +M V30 14 C 3.8261 3.8026 0.0 0 +M V30 15 C -5.2506 4.556 0.0 0 +M V30 16 C -6.5574 3.8261 0.0 0 +M V30 17 C -6.5692 2.3427 0.0 0 +M V30 18 C -7.8642 4.5796 0.0 0 +M V30 19 C -7.8759 1.601 0.0 0 +M V30 20 C -9.1709 3.8496 0.0 0 +M V30 21 C -9.1827 2.3663 0.0 0 +M V30 22 C -10.4895 1.6246 0.0 0 +M V30 23 O -11.7963 2.3898 0.0 0 +M V30 24 C -13.103 1.6481 0.0 0 +M V30 25 C -13.1148 0.1648 0.0 0 +M V30 26 C -14.4098 2.4134 0.0 0 +M V30 27 C -14.4216 -0.5768 0.0 0 +M V30 28 C -15.7166 1.6717 0.0 0 +M V30 29 O -15.7284 0.1883 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 2 19 21 +M V30 21 1 21 22 +M V30 22 1 22 23 +M V30 23 1 23 24 +M V30 24 1 24 25 +M V30 25 1 24 26 +M V30 26 1 25 27 +M V30 27 1 26 28 +M V30 28 1 27 29 +M V30 29 1 6 8 +M V30 30 1 11 14 +M V30 31 1 20 21 +M V30 32 1 28 29 +M V30 END BOND +M V30 END CTAB +M END +> +751 + +> +Z1501729919 + +> +393.436 + +> +1.494 + +> +2 + +> +89.020 + +> +6 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 752 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O -0.7713 -1.2935 0.0 0 +M V30 3 O 0.7357 1.3172 0.0 0 +M V30 4 N 1.2935 -0.7357 0.0 0 +M V30 5 C -1.3172 0.7594 0.0 0 +M V30 6 C 2.587 0.0237 0.0 0 +M V30 7 C 3.8805 -0.712 0.0 0 +M V30 8 C 5.174 0.0474 0.0 0 +M V30 9 N 6.4675 -0.6882 0.0 0 +M V30 10 C 7.761 0.0712 0.0 0 +M V30 11 O 7.7492 1.5901 0.0 0 +M V30 12 N 9.0545 -0.6645 0.0 0 +M V30 13 C 10.348 0.0949 0.0 0 CFG=2 +M V30 14 C 11.6416 -0.6408 0.0 0 +M V30 15 C 10.3362 1.6139 0.0 0 +M V30 16 C 12.9351 0.1186 0.0 0 +M V30 17 C 11.6297 -2.136 0.0 0 +M V30 18 C 11.6297 2.3852 0.0 0 +M V30 19 C 14.2286 -0.617 0.0 0 +M V30 20 C 12.9232 1.6376 0.0 0 +M V30 21 C 12.9232 -2.8718 0.0 0 +M V30 22 C 14.2167 -2.1123 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 4 6 +M V30 6 1 6 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 1 9 10 +M V30 10 2 10 11 +M V30 11 1 10 12 +M V30 12 1 13 12 CFG=1 +M V30 13 1 13 14 +M V30 14 1 13 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 1 16 20 +M V30 20 2 17 21 +M V30 21 2 19 22 +M V30 22 1 18 20 +M V30 23 1 21 22 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 13) +M V30 END COLLECTION +M V30 END CTAB +M END +> +752 + +> +Z336689022 + +> +325.426 + +> +2.056 + +> +3 + +> +87.300 + +> +5 + +> +ATM + +> + + +> + + +$$$$ +Compound 753 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4892 0.0 0 +M V30 3 N 1.2764 -2.222 0.0 0 +M V30 4 N -1.3237 -2.222 0.0 0 +M V30 5 C 2.5647 -1.4655 0.0 0 +M V30 6 C -2.6356 -1.4655 0.0 0 +M V30 7 C 3.853 -2.1983 0.0 0 +M V30 8 C 2.5529 0.0472 0.0 0 +M V30 9 C -3.9476 -2.1983 0.0 0 +M V30 10 N 3.8412 -3.6875 0.0 0 +M V30 11 C 5.1413 -1.4419 0.0 0 +M V30 12 C 3.8412 0.8037 0.0 0 +M V30 13 C -5.2595 -1.4419 0.0 0 +M V30 14 N 5.0468 -4.5622 0.0 0 +M V30 15 C 2.612 -4.5622 0.0 0 +M V30 16 C 5.1295 0.0709 0.0 0 +M V30 17 N -6.5714 -2.1747 0.0 0 +M V30 18 C 4.574 -5.9805 0.0 0 +M V30 19 C 3.0611 -5.9805 0.0 0 +M V30 20 C 1.1701 -4.0894 0.0 0 +M V30 21 C -7.8834 -1.4183 0.0 0 +M V30 22 C 5.4486 -7.186 0.0 0 +M V30 23 O -7.8952 0.0945 0.0 0 +M V30 24 C -9.1953 -2.151 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 1 10 15 +M V30 15 2 11 16 +M V30 16 1 13 17 +M V30 17 2 14 18 +M V30 18 2 15 19 +M V30 19 1 15 20 +M V30 20 1 17 21 +M V30 21 1 18 22 +M V30 22 2 21 23 +M V30 23 1 21 24 +M V30 24 1 12 16 +M V30 25 1 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +753 + +> +Z413301062 + +> +329.397 + +> +1.748 + +> +3 + +> +88.050 + +> +6 + +> +ATM + +> + + +> + + +$$$$ +Compound 754 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O -0.7721 -1.2948 0.0 0 +M V30 3 O 0.7365 1.3186 0.0 0 +M V30 4 C 1.2948 -0.7484 0.0 0 +M V30 5 C -1.3186 0.7602 0.0 0 +M V30 6 N 1.2829 -2.2452 0.0 0 +M V30 7 C 2.5897 0.0118 0.0 0 +M V30 8 C 2.5778 -2.9936 0.0 0 +M V30 9 C 3.8846 -0.7365 0.0 0 +M V30 10 C 3.8727 -2.2214 0.0 0 +M V30 11 N 5.1676 -2.9699 0.0 0 +M V30 12 C 6.4625 -2.1977 0.0 0 +M V30 13 O 6.4506 -0.6771 0.0 0 +M V30 14 N 7.7573 -2.9461 0.0 0 +M V30 15 C 9.0522 -2.1739 0.0 0 +M V30 16 C 10.3471 -2.9223 0.0 0 +M V30 17 C 10.3352 -4.4192 0.0 0 +M V30 18 C 11.642 -2.1502 0.0 0 +M V30 19 C 11.6301 -5.1557 0.0 0 +M V30 20 C 12.9369 -2.8986 0.0 0 +M V30 21 O 11.9271 -6.6169 0.0 0 +M V30 22 C 12.925 -4.3954 0.0 0 +M V30 23 C 13.412 -6.7595 0.0 0 +M V30 24 O 14.0298 -5.3933 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 2 8 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 1 19 21 +M V30 21 2 19 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 9 10 +M V30 25 1 20 22 +M V30 26 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +754 + +> +Z435181810 + +> +349.362 + +> +2.201 + +> +2 + +> +106.620 + +> +4 + +> +DNA_pk + +> + + +> + + +$$$$ +Compound 755 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2895 0.7689 0.0 0 +M V30 3 N 1.2777 2.2833 0.0 0 +M V30 4 C 2.579 0.0236 0.0 0 +M V30 5 C 2.5672 3.0404 0.0 0 +M V30 6 C 3.8686 0.7926 0.0 0 +M V30 7 C 2.5672 -1.4669 0.0 0 +M V30 8 N 3.8567 2.3069 0.0 0 +M V30 9 C 2.5554 4.5547 0.0 0 +M V30 10 C 5.1581 0.0473 0.0 0 +M V30 11 C 3.8567 -2.2004 0.0 0 +M V30 12 N 3.8449 5.3237 0.0 0 +M V30 13 C 5.1463 -1.4433 0.0 0 +M V30 14 C 3.8331 6.8381 0.0 0 +M V30 15 C 5.1344 4.5784 0.0 0 +M V30 16 N 2.5199 7.5952 0.0 0 +M V30 17 C 5.1226 7.5952 0.0 0 +M V30 18 C 2.508 9.1095 0.0 0 +M V30 19 C 5.1108 9.1095 0.0 0 +M V30 20 C 6.4122 6.8499 0.0 0 +M V30 21 N 3.7976 9.8667 0.0 0 +M V30 22 C 6.4003 9.8667 0.0 0 +M V30 23 N 7.7017 7.6071 0.0 0 +M V30 24 C 7.6899 9.1332 0.0 0 +M V30 25 C 8.9912 6.8617 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 1 12 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 1 16 18 +M V30 18 2 17 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 1 19 22 +M V30 22 1 20 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 1 6 8 +M V30 26 1 11 13 +M V30 27 1 19 21 +M V30 28 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +755 + +> +Z989211876 + +> +336.391 + +> +0.424 + +> +1 + +> +73.720 + +> +3 + +> +parp15 + +> + + +> + + +$$$$ +Compound 756 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.285 -0.7427 0.0 0 +M V30 3 N 1.2732 -2.2282 0.0 0 +M V30 4 N 2.5701 0.0117 0.0 0 +M V30 5 C -0.0353 -2.9592 0.0 0 +M V30 6 C 2.5583 1.5208 0.0 0 +M V30 7 C -1.344 -2.2046 0.0 0 +M V30 8 C -0.0471 -4.4447 0.0 0 +M V30 9 C 3.8434 2.2754 0.0 0 +M V30 10 C 1.2497 2.2754 0.0 0 +M V30 11 C -2.6526 -2.9356 0.0 0 +M V30 12 C -1.3558 -5.1756 0.0 0 +M V30 13 C 3.8316 3.7845 0.0 0 +M V30 14 C 1.2379 3.7845 0.0 0 +M V30 15 C -2.6644 -4.4211 0.0 0 +M V30 16 C -4.091 -2.464 0.0 0 +M V30 17 C 2.523 4.539 0.0 0 +M V30 18 O -4.1028 -4.8691 0.0 0 +M V30 19 C -4.987 -3.6666 0.0 0 +M V30 20 O 2.5112 6.0481 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 2 11 15 +M V30 15 1 11 16 +M V30 16 1 13 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 1 17 20 +M V30 20 1 12 15 +M V30 21 1 14 17 +M V30 22 1 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +756 + +> +Z413791288 + +> +276.331 + +> +1.283 + +> +3 + +> +70.590 + +> +2 + +> +ATM + +> + + +> + + +$$$$ +Compound 757 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.1649 1.4965 0.0 0 +M V30 3 C 1.4494 -0.2945 0.0 0 +M V30 4 N 1.1901 2.121 0.0 0 +M V30 5 N -1.4729 2.2507 0.0 0 +M V30 6 C 2.1917 1.0134 0.0 0 +M V30 7 C 2.1917 -1.579 0.0 0 +M V30 8 C -2.7809 1.5201 0.0 0 +M V30 9 C 3.6765 1.0251 0.0 0 +M V30 10 C 3.6765 -1.5672 0.0 0 +M V30 11 C -4.0889 2.2742 0.0 0 +M V30 12 C 4.4189 -0.2592 0.0 0 +M V30 13 N -5.3969 1.5436 0.0 0 +M V30 14 C -6.7049 2.2978 0.0 0 +M V30 15 O -6.7167 3.8061 0.0 0 +M V30 16 N -8.0129 1.5672 0.0 0 +M V30 17 C -9.321 2.3214 0.0 0 +M V30 18 C -9.3327 3.8297 0.0 0 +M V30 19 C -10.629 1.5908 0.0 0 +M V30 20 C -10.6407 4.5839 0.0 0 +M V30 21 C -11.937 2.3449 0.0 0 +M V30 22 C -11.9487 3.8533 0.0 0 +M V30 23 O -13.2567 4.6074 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 11 13 +M V30 13 1 13 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 22 23 +M V30 23 1 4 6 +M V30 24 1 10 12 +M V30 25 1 21 22 +M V30 END BOND +M V30 END CTAB +M END +> +757 + +> +Z446561012 + +> +334.436 + +> +2.163 + +> +4 + +> +86.280 + +> +5 + +> +ATM + +> + + +> + + +$$$$ +Compound 758 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4962 0.0 0 +M V30 3 N 1.2824 -2.2443 0.0 0 +M V30 4 C -1.3299 -2.2443 0.0 0 +M V30 5 C 1.2706 -3.7406 0.0 0 +M V30 6 C -1.3418 -3.7406 0.0 0 +M V30 7 C -2.6481 -1.4843 0.0 0 +M V30 8 N -0.0474 -4.4768 0.0 0 +M V30 9 C 2.5649 -4.4768 0.0 0 +M V30 10 C -2.6599 -4.4768 0.0 0 +M V30 11 C -3.9662 -2.2324 0.0 0 +M V30 12 N 3.8593 -3.7168 0.0 0 +M V30 13 C -3.9781 -3.7168 0.0 0 +M V30 14 C 5.1537 -4.4531 0.0 0 +M V30 15 C 3.8474 -2.1968 0.0 0 +M V30 16 O 5.1418 -5.9493 0.0 0 +M V30 17 N 6.4481 -3.6931 0.0 0 +M V30 18 C 5.1418 -1.4368 0.0 0 +M V30 19 C 7.7424 -4.4293 0.0 0 +M V30 20 C 9.0368 -3.6693 0.0 0 +M V30 21 C 7.7306 -5.9256 0.0 0 +M V30 22 C 10.3312 -4.4056 0.0 0 +M V30 23 C 9.0249 -6.6618 0.0 0 +M V30 24 C 10.3193 -5.9018 0.0 0 +M V30 25 O 11.6137 -6.6381 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 1 12 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 24 25 +M V30 25 1 6 8 +M V30 26 1 11 13 +M V30 27 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +758 + +> +Z446558906 + +> +344.408 + +> +-0.328 + +> +3 + +> +94.030 + +> +4 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 759 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5072 0.0 0 +M V30 3 N -1.3188 2.2609 0.0 0 +M V30 4 C 1.2717 2.2609 0.0 0 +M V30 5 C -1.3306 3.7681 0.0 0 +M V30 6 C 1.2599 3.7681 0.0 0 +M V30 7 C 2.5553 1.519 0.0 0 +M V30 8 N -0.0471 4.5218 0.0 0 +M V30 9 C -2.6377 4.5218 0.0 0 +M V30 10 C 2.5435 4.5218 0.0 0 +M V30 11 C 3.8388 2.2726 0.0 0 +M V30 12 C -3.9448 3.7917 0.0 0 +M V30 13 C 3.827 3.7917 0.0 0 +M V30 14 C -5.2519 4.5453 0.0 0 +M V30 15 O -5.2636 6.0526 0.0 0 +M V30 16 N -6.559 3.8152 0.0 0 +M V30 17 C -7.8661 4.5689 0.0 0 CFG=2 +M V30 18 C -7.8778 6.0762 0.0 0 +M V30 19 C -9.1731 3.8388 0.0 0 +M V30 20 C -9.1849 6.8298 0.0 0 +M V30 21 C -6.5943 6.8298 0.0 0 +M V30 22 C -10.4802 4.5924 0.0 0 +M V30 23 C -10.1505 2.7083 0.0 0 +M V30 24 C -8.2193 2.7083 0.0 0 +M V30 25 C -9.1967 8.3371 0.0 0 +M V30 26 C -10.492 6.0997 0.0 0 +M V30 27 C -6.6061 8.3371 0.0 0 +M V30 28 C -7.9132 9.0907 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 17 16 CFG=1 +M V30 17 1 17 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 1 18 21 +M V30 21 1 19 22 +M V30 22 1 19 23 +M V30 23 1 19 24 +M V30 24 1 20 25 +M V30 25 1 20 26 +M V30 26 2 21 27 +M V30 27 2 25 28 +M V30 28 1 6 8 +M V30 29 1 11 13 +M V30 30 1 22 26 +M V30 31 1 27 28 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 17) +M V30 END COLLECTION +M V30 END CTAB +M END +> +759 + +> +Z1500828562 + +> +375.464 + +> +3.221 + +> +2 + +> +70.560 + +> +4 + +> +parp15 + +> + + +> + + +$$$$ +Compound 760 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O -0.7609 -1.2959 0.0 0 +M V30 3 O 0.7371 1.3196 0.0 0 +M V30 4 N -1.3196 0.7609 0.0 0 +M V30 5 C 1.2959 -0.7371 0.0 0 +M V30 6 C 2.5918 0.0237 0.0 0 +M V30 7 C 1.284 -2.2351 0.0 0 +M V30 8 C 3.8877 -0.7133 0.0 0 +M V30 9 C 2.5799 -2.9841 0.0 0 +M V30 10 C 3.8758 -2.2113 0.0 0 +M V30 11 N 5.1717 -2.9603 0.0 0 +M V30 12 C 6.4676 -2.1994 0.0 0 +M V30 13 O 6.4557 -0.6776 0.0 0 +M V30 14 N 7.7635 -2.9484 0.0 0 +M V30 15 C 9.0594 -2.1875 0.0 0 +M V30 16 C 10.3553 -2.9366 0.0 0 +M V30 17 C 10.3434 -4.4346 0.0 0 +M V30 18 C 11.6512 -2.1757 0.0 0 +M V30 19 C 11.6394 -5.1717 0.0 0 +M V30 20 C 12.9471 -2.9247 0.0 0 +M V30 21 O 11.6275 -6.6697 0.0 0 +M V30 22 C 12.9353 -4.4108 0.0 0 +M V30 23 C 12.9234 -7.4068 0.0 0 +M V30 24 O 14.2312 -5.1479 0.0 0 +M V30 25 C 14.2193 -6.6459 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 2 5 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 2 8 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 1 19 21 +M V30 21 2 19 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 1 9 10 +M V30 26 1 20 22 +M V30 27 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +760 + +> +Z124837012 + +> +363.388 + +> +1.334 + +> +3 + +> +119.750 + +> +4 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 761 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.7559 -1.2875 0.0 0 +M V30 3 N -0.0236 -2.575 0.0 0 +M V30 4 N -2.2678 -1.2756 0.0 0 +M V30 5 C 1.4646 -2.5631 0.0 0 +M V30 6 C -3.0238 0.0354 0.0 0 CFG=2 +M V30 7 C 2.197 -3.8506 0.0 0 +M V30 8 C 2.197 -1.252 0.0 0 +M V30 9 C -4.5357 0.0472 0.0 0 +M V30 10 C -2.2915 1.3465 0.0 0 +M V30 11 O 3.6853 -3.8388 0.0 0 +M V30 12 N 1.441 -5.1381 0.0 0 +M V30 13 O 1.441 0.059 0.0 0 +M V30 14 N 3.6853 -1.2402 0.0 0 +M V30 15 C -5.2917 1.3583 0.0 0 +M V30 16 C -5.2917 -1.2402 0.0 0 +M V30 17 O -4.5594 2.6694 0.0 0 +M V30 18 C -6.8036 1.3701 0.0 0 +M V30 19 C -6.8036 -1.2284 0.0 0 +M V30 20 C -3.0711 2.6813 0.0 0 +M V30 21 C -7.5596 0.0826 0.0 0 +M V30 22 O -7.5596 -2.5159 0.0 0 +M V30 23 C -9.0715 -2.5041 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 6 4 CFG=3 +M V30 6 1 5 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 7 12 +M V30 12 2 8 13 +M V30 13 1 8 14 +M V30 14 2 9 15 +M V30 15 1 9 16 +M V30 16 1 15 17 +M V30 17 1 15 18 +M V30 18 2 16 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 1 19 22 +M V30 22 1 22 23 +M V30 23 1 19 21 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 6) +M V30 END COLLECTION +M V30 END CTAB +M END +> +761 + +> +Z434468634 + +> +324.332 + +> +-0.816 + +> +4 + +> +145.770 + +> +7 + +> +ATM + +> + + +> + + +$$$$ +Compound 762 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O -0.759 -1.2927 0.0 0 +M V30 3 O 0.7471 1.3164 0.0 0 +M V30 4 N 1.2927 -0.7353 0.0 0 +M V30 5 C -1.3164 0.7708 0.0 0 +M V30 6 C 2.5854 0.0237 0.0 0 +M V30 7 C 1.2808 -2.2296 0.0 0 +M V30 8 C 3.8781 -0.7115 0.0 0 +M V30 9 C 2.5735 -2.9649 0.0 0 +M V30 10 C 3.8662 -2.2059 0.0 0 +M V30 11 N 5.159 -2.9412 0.0 0 +M V30 12 C 6.4517 -2.1821 0.0 0 +M V30 13 O 6.4398 -0.6641 0.0 0 +M V30 14 N 7.7444 -2.9175 0.0 0 +M V30 15 C 9.0371 -2.1584 0.0 0 +M V30 16 C 10.3298 -2.8937 0.0 0 +M V30 17 C 9.0252 -0.6404 0.0 0 +M V30 18 N 10.318 -4.3881 0.0 0 +M V30 19 C 11.6225 -2.1347 0.0 0 +M V30 20 C 10.318 0.1185 0.0 0 +M V30 21 N 9.0845 -5.2657 0.0 0 +M V30 22 C 11.5277 -5.2657 0.0 0 +M V30 23 C 11.6107 -0.6167 0.0 0 +M V30 24 C 9.5352 -6.6889 0.0 0 +M V30 25 C 11.0533 -6.6889 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 4 6 +M V30 6 1 4 7 +M V30 7 1 6 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 18 21 +M V30 21 1 18 22 +M V30 22 2 19 23 +M V30 23 2 21 24 +M V30 24 2 22 25 +M V30 25 1 9 10 +M V30 26 1 20 23 +M V30 27 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +762 + +> +Z434458558 + +> +363.435 + +> +0.855 + +> +2 + +> +96.330 + +> +3 + +> +ATM + +> + + +> + + +$$$$ +Compound 763 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.5033 0.0117 0.0 0 +M V30 3 C 0.4463 1.4329 0.0 0 +M V30 4 N -1.9731 1.4446 0.0 0 +M V30 5 C -0.7751 2.3255 0.0 0 +M V30 6 C -0.7869 3.8289 0.0 0 +M V30 7 O -2.0906 4.5805 0.0 0 +M V30 8 C -2.1023 6.0839 0.0 0 +M V30 9 C -3.406 6.8356 0.0 0 +M V30 10 C -0.8221 6.8356 0.0 0 +M V30 11 C -3.4178 8.339 0.0 0 +M V30 12 C -0.8339 8.339 0.0 0 +M V30 13 N -4.7215 9.0907 0.0 0 +M V30 14 C -2.1376 9.0907 0.0 0 +M V30 15 C -4.7332 10.594 0.0 0 +M V30 16 O -3.453 11.3457 0.0 0 +M V30 17 N -6.0369 11.3457 0.0 0 +M V30 18 C -6.0487 12.8491 0.0 0 +M V30 19 C -4.7685 13.6008 0.0 0 +M V30 20 C -7.3524 13.6008 0.0 0 +M V30 21 C -4.7802 15.1042 0.0 0 +M V30 22 C -7.3641 15.1042 0.0 0 +M V30 23 C -6.0839 15.8559 0.0 0 +M V30 24 O -6.0957 17.3592 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 2 3 5 +M V30 5 1 5 6 +M V30 6 1 6 7 +M V30 7 1 7 8 +M V30 8 2 8 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 1 11 13 +M V30 13 2 11 14 +M V30 14 1 13 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 17 18 +M V30 18 1 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 23 24 +M V30 24 1 4 5 +M V30 25 1 12 14 +M V30 26 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +763 + +> +Z1150579909 + +> +347.432 + +> +1.260 + +> +3 + +> +83.480 + +> +5 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 764 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4884 0.0 0 +M V30 3 C -1.3112 0.756 0.0 0 +M V30 4 C -1.323 -2.2208 0.0 0 +M V30 5 C 1.2757 -2.2208 0.0 0 +M V30 6 C -2.6224 0.0118 0.0 0 +M V30 7 C -1.3348 -3.7092 0.0 0 +M V30 8 C -2.6342 -1.4647 0.0 0 CFG=2 +M V30 9 C 1.2639 -3.7092 0.0 0 +M V30 10 C -0.0472 -4.4416 0.0 0 +M V30 11 N -3.9454 -2.1971 0.0 0 +M V30 12 C -5.2567 -1.4411 0.0 0 +M V30 13 O -5.2685 0.0708 0.0 0 +M V30 14 N -6.5679 -2.1735 0.0 0 +M V30 15 C -7.8791 -1.4175 0.0 0 +M V30 16 C -9.1903 -2.1499 0.0 0 +M V30 17 C -9.2021 -3.6383 0.0 0 +M V30 18 C -10.5015 -1.3939 0.0 0 +M V30 19 C -10.5134 -4.3707 0.0 0 +M V30 20 C -11.8128 -2.1263 0.0 0 +M V30 21 O -10.5252 -5.8591 0.0 0 +M V30 22 C -11.8246 -3.6147 0.0 0 +M V30 23 O -13.1358 -4.3471 0.0 0 +M V30 24 C -14.447 -3.591 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 2 7 10 +M V30 10 1 8 11 CFG=1 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 1 19 21 +M V30 21 2 19 22 +M V30 22 1 22 23 +M V30 23 1 23 24 +M V30 24 1 6 8 +M V30 25 1 9 10 +M V30 26 1 20 22 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 8) +M V30 END COLLECTION +M V30 END CTAB +M END +> +764 + +> +Z447605632 + +> +344.428 + +> +2.574 + +> +3 + +> +70.590 + +> +4 + +> +ATM + +> + + +> + + +$$$$ +Compound 765 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.5123 0.0 0 +M V30 3 N 1.276 2.2685 0.0 0 +M V30 4 C -1.3233 2.2685 0.0 0 +M V30 5 C 2.5639 1.5359 0.0 0 +M V30 6 C -2.6348 1.5359 0.0 0 +M V30 7 C -1.3351 3.7808 0.0 0 +M V30 8 C 3.8517 2.2921 0.0 0 +M V30 9 C -3.9463 2.2921 0.0 0 +M V30 10 C -2.6466 4.5488 0.0 0 +M V30 11 C 5.1396 1.5596 0.0 0 +M V30 12 C -3.9581 3.8045 0.0 0 +M V30 13 N 6.4275 2.3157 0.0 0 +M V30 14 C 7.7153 1.5832 0.0 0 +M V30 15 C 6.4157 3.8281 0.0 0 +M V30 16 C 9.0032 2.3394 0.0 0 +M V30 17 C 7.7035 4.5961 0.0 0 +M V30 18 C 8.9914 3.8517 0.0 0 +M V30 19 C 10.2911 1.6068 0.0 0 +M V30 20 C 10.2793 4.6197 0.0 0 +M V30 21 C 11.5789 2.363 0.0 0 +M V30 22 C 11.5671 3.8754 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 11 13 +M V30 13 1 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 2 20 22 +M V30 22 1 10 12 +M V30 23 1 17 18 +M V30 24 1 21 22 +M V30 END BOND +M V30 END CTAB +M END +> +765 + +> +Z364385042 + +> +294.391 + +> +3.339 + +> +1 + +> +32.340 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1688212 + +> +0.87 + +$$$$ +Compound 766 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 29 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O 0.7336 -1.2897 0.0 0 +M V30 3 O -0.7691 1.3134 0.0 0 +M V30 4 C -1.3134 -0.7454 0.0 0 +M V30 5 C 1.2897 0.7572 0.0 0 +M V30 6 C -2.6268 0.0236 0.0 0 +M V30 7 C 2.5795 0.0236 0.0 0 +M V30 8 C -3.9403 -0.7218 0.0 0 +M V30 9 C -2.6387 1.5382 0.0 0 +M V30 10 C 3.8693 0.7809 0.0 0 +M V30 11 C -5.2537 0.0473 0.0 0 +M V30 12 C -3.9521 2.2955 0.0 0 +M V30 13 N 5.1591 0.0473 0.0 0 +M V30 14 C -5.2655 1.5619 0.0 0 +M V30 15 C 6.4488 0.8046 0.0 0 +M V30 16 O 6.437 2.3192 0.0 0 +M V30 17 N 7.7386 0.0709 0.0 0 +M V30 18 C 9.0284 0.8282 0.0 0 +M V30 19 C 10.3182 0.0946 0.0 0 +M V30 20 C 9.0165 2.3428 0.0 0 +M V30 21 C 11.6079 0.8519 0.0 0 +M V30 22 C 10.3063 3.1001 0.0 0 +M V30 23 N 12.8977 0.1183 0.0 0 +M V30 24 C 11.5961 2.3665 0.0 0 +M V30 25 C 13.0397 -1.3607 0.0 0 +M V30 26 C 14.2585 0.7454 0.0 0 +M V30 27 O 11.9156 -2.3547 0.0 0 +M V30 28 N 14.4951 -1.6565 0.0 0 +M V30 29 C 15.2524 -0.3549 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 10 13 +M V30 13 2 11 14 +M V30 14 1 13 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 17 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 1 21 23 +M V30 23 2 21 24 +M V30 24 1 23 25 +M V30 25 1 23 26 +M V30 26 2 25 27 +M V30 27 1 25 28 +M V30 28 1 26 29 +M V30 29 1 12 14 +M V30 30 1 22 24 +M V30 31 1 28 29 +M V30 END BOND +M V30 END CTAB +M END +> +766 + +> +Z433451902 + +> +416.494 + +> +0.647 + +> +3 + +> +107.610 + +> +8 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 767 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.1645 1.4931 0.0 0 +M V30 3 C 1.4461 -0.2939 0.0 0 +M V30 4 C 1.1874 2.1162 0.0 0 +M V30 5 C -1.4696 2.2456 0.0 0 +M V30 6 C 2.1868 1.0111 0.0 0 +M V30 7 C -2.7746 1.5049 0.0 0 +M V30 8 N -4.0797 2.2573 0.0 0 +M V30 9 C -5.3847 1.5166 0.0 0 +M V30 10 O -5.3965 0.0352 0.0 0 +M V30 11 N -6.6897 2.2691 0.0 0 +M V30 12 C -7.9948 1.5284 0.0 0 CFG=2 +M V30 13 C -9.2998 2.2808 0.0 0 +M V30 14 C -8.0065 0.047 0.0 0 +M V30 15 C -9.3116 3.7857 0.0 0 +M V30 16 C -10.6049 1.5401 0.0 0 +M V30 17 C -10.6166 4.5382 0.0 0 +M V30 18 C -11.9099 2.2926 0.0 0 +M V30 19 N -10.6284 6.0431 0.0 0 +M V30 20 C -11.9216 3.8093 0.0 0 +M V30 21 C -11.9334 6.7956 0.0 0 +M V30 22 O -13.2267 4.5617 0.0 0 +M V30 23 O -11.9452 8.3005 0.0 0 +M V30 24 C -13.2384 6.0666 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 1 12 11 CFG=1 +M V30 12 1 12 13 +M V30 13 1 12 14 +M V30 14 2 13 15 +M V30 15 1 13 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 17 19 +M V30 19 2 17 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 2 21 23 +M V30 23 1 21 24 +M V30 24 1 4 6 +M V30 25 1 18 20 +M V30 26 1 22 24 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 12) +M V30 END COLLECTION +M V30 END CTAB +M END +> +767 + +> +Z433896226 + +> +345.416 + +> +2.002 + +> +3 + +> +79.460 + +> +5 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 768 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.497 0.0 0 +M V30 3 N 1.2831 -2.2336 0.0 0 +M V30 4 C -1.3306 -2.2336 0.0 0 +M V30 5 C 1.2712 -3.7306 0.0 0 +M V30 6 O -1.3425 -3.7306 0.0 0 +M V30 7 C -0.0475 -4.4672 0.0 0 +M V30 8 C 2.5662 -4.4672 0.0 0 +M V30 9 C -0.0594 -5.9642 0.0 0 +M V30 10 C 2.5544 -5.9642 0.0 0 +M V30 11 C 1.2356 -6.7127 0.0 0 +M V30 12 C 3.8494 -6.7127 0.0 0 CFG=1 +M V30 13 N 5.1444 -5.9523 0.0 0 +M V30 14 C 3.8375 -8.2097 0.0 0 +M V30 15 C 6.4395 -6.7008 0.0 0 +M V30 16 O 6.4276 -8.1978 0.0 0 +M V30 17 N 7.7345 -5.9405 0.0 0 +M V30 18 C 9.0295 -6.689 0.0 0 +M V30 19 C 10.3245 -5.9286 0.0 0 +M V30 20 N 11.6909 -6.5345 0.0 0 +M V30 21 C 10.4671 -4.4197 0.0 0 +M V30 22 N 12.6889 -5.4058 0.0 0 +M V30 23 C 11.9285 -4.0989 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 7 9 +M V30 9 2 8 10 +M V30 10 2 9 11 +M V30 11 1 10 12 +M V30 12 1 12 13 CFG=1 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 17 18 +M V30 18 1 18 19 +M V30 19 2 19 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 2 21 23 +M V30 23 1 6 7 +M V30 24 1 10 11 +M V30 25 1 22 23 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 12) +M V30 END COLLECTION +M V30 END CTAB +M END +> +768 + +> +Z535626280 + +> +315.327 + +> +0.026 + +> +4 + +> +108.140 + +> +4 + +> +parp1 + +> + + +> + + +$$$$ +Compound 769 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4965 0.0 0 +M V30 3 N 1.2827 -2.2329 0.0 0 +M V30 4 C -1.3302 -2.2329 0.0 0 +M V30 5 C 1.2708 -3.7295 0.0 0 +M V30 6 O -1.3421 -3.7295 0.0 0 +M V30 7 C -0.0475 -4.4659 0.0 0 +M V30 8 C 2.5655 -4.4659 0.0 0 +M V30 9 C -0.0593 -5.9624 0.0 0 +M V30 10 C 2.5536 -5.9624 0.0 0 +M V30 11 C 1.2352 -6.7107 0.0 0 +M V30 12 C 3.8482 -6.7107 0.0 0 CFG=1 +M V30 13 N 5.1429 -5.9506 0.0 0 +M V30 14 C 3.8364 -8.2073 0.0 0 +M V30 15 C 6.4375 -6.6988 0.0 0 +M V30 16 O 6.4257 -8.1954 0.0 0 +M V30 17 N 7.7322 -5.9387 0.0 0 +M V30 18 C 9.0268 -6.687 0.0 0 +M V30 19 C 10.3215 -5.9268 0.0 0 +M V30 20 C 11.6161 -6.6751 0.0 0 +M V30 21 C 10.3096 -4.4065 0.0 0 +M V30 22 N 12.9107 -5.9149 0.0 0 +M V30 23 C 11.6042 -3.6463 0.0 0 +M V30 24 C 12.8989 -4.3946 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 7 9 +M V30 9 2 8 10 +M V30 10 2 9 11 +M V30 11 1 10 12 +M V30 12 1 12 13 CFG=1 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 17 18 +M V30 18 1 18 19 +M V30 19 2 19 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 2 21 23 +M V30 23 2 22 24 +M V30 24 1 6 7 +M V30 25 1 10 11 +M V30 26 1 23 24 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 12) +M V30 END COLLECTION +M V30 END CTAB +M END +> +769 + +> +Z432469376 + +> +326.350 + +> +0.430 + +> +3 + +> +92.350 + +> +4 + +> +Tankyrase1, parp2 + +> + + +> + + +$$$$ +Compound 770 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.4161 -0.4484 0.0 0 +M V30 3 C -0.3186 1.4751 0.0 0 CFG=2 +M V30 4 N 1.8646 -1.8646 0.0 0 +M V30 5 N 2.6199 0.4484 0.0 0 +M V30 6 C -1.7584 1.9472 0.0 0 +M V30 7 C 0.7789 2.4901 0.0 0 +M V30 8 N 3.3516 -1.8528 0.0 0 +M V30 9 C 3.8237 -0.4248 0.0 0 +M V30 10 C 2.6081 1.959 0.0 0 +M V30 11 O -2.8795 0.9559 0.0 0 +M V30 12 N -2.077 3.4224 0.0 0 +M V30 13 O 5.2398 0.0472 0.0 0 +M V30 14 C 3.8945 2.7143 0.0 0 +M V30 15 C -1.0857 4.5435 0.0 0 +M V30 16 C -3.4578 4.0479 0.0 0 +M V30 17 C 3.8827 4.2249 0.0 0 +M V30 18 O 0.3894 4.4019 0.0 0 +M V30 19 N -1.841 5.8535 0.0 0 +M V30 20 C -3.3162 5.5467 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 3 1 CFG=3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 3 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 1 5 10 +M V30 10 2 6 11 +M V30 11 1 6 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 1 12 15 +M V30 15 1 12 16 +M V30 16 1 14 17 +M V30 17 2 15 18 +M V30 18 1 15 19 +M V30 19 1 16 20 +M V30 20 2 8 9 +M V30 21 1 19 20 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 3) +M V30 END COLLECTION +M V30 END CTAB +M END +> +770 + +> +Z112529980 + +> +299.349 + +> +0.506 + +> +2 + +> +100.350 + +> +5 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 771 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4961 0.0 0 +M V30 3 N 1.2824 -2.2442 0.0 0 +M V30 4 C -1.3299 -2.2442 0.0 0 +M V30 5 C 1.2705 -3.7404 0.0 0 +M V30 6 C -1.3418 -3.7404 0.0 0 +M V30 7 C -2.6479 -1.4842 0.0 0 +M V30 8 C 2.5648 -4.4766 0.0 0 +M V30 9 C -0.0474 -4.4766 0.0 0 +M V30 10 C -2.6598 -4.4766 0.0 0 +M V30 11 C -3.966 -2.2323 0.0 0 +M V30 12 O 2.5529 -5.9728 0.0 0 +M V30 13 N 3.8591 -3.7166 0.0 0 +M V30 14 C -3.9779 -3.7166 0.0 0 +M V30 15 C 5.1534 -4.4528 0.0 0 +M V30 16 C 6.4477 -3.6929 0.0 0 +M V30 17 C 7.742 -4.4291 0.0 0 +M V30 18 C 9.1076 -3.7997 0.0 0 +M V30 19 C 7.8845 -5.9134 0.0 0 +M V30 20 C 10.105 -4.9041 0.0 0 +M V30 21 C 9.3451 -6.2102 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 6 9 +M V30 22 1 11 14 +M V30 23 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +771 + +> +Z318777002 + +> +284.353 + +> +2.735 + +> +2 + +> +58.200 + +> +4 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 772 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.4103 0.4701 0.0 0 +M V30 3 C -0.8932 1.2223 0.0 0 +M V30 4 N 2.6914 -0.2585 0.0 0 +M V30 5 C 1.3986 1.9745 0.0 0 +M V30 6 C -0.0235 2.4446 0.0 0 +M V30 7 C 3.9725 0.4936 0.0 0 +M V30 8 C 2.6796 2.7267 0.0 0 +M V30 9 C -0.4936 3.8784 0.0 0 +M V30 10 N 3.9607 1.998 0.0 0 +M V30 11 N 2.6679 4.231 0.0 0 +M V30 12 C -1.9627 4.1958 0.0 0 +M V30 13 C 0.4936 4.995 0.0 0 +M V30 14 C 1.3633 4.9832 0.0 0 +M V30 15 C -2.4328 5.6296 0.0 0 +M V30 16 C 0.0235 6.4289 0.0 0 +M V30 17 C 1.3515 6.4876 0.0 0 CFG=1 +M V30 18 C -1.4456 6.7462 0.0 0 +M V30 19 C 2.6326 7.2516 0.0 0 +M V30 20 C 0.047 7.2516 0.0 0 +M V30 21 F -1.9157 8.1801 0.0 0 +M V30 22 O 2.6209 8.756 0.0 0 +M V30 23 C 0.0352 8.756 0.0 0 +M V30 24 O 1.234 9.6492 0.0 0 +M V30 25 C -1.187 9.6492 0.0 0 +M V30 26 C 0.7639 11.0831 0.0 0 +M V30 27 C -0.7404 11.0831 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 1 11 14 +M V30 14 1 12 15 +M V30 15 2 13 16 +M V30 16 1 17 14 CFG=3 +M V30 17 2 15 18 +M V30 18 1 17 19 +M V30 19 1 17 20 +M V30 20 1 18 21 +M V30 21 1 19 22 +M V30 22 1 20 23 +M V30 23 1 23 24 +M V30 24 2 23 25 +M V30 25 1 24 26 +M V30 26 1 25 27 +M V30 27 1 5 6 +M V30 28 1 8 10 +M V30 29 1 16 18 +M V30 30 2 26 27 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 17) +M V30 END COLLECTION +M V30 END CTAB +M END +> +772 + +> +Z285892750 + +> +383.439 + +> +4.347 + +> +2 + +> +71.180 + +> +7 + +> +ATM + +> + + +> + + +$$$$ +Compound 773 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5081 0.0 0 +M V30 3 N -1.3195 2.2739 0.0 0 +M V30 4 C 1.2724 2.2739 0.0 0 +M V30 5 C -1.3313 3.782 0.0 0 +M V30 6 C 1.2606 3.782 0.0 0 +M V30 7 C 2.5567 1.5316 0.0 0 +M V30 8 C -2.6391 4.536 0.0 0 +M V30 9 C -0.0471 4.536 0.0 0 +M V30 10 C 2.5449 4.536 0.0 0 +M V30 11 C 3.8409 2.2975 0.0 0 +M V30 12 O -2.6509 6.0441 0.0 0 +M V30 13 N -3.9469 3.8056 0.0 0 +M V30 14 C 3.8291 3.8056 0.0 0 +M V30 15 C -5.2547 4.5596 0.0 0 +M V30 16 C -6.5626 3.8291 0.0 0 +M V30 17 O -7.9411 4.4536 0.0 0 +M V30 18 C -6.7275 2.3564 0.0 0 +M V30 19 C -8.9543 3.3578 0.0 0 +M V30 20 C -8.2003 2.0618 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 2 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 6 9 +M V30 21 1 11 14 +M V30 22 2 19 20 +M V30 END BOND +M V30 END CTAB +M END +> +773 + +> +Z79377362 + +> +268.267 + +> +1.259 + +> +2 + +> +71.340 + +> +3 + +> +parp15, parp10 + +> + + +> + + +$$$$ +Compound 774 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.6278 1.386 0.0 0 +M V30 3 N -2.1086 1.7058 0.0 0 +M V30 4 C 0.1066 2.7009 0.0 0 +M V30 5 C -2.2744 3.2103 0.0 0 +M V30 6 C -3.234 0.7107 0.0 0 +M V30 7 C -0.9121 3.8263 0.0 0 +M V30 8 C -2.9378 -0.7463 0.0 0 +M V30 9 C -1.5163 -1.1964 0.0 0 +M V30 10 C -4.0632 -1.7414 0.0 0 +M V30 11 C -1.2201 -2.6535 0.0 0 +M V30 12 C -3.7671 -3.1984 0.0 0 +M V30 13 N 0.2013 -3.1037 0.0 0 +M V30 14 C -2.3455 -3.6486 0.0 0 +M V30 15 C 0.4975 -4.5608 0.0 0 +M V30 16 O -0.6278 -5.5559 0.0 0 +M V30 17 C 1.919 -5.0109 0.0 0 +M V30 18 N 3.1274 -4.1106 0.0 0 +M V30 19 C 2.3692 -6.4325 0.0 0 +M V30 20 C 4.3357 -4.9872 0.0 0 +M V30 21 C 3.1155 -2.5943 0.0 0 +M V30 22 C 3.8618 -6.4206 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 6 8 +M V30 8 2 8 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 1 11 13 +M V30 13 2 11 14 +M V30 14 1 13 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 17 18 +M V30 18 2 17 19 +M V30 19 1 18 20 +M V30 20 1 18 21 +M V30 21 1 19 22 +M V30 22 1 5 7 +M V30 23 1 12 14 +M V30 24 2 20 22 +M V30 END BOND +M V30 END CTAB +M END +> +774 + +> +Z356152936 + +> +297.352 + +> +1.241 + +> +1 + +> +54.340 + +> +4 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL539474 + +> +0.88 + +$$$$ +Compound 775 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.1648 1.4953 0.0 0 +M V30 3 C 1.4482 -0.2943 0.0 0 +M V30 4 C 1.1892 2.1194 0.0 0 +M V30 5 C -1.4718 2.2489 0.0 0 +M V30 6 C 2.19 1.0126 0.0 0 +M V30 7 C -2.7788 1.5189 0.0 0 +M V30 8 N -4.0857 2.2725 0.0 0 +M V30 9 C -5.3927 1.5424 0.0 0 +M V30 10 O -5.4045 0.0588 0.0 0 +M V30 11 C -6.6997 2.296 0.0 0 +M V30 12 N -6.7115 3.8032 0.0 0 +M V30 13 C -8.0067 1.566 0.0 0 +M V30 14 C -8.0185 4.5567 0.0 0 +M V30 15 C -9.3137 2.3196 0.0 0 +M V30 16 O -8.0303 6.0639 0.0 0 +M V30 17 C -9.3255 3.8149 0.0 0 +M V30 18 C -10.6207 1.5895 0.0 0 +M V30 19 C -10.6324 4.5685 0.0 0 +M V30 20 C -11.9277 2.3431 0.0 0 +M V30 21 C -11.9394 3.8267 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 1 11 12 +M V30 12 2 11 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 2 19 21 +M V30 21 1 4 6 +M V30 22 2 15 17 +M V30 23 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +775 + +> +Z70995976 + +> +298.360 + +> +1.858 + +> +2 + +> +58.200 + +> +4 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 776 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 15 16 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5091 0.0 0 +M V30 3 N 1.2733 2.2754 0.0 0 +M V30 4 N -1.3204 2.2754 0.0 0 +M V30 5 C 2.5584 1.5327 0.0 0 +M V30 6 C -2.6292 1.5327 0.0 0 +M V30 7 C 2.5466 0.0471 0.0 0 +M V30 8 C 3.8435 2.299 0.0 0 +M V30 9 C -3.9379 2.299 0.0 0 +M V30 10 N 3.8317 -0.6838 0.0 0 +M V30 11 C 5.1287 1.5563 0.0 0 +M V30 12 C -5.3998 1.9217 0.0 0 +M V30 13 C -4.3387 3.761 0.0 0 +M V30 14 C 5.1169 0.0707 0.0 0 +M V30 15 C -5.8007 3.3837 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 9 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 12 15 +M V30 15 1 11 14 +M V30 16 1 13 15 +M V30 END BOND +M V30 END CTAB +M END +> +776 + +> +Z381531318 + +> +205.256 + +> +2.108 + +> +2 + +> +54.020 + +> +3 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1998193 + +> +0.86 + +$$$$ +Compound 777 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4925 0.0 0 +M V30 3 N -1.3267 -2.227 0.0 0 +M V30 4 N 1.2793 -2.227 0.0 0 +M V30 5 C -2.6416 -1.4689 0.0 0 +M V30 6 C -1.3385 -3.7196 0.0 0 +M V30 7 C 2.5705 -1.4689 0.0 0 +M V30 8 C -3.9565 -2.2033 0.0 0 +M V30 9 C -2.6535 -4.4659 0.0 0 +M V30 10 C 3.8617 -2.2033 0.0 0 +M V30 11 N -3.9684 -3.6959 0.0 0 +M V30 12 N 5.153 -1.4452 0.0 0 +M V30 13 C -5.2833 -4.4422 0.0 0 +M V30 14 C 6.4442 -2.1796 0.0 0 +M V30 15 C -5.2951 -5.9348 0.0 0 +M V30 16 C -6.5982 -3.6722 0.0 0 +M V30 17 N 7.7354 -1.4215 0.0 0 +M V30 18 N 6.4323 -3.6722 0.0 0 +M V30 19 O -4.0039 -6.6693 0.0 0 +M V30 20 C -6.61 -6.6693 0.0 0 +M V30 21 C -7.9131 -4.4185 0.0 0 +M V30 22 C 9.0266 -2.1559 0.0 0 +M V30 23 C 7.7235 -4.4185 0.0 0 +M V30 24 C -7.9249 -5.9111 0.0 0 +M V30 25 C 9.0148 -3.6485 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 2 13 15 +M V30 15 1 13 16 +M V30 16 2 14 17 +M V30 17 1 14 18 +M V30 18 1 15 19 +M V30 19 1 15 20 +M V30 20 2 16 21 +M V30 21 1 17 22 +M V30 22 2 18 23 +M V30 23 2 20 24 +M V30 24 2 22 25 +M V30 25 1 9 11 +M V30 26 1 21 24 +M V30 27 1 23 25 +M V30 END BOND +M V30 END CTAB +M END +> +777 + +> +Z558082812 + +> +342.396 + +> +1.713 + +> +3 + +> +93.620 + +> +5 + +> +DNA_pk + +> + + +> + + +$$$$ +Compound 778 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2947 -0.7483 0.0 0 +M V30 3 N 2.5894 0.0237 0.0 0 +M V30 4 C 1.2828 -2.2449 0.0 0 +M V30 5 C 3.8841 -0.7245 0.0 0 +M V30 6 C 2.5775 -2.9814 0.0 0 +M V30 7 C -0.0356 -2.9814 0.0 0 +M V30 8 N 3.8723 -2.2212 0.0 0 +M V30 9 C 5.1789 0.0475 0.0 0 +M V30 10 C 2.5657 -4.478 0.0 0 +M V30 11 C -0.0475 -4.478 0.0 0 +M V30 12 N 6.4736 -0.7008 0.0 0 +M V30 13 C 1.2472 -5.2264 0.0 0 +M V30 14 C 7.7683 0.0712 0.0 0 +M V30 15 C 6.4617 -2.1974 0.0 0 +M V30 16 O 7.7564 1.5916 0.0 0 +M V30 17 N 9.0631 -0.677 0.0 0 +M V30 18 C 7.7564 -2.9339 0.0 0 +M V30 19 C 10.3578 0.095 0.0 0 +M V30 20 C 11.6525 -0.6533 0.0 0 CFG=2 +M V30 21 C 11.795 -2.138 0.0 0 +M V30 22 C 13.0185 -0.0237 0.0 0 +M V30 23 O 13.2561 -2.435 0.0 0 +M V30 24 C 14.0163 -1.1284 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 1 12 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 17 19 +M V30 19 1 20 19 CFG=1 +M V30 20 1 20 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 6 8 +M V30 25 1 11 13 +M V30 26 1 23 24 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 20) +M V30 END COLLECTION +M V30 END CTAB +M END +> +778 + +> +Z558220948 + +> +330.382 + +> +-0.530 + +> +2 + +> +83.030 + +> +5 + +> +parp2 + +> + + +> + + +$$$$ +Compound 779 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.4966 0.1649 0.0 0 +M V30 3 N -2.1213 1.5438 0.0 0 +M V30 4 C -2.3923 -1.037 0.0 0 +M V30 5 C -1.2492 2.7694 0.0 0 +M V30 6 C -3.6179 1.7088 0.0 0 +M V30 7 N -3.9008 -1.0252 0.0 0 +M V30 8 C -1.9445 -2.4512 0.0 0 +M V30 9 C -1.8738 4.1483 0.0 0 CFG=2 +M V30 10 C -4.2426 3.0876 0.0 0 +M V30 11 N -4.3722 -2.4394 0.0 0 +M V30 12 C -3.1701 -3.3233 0.0 0 +M V30 13 N -3.3705 4.3133 0.0 0 +M V30 14 C -1.0017 5.3739 0.0 0 +M V30 15 C -3.1819 -4.8082 0.0 0 +M V30 16 C -1.6263 6.7528 0.0 0 +M V30 17 C 0.4714 5.2325 0.0 0 +M V30 18 C -2.4512 -6.0928 0.0 0 +M V30 19 C -3.9479 -6.0928 0.0 0 +M V30 20 C -0.7542 7.9784 0.0 0 +M V30 21 C 1.3434 6.4581 0.0 0 +M V30 22 N 0.7188 7.837 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 9 13 +M V30 13 1 9 14 CFG=3 +M V30 14 1 12 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 15 19 +M V30 19 1 16 20 +M V30 20 2 17 21 +M V30 21 2 20 22 +M V30 22 1 10 13 +M V30 23 1 11 12 +M V30 24 1 18 19 +M V30 25 1 21 22 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 9) +M V30 END COLLECTION +M V30 END CTAB +M END +> +779 + +> +Z2003988664 + +> +297.355 + +> +0.269 + +> +2 + +> +73.910 + +> +3 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 780 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.8948 1.2245 0.0 0 +M V30 3 C 1.4129 0.4709 0.0 0 +M V30 4 N -0.0235 2.4491 0.0 0 +M V30 5 N -2.402 1.2363 0.0 0 +M V30 6 C 1.4011 1.9781 0.0 0 +M V30 7 C 2.5668 -0.4474 0.0 0 +M V30 8 C -3.1555 2.5433 0.0 0 +M V30 9 C 2.555 2.9201 0.0 0 +M V30 10 C 4.0151 -0.1059 0.0 0 +M V30 11 O -2.4255 3.8503 0.0 0 +M V30 12 C -4.6627 2.555 0.0 0 +M V30 13 C 4.0033 2.6021 0.0 0 +M V30 14 C 4.6509 1.2481 0.0 0 +M V30 15 C -5.4163 3.862 0.0 0 +M V30 16 C -6.9234 3.8738 0.0 0 +M V30 17 C -7.677 5.1808 0.0 0 +M V30 18 C -7.677 2.5904 0.0 0 +M V30 19 C -9.1842 5.1926 0.0 0 +M V30 20 C -9.1842 2.6021 0.0 0 +M V30 21 O -9.9377 6.4995 0.0 0 +M V30 22 C -9.9377 3.9091 0.0 0 +M V30 23 O -9.9377 1.3187 0.0 0 +M V30 24 C -11.4449 6.5113 0.0 0 +M V30 25 O -11.4449 3.9209 0.0 0 +M V30 26 C -11.4449 1.3305 0.0 0 +M V30 27 C -12.1985 2.6375 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 2 12 15 CFG=2 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 1 19 21 +M V30 21 2 19 22 +M V30 22 1 20 23 +M V30 23 1 21 24 +M V30 24 1 22 25 +M V30 25 1 23 26 +M V30 26 1 25 27 +M V30 27 1 4 6 +M V30 28 1 13 14 +M V30 29 1 20 22 +M V30 END BOND +M V30 END CTAB +M END +> +780 + +> +Z228295256 + +> +388.481 + +> +4.332 + +> +1 + +> +69.680 + +> +6 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL516766 + +> +0.96 + +$$$$ +Compound 781 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4918 0.0 0 +M V30 3 N 1.2787 -2.2259 0.0 0 +M V30 4 C -1.326 -2.2259 0.0 0 +M V30 5 C 1.2668 -3.7177 0.0 0 +M V30 6 C 2.5692 -1.4681 0.0 0 +M V30 7 C -1.3379 -3.7177 0.0 0 +M V30 8 N -0.0473 -4.4636 0.0 0 +M V30 9 C 2.5574 -4.4636 0.0 0 +M V30 10 C 3.8598 -2.2022 0.0 0 +M V30 11 N -2.6521 -4.4636 0.0 0 +M V30 12 C 3.8479 -3.7059 0.0 0 +M V30 13 C -3.9663 -3.7059 0.0 0 +M V30 14 C -2.6639 -5.9555 0.0 0 +M V30 15 C -5.2806 -4.4518 0.0 0 +M V30 16 C -3.9782 -6.7014 0.0 0 +M V30 17 O -5.2924 -5.9436 0.0 0 +M V30 18 C -6.5948 -3.694 0.0 0 +M V30 19 C -3.99 -8.1932 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 7 11 +M V30 11 2 9 12 +M V30 12 1 11 13 +M V30 13 1 11 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 1 7 8 +M V30 20 1 10 12 +M V30 21 1 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +781 + +> +Z203890048 + +> +259.304 + +> +1.384 + +> +0 + +> +45.140 + +> +1 + +> +DNA-dependent protein kinase + +> +CHEMBL179043 + +> +0.93 + +$$$$ +Compound 782 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4921 0.0 0 +M V30 3 N 1.2789 -2.2263 0.0 0 +M V30 4 C -1.3263 -2.2263 0.0 0 +M V30 5 C 1.2671 -3.7184 0.0 0 +M V30 6 C 2.5697 -1.4684 0.0 0 +M V30 7 C -1.3381 -3.7184 0.0 0 +M V30 8 N -0.0473 -4.4645 0.0 0 +M V30 9 C 2.5579 -4.4645 0.0 0 +M V30 10 C 3.8605 -2.2026 0.0 0 +M V30 11 N -2.6526 -4.4645 0.0 0 +M V30 12 C 3.8487 -3.7066 0.0 0 +M V30 13 C -3.9671 -3.7066 0.0 0 +M V30 14 C -2.6644 -5.9566 0.0 0 +M V30 15 C -5.2816 -4.4526 0.0 0 +M V30 16 C -3.9789 -6.7026 0.0 0 +M V30 17 N -5.2934 -5.9447 0.0 0 +M V30 18 C -6.6079 -6.6908 0.0 0 +M V30 19 C -6.6197 -8.1829 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 7 11 +M V30 11 2 9 12 +M V30 12 1 11 13 +M V30 13 1 11 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 17 18 +M V30 18 1 18 19 +M V30 19 1 7 8 +M V30 20 1 10 12 +M V30 21 1 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +782 + +> +Z56842181 + +> +258.319 + +> +1.436 + +> +0 + +> +39.150 + +> +2 + +> +DNA-dependent protein kinase + +> +CHEMBL179043 + +> +0.85 + +$$$$ +Compound 783 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 34 38 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O -0.7547 1.309 0.0 0 +M V30 3 O 0.7429 -1.2854 0.0 0 +M V30 4 N 1.2854 0.7547 0.0 0 +M V30 5 C -1.309 -0.7429 0.0 0 +M V30 6 C 2.5708 0.0235 0.0 0 +M V30 7 C 1.2736 2.2642 0.0 0 +M V30 8 C -2.618 0.0235 0.0 0 +M V30 9 C -1.3208 -2.2288 0.0 0 +M V30 10 C 3.8562 0.7783 0.0 0 +M V30 11 C 2.559 3.0189 0.0 0 +M V30 12 C -3.927 -0.7193 0.0 0 +M V30 13 C -2.6298 -2.96 0.0 0 +M V30 14 N 3.8444 2.2878 0.0 0 +M V30 15 N -5.3657 -0.2476 0.0 0 +M V30 16 C -3.9388 -2.2052 0.0 0 +M V30 17 C 5.1299 3.0425 0.0 0 +M V30 18 C -6.262 -1.4505 0.0 0 +M V30 19 N -5.3775 -2.6534 0.0 0 +M V30 20 O 6.4153 2.3114 0.0 0 +M V30 21 C 5.1181 4.552 0.0 0 +M V30 22 O -7.7715 -1.4387 0.0 0 +M V30 23 O 6.4035 5.3068 0.0 0 +M V30 24 C 6.3917 6.8163 0.0 0 +M V30 25 C 7.6772 7.571 0.0 0 +M V30 26 C 5.0827 7.571 0.0 0 +M V30 27 C 7.6654 9.0805 0.0 0 +M V30 28 C 5.0709 9.0805 0.0 0 +M V30 29 C 6.3563 9.8353 0.0 0 +M V30 30 C 8.9508 9.8353 0.0 0 +M V30 31 N 6.3445 11.3448 0.0 0 +M V30 32 C 8.939 11.3448 0.0 0 +M V30 33 C 7.63 12.0995 0.0 0 +M V30 34 O 7.6182 13.609 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 1 10 14 +M V30 14 1 12 15 +M V30 15 2 12 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 17 21 +M V30 21 2 18 22 +M V30 22 1 21 23 +M V30 23 1 23 24 +M V30 24 2 24 25 +M V30 25 1 24 26 +M V30 26 1 25 27 +M V30 27 2 26 28 +M V30 28 2 27 29 +M V30 29 1 27 30 +M V30 30 1 29 31 +M V30 31 1 30 32 +M V30 32 1 31 33 +M V30 33 2 33 34 +M V30 34 1 11 14 +M V30 35 1 13 16 +M V30 36 1 18 19 +M V30 37 1 28 29 +M V30 38 1 32 33 +M V30 END BOND +M V30 END CTAB +M END +> +783 + +> +Z273256524 + +> +485.513 + +> +1.967 + +> +3 + +> +137.150 + +> +4 + +> +parp10 + +> + + +> + + +$$$$ +Compound 784 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.486 -0.267 0.0 0 +M V30 3 C -1.9852 -1.6717 0.0 0 +M V30 4 C -2.4844 0.8823 0.0 0 +M V30 5 C -3.4712 -1.9387 0.0 0 +M V30 6 C -1.01 -2.7978 0.0 0 +M V30 7 C -3.9704 0.6153 0.0 0 +M V30 8 C -4.4696 -0.7894 0.0 0 +M V30 9 C 0.4527 -2.5076 0.0 0 CFG=1 +M V30 10 N 1.4279 -3.6337 0.0 0 +M V30 11 C 0.9287 -1.0796 0.0 0 +M V30 12 C 2.8907 -3.3435 0.0 0 +M V30 13 O 3.3667 -1.9155 0.0 0 +M V30 14 N 3.8659 -4.4696 0.0 0 +M V30 15 C 5.3287 -4.1794 0.0 0 +M V30 16 C 6.3039 -5.3055 0.0 0 +M V30 17 C 5.8047 -2.7514 0.0 0 +M V30 18 O 7.7667 -5.0152 0.0 0 +M V30 19 N 5.8047 -6.7102 0.0 0 +M V30 20 O 4.8063 -1.6021 0.0 0 +M V30 21 N 7.2675 -2.4612 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 1 9 10 CFG=3 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 17 21 +M V30 21 1 7 8 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 9) +M V30 END COLLECTION +M V30 END CTAB +M END +> +784 + +> +Z435724988 + +> +312.752 + +> +0.346 + +> +4 + +> +127.310 + +> +6 + +> +ATM + +> + + +> + + +$$$$ +Compound 785 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -1.3138 0.7693 0.0 0 +M V30 3 C -1.3256 2.2844 0.0 0 +M V30 4 C -2.6276 0.0236 0.0 0 +M V30 5 C -2.6395 3.0419 0.0 0 +M V30 6 C -0.0355 3.0419 0.0 0 +M V30 7 C -3.9415 0.793 0.0 0 +M V30 8 F -2.6513 4.557 0.0 0 +M V30 9 C -3.9533 2.3081 0.0 0 +M V30 10 N 1.2546 2.3081 0.0 0 +M V30 11 C 2.5448 3.0656 0.0 0 +M V30 12 C 1.2428 0.8167 0.0 0 +M V30 13 O 2.533 4.5807 0.0 0 +M V30 14 C 3.835 2.3317 0.0 0 +M V30 15 C 2.533 0.071 0.0 0 +M V30 16 O 5.1251 3.0893 0.0 0 +M V30 17 C 6.4153 2.3554 0.0 0 +M V30 18 C 7.7055 3.1129 0.0 0 +M V30 19 C 6.4035 0.864 0.0 0 +M V30 20 C 8.9957 2.3791 0.0 0 +M V30 21 C 7.6936 0.1183 0.0 0 +M V30 22 C 8.9838 0.8877 0.0 0 +M V30 23 C 10.274 0.142 0.0 0 +M V30 24 O 11.5642 0.9114 0.0 0 +M V30 25 N 10.2622 -1.3493 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 1 10 11 +M V30 11 1 10 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 12 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 2 20 22 +M V30 22 1 22 23 +M V30 23 2 23 24 +M V30 24 1 23 25 +M V30 25 1 7 9 +M V30 26 1 21 22 +M V30 END BOND +M V30 END CTAB +M END +> +785 + +> +Z997292602 + +> +348.344 + +> +2.546 + +> +1 + +> +72.630 + +> +7 + +> +parp14 + +> + + +> + + +$$$$ +Compound 786 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O -0.7582 -1.2913 0.0 0 +M V30 3 O 0.7463 1.315 0.0 0 +M V30 4 N -1.315 0.77 0.0 0 +M V30 5 C 1.2913 -0.7345 0.0 0 +M V30 6 C -2.6301 0.0236 0.0 0 +M V30 7 C -1.3269 2.2865 0.0 0 +M V30 8 C 2.5827 0.0236 0.0 0 +M V30 9 C 1.2795 -2.2273 0.0 0 +M V30 10 C 3.8741 -0.7108 0.0 0 +M V30 11 C 2.5708 -2.9618 0.0 0 +M V30 12 C 3.8622 -2.2036 0.0 0 +M V30 13 C 5.1536 -2.9381 0.0 0 +M V30 14 N 5.1417 -4.4309 0.0 0 +M V30 15 C 6.4331 -5.1773 0.0 0 +M V30 16 O 6.4213 -6.6701 0.0 0 +M V30 17 N 7.7245 -4.4072 0.0 0 +M V30 18 C 7.7126 -2.8907 0.0 0 +M V30 19 C 9.004 -2.1325 0.0 0 +M V30 20 C 6.3976 -2.1325 0.0 0 +M V30 21 N 10.2954 -2.867 0.0 0 +M V30 22 C 8.9922 -0.616 0.0 0 +M V30 23 C 6.3857 -0.616 0.0 0 +M V30 24 C 11.6578 -2.2391 0.0 0 +M V30 25 C 10.4376 -4.348 0.0 0 +M V30 26 C 7.6771 0.1421 0.0 0 +M V30 27 C 12.653 -3.3409 0.0 0 +M V30 28 C 11.8948 -4.6441 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 2 10 12 +M V30 12 1 12 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 17 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 19 22 +M V30 22 2 20 23 +M V30 23 1 21 24 +M V30 24 1 21 25 +M V30 25 2 22 26 +M V30 26 1 24 27 +M V30 27 1 25 28 +M V30 28 1 11 12 +M V30 29 1 23 26 +M V30 30 1 27 28 +M V30 END BOND +M V30 END CTAB +M END +> +786 + +> +Z432972080 + +> +402.510 + +> +2.343 + +> +2 + +> +81.750 + +> +5 + +> +ATM + +> + + +> + + +$$$$ +Compound 787 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3085 0.7544 0.0 0 CFG=2 +M V30 3 C -2.6171 0.0235 0.0 0 +M V30 4 C -1.3203 2.2634 0.0 0 +M V30 5 C -2.7821 -1.45 0.0 0 +M V30 6 C -3.9964 0.6483 0.0 0 +M V30 7 N -0.0353 3.0297 0.0 0 +M V30 8 C -4.2558 -1.7447 0.0 0 +M V30 9 C -5.0103 -0.4479 0.0 0 +M V30 10 C -0.0471 4.5387 0.0 0 +M V30 11 N -1.3557 5.2932 0.0 0 +M V30 12 C 1.2378 5.2932 0.0 0 +M V30 13 C -1.3675 6.8022 0.0 0 +M V30 14 C 1.226 6.8022 0.0 0 +M V30 15 C 2.5228 4.5505 0.0 0 +M V30 16 N -0.0825 7.5567 0.0 0 +M V30 17 N 2.511 7.5567 0.0 0 +M V30 18 C 3.8078 5.305 0.0 0 +M V30 19 C 3.796 6.8258 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 2 1 CFG=3 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 10 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 2 12 15 +M V30 15 2 13 16 +M V30 16 2 14 17 +M V30 17 1 15 18 +M V30 18 1 17 19 +M V30 19 1 8 9 +M V30 20 1 14 16 +M V30 21 2 18 19 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 2) +M V30 END COLLECTION +M V30 END CTAB +M END +> +787 + +> +Z1502780551 + +> +258.319 + +> +1.998 + +> +2 + +> +70.930 + +> +4 + +> +DNA_pk + +> + + +> + + +$$$$ +Compound 788 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5045 0.0 0 +M V30 3 N -1.3164 2.2567 0.0 0 +M V30 4 C 1.2694 2.2567 0.0 0 +M V30 5 C -1.3281 3.7612 0.0 0 +M V30 6 C 1.2576 3.7612 0.0 0 +M V30 7 C 2.5505 1.528 0.0 0 +M V30 8 N -0.047 4.5252 0.0 0 +M V30 9 C -2.6328 4.5252 0.0 0 +M V30 10 C 2.5388 4.5252 0.0 0 +M V30 11 C 3.8317 2.2802 0.0 0 +M V30 12 C -3.9375 3.7847 0.0 0 +M V30 13 C 3.82 3.7847 0.0 0 +M V30 14 C -5.2422 4.5487 0.0 0 +M V30 15 O -5.2539 6.0532 0.0 0 +M V30 16 N -6.5469 3.8082 0.0 0 +M V30 17 C -7.8516 4.5722 0.0 0 +M V30 18 C -6.5586 2.3272 0.0 0 +M V30 19 C -9.1562 3.8317 0.0 0 +M V30 20 C -10.4609 4.5957 0.0 0 +M V30 21 C -11.7656 3.8552 0.0 0 +M V30 22 C -10.4727 6.1002 0.0 0 +M V30 23 C -13.0703 4.6192 0.0 0 +M V30 24 C -11.7774 6.8525 0.0 0 +M V30 25 C -13.082 6.1237 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 1 20 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 1 6 8 +M V30 26 1 11 13 +M V30 27 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +788 + +> +Z1001544832 + +> +341.447 + +> +2.637 + +> +1 + +> +61.770 + +> +6 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3105400 + +> +0.9 + +$$$$ +Compound 789 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.2942 0.7718 0.0 0 +M V30 3 C -0.0118 -1.4961 0.0 0 +M V30 4 C 2.5885 0.0237 0.0 0 +M V30 5 C 1.2824 2.2917 0.0 0 +M V30 6 N 2.5767 -1.4724 0.0 0 CHG=1 +M V30 7 C 3.8828 0.7955 0.0 0 +M V30 8 C 2.5767 3.0516 0.0 0 +M V30 9 O 1.2586 -2.2086 0.0 0 +M V30 10 O 3.871 -2.2086 0.0 0 CHG=-1 +M V30 11 C 3.871 2.3154 0.0 0 +M V30 12 C 5.1653 3.0754 0.0 0 +M V30 13 O 5.1534 4.5953 0.0 0 +M V30 14 N 6.4595 2.3392 0.0 0 +M V30 15 C 7.7538 3.0991 0.0 0 +M V30 16 C 9.0481 2.3629 0.0 0 +M V30 17 O 9.0363 0.8668 0.0 0 +M V30 18 N 10.3424 3.1229 0.0 0 +M V30 19 C 11.6367 2.3867 0.0 0 +M V30 20 C 11.6248 0.8905 0.0 0 +M V30 21 C 12.931 3.1466 0.0 0 +M V30 22 N 12.9191 0.1424 0.0 0 +M V30 23 C 14.2253 2.4104 0.0 0 +M V30 24 C 14.2134 0.9143 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 18 19 +M V30 19 2 19 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 2 21 23 +M V30 23 2 22 24 +M V30 24 1 8 11 +M V30 25 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +789 + +> +Z31790767 + +> +346.361 + +> +1.985 + +> +2 + +> +114.230 + +> +6 + +> +ATM + +> + + +> + + +$$$$ +Compound 790 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 34 37 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O -0.7582 -1.2913 0.0 0 +M V30 3 O 0.7463 1.315 0.0 0 +M V30 4 N -1.315 0.7582 0.0 0 +M V30 5 C 1.2913 -0.7345 0.0 0 +M V30 6 C -2.6301 0.0236 0.0 0 +M V30 7 C 2.5827 0.0236 0.0 0 +M V30 8 C 1.2795 -2.2273 0.0 0 +M V30 9 C -3.9452 0.7819 0.0 0 +M V30 10 C -2.642 -1.469 0.0 0 +M V30 11 C 3.8741 -0.7108 0.0 0 +M V30 12 C 2.5709 -2.9618 0.0 0 +M V30 13 C -5.2603 0.0473 0.0 0 +M V30 14 C -3.957 -2.2154 0.0 0 +M V30 15 C 3.8623 -2.2036 0.0 0 +M V30 16 C -5.2721 -1.4454 0.0 0 +M V30 17 C 5.1536 -2.9381 0.0 0 +M V30 18 F -6.5872 -2.1917 0.0 0 +M V30 19 O 5.1418 -4.4309 0.0 0 +M V30 20 N 6.445 -2.1799 0.0 0 +M V30 21 C 7.7364 -2.9145 0.0 0 +M V30 22 C 9.0278 -2.1562 0.0 0 +M V30 23 C 10.3192 -2.8908 0.0 0 +M V30 24 C 9.0159 -0.6397 0.0 0 +M V30 25 C 11.6106 -2.1325 0.0 0 +M V30 26 C 10.3073 0.1184 0.0 0 +M V30 27 C 11.5987 -0.616 0.0 0 +M V30 28 C 12.9019 -2.8671 0.0 0 +M V30 29 N 12.8901 -4.3599 0.0 0 +M V30 30 C 11.658 -5.2366 0.0 0 +M V30 31 C 14.0986 -5.2366 0.0 0 +M V30 32 O 10.2126 -4.7627 0.0 0 +M V30 33 C 12.1082 -6.6583 0.0 0 +M V30 34 C 13.6247 -6.6583 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 2 11 15 +M V30 15 2 13 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 17 19 +M V30 19 1 17 20 +M V30 20 1 20 21 +M V30 21 1 21 22 +M V30 22 2 22 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 2 24 26 +M V30 26 2 25 27 +M V30 27 1 25 28 +M V30 28 1 28 29 +M V30 29 1 29 30 +M V30 30 1 29 31 +M V30 31 2 30 32 +M V30 32 1 30 33 +M V30 33 1 31 34 +M V30 34 1 12 15 +M V30 35 1 14 16 +M V30 36 1 26 27 +M V30 37 1 33 34 +M V30 END BOND +M V30 END CTAB +M END +> +790 + +> +Z384600138 + +> +481.539 + +> +2.975 + +> +2 + +> +95.580 + +> +7 + +> +parp2 + +> + + +> + + +$$$$ +Compound 791 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4736 1.4208 0.0 0 +M V30 3 N -0.5198 2.5644 0.0 0 +M V30 4 C 1.9291 1.7096 0.0 0 +M V30 5 C -0.0462 3.9852 0.0 0 +M V30 6 C 2.4027 3.1304 0.0 0 +M V30 7 C 2.8994 0.5891 0.0 0 +M V30 8 N 1.4092 4.274 0.0 0 +M V30 9 C -1.0396 5.1288 0.0 0 +M V30 10 C 3.8582 3.4192 0.0 0 +M V30 11 C 4.3549 0.8779 0.0 0 +M V30 12 C -2.5182 4.8631 0.0 0 +M V30 13 C 4.8285 2.2987 0.0 0 +M V30 14 C -3.5116 6.0067 0.0 0 +M V30 15 C -4.9902 5.7411 0.0 0 +M V30 16 O -5.4869 4.3433 0.0 0 +M V30 17 N -5.9836 6.8847 0.0 0 +M V30 18 C -7.4622 6.619 0.0 0 CFG=2 +M V30 19 C -8.4557 7.7626 0.0 0 +M V30 20 C -7.9589 5.2212 0.0 0 +M V30 21 C -9.9343 7.4969 0.0 0 +M V30 22 C -7.982 9.1834 0.0 0 +M V30 23 C -9.4375 4.9555 0.0 0 +M V30 24 C -10.9277 8.6405 0.0 0 +M V30 25 C -8.9755 10.327 0.0 0 +M V30 26 C -10.4541 10.0613 0.0 0 +M V30 27 C -11.4475 11.2049 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 18 17 CFG=1 +M V30 18 1 18 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 1 19 22 +M V30 22 1 20 23 +M V30 23 1 21 24 +M V30 24 2 22 25 +M V30 25 2 24 26 +M V30 26 1 26 27 +M V30 27 1 6 8 +M V30 28 1 11 13 +M V30 29 1 25 26 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 18) +M V30 END COLLECTION +M V30 END CTAB +M END +> +791 + +> +Z28877949 + +> +363.453 + +> +2.926 + +> +2 + +> +70.560 + +> +7 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3092537 + +> +0.94 + +$$$$ +Compound 792 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3129 -0.7451 0.0 0 +M V30 3 N -1.3247 -2.2355 0.0 0 +M V30 4 C -2.6258 0.0236 0.0 0 +M V30 5 C -0.0354 -2.9688 0.0 0 +M V30 6 C -2.6376 1.5376 0.0 0 +M V30 7 C -3.9387 -0.7215 0.0 0 +M V30 8 C -0.0473 -4.4591 0.0 0 +M V30 9 O -1.3484 2.2946 0.0 0 +M V30 10 C -3.9505 2.2946 0.0 0 +M V30 11 C -5.2516 0.0473 0.0 0 +M V30 12 C -0.0591 1.5613 0.0 0 +M V30 13 C -5.2634 1.5613 0.0 0 +M V30 14 C -3.9624 3.8086 0.0 0 +M V30 15 C 1.2301 2.3183 0.0 0 +M V30 16 O 1.2182 3.8323 0.0 0 +M V30 17 N 2.5193 1.5849 0.0 0 +M V30 18 C 3.8086 2.3419 0.0 0 CFG=2 +M V30 19 C 5.0979 1.6086 0.0 0 +M V30 20 C 3.7968 3.8559 0.0 0 +M V30 21 C 6.3871 2.3656 0.0 0 +M V30 22 C 5.086 0.1182 0.0 0 +M V30 23 C 7.6764 1.6322 0.0 0 +M V30 24 C 6.3753 -0.6268 0.0 0 +M V30 25 C 7.6646 0.1419 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 1 12 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 18 17 CFG=1 +M V30 18 1 18 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 1 19 22 +M V30 22 1 21 23 +M V30 23 2 22 24 +M V30 24 2 23 25 +M V30 25 1 11 13 +M V30 26 1 24 25 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 18) +M V30 END COLLECTION +M V30 END CTAB +M END +> +792 + +> +Z1002384298 + +> +340.416 + +> +2.972 + +> +2 + +> +67.430 + +> +7 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3105430 + +> +0.96 + +$$$$ +Compound 793 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 19 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.288 0.7681 0.0 0 +M V30 3 C -0.0118 -1.4889 0.0 0 +M V30 4 C 1.2762 2.2807 0.0 0 CFG=1 +M V30 5 O -0.2363 2.2925 0.0 0 +M V30 6 C 1.2644 3.7933 0.0 0 +M V30 7 C 2.7652 2.2925 0.0 0 +M V30 8 N 2.5525 4.5496 0.0 0 +M V30 9 C 2.5407 6.0622 0.0 0 +M V30 10 C 3.8405 3.8051 0.0 0 +M V30 11 O 1.2289 6.8185 0.0 0 +M V30 12 N 3.8287 6.8185 0.0 0 +M V30 13 O 3.8287 2.3161 0.0 0 +M V30 14 C 5.1286 4.5614 0.0 0 +M V30 15 C 5.1168 6.0858 0.0 0 +M V30 16 C 6.4167 3.8169 0.0 0 +M V30 17 C 6.4049 6.8421 0.0 0 +M V30 18 C 7.7048 4.5732 0.0 0 +M V30 19 C 7.693 6.1095 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 1 4 5 +M V30 5 1 4 6 CFG=1 +M V30 6 1 4 7 +M V30 7 1 6 8 +M V30 8 1 8 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 10 14 +M V30 14 1 12 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 2 17 19 +M V30 19 2 14 15 +M V30 20 1 18 19 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 4) +M V30 END COLLECTION +M V30 END CTAB +M END +> +793 + +> +Z1503960629 + +> +280.343 + +> +1.676 + +> +2 + +> +69.640 + +> +4 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 794 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O 0.7331 -1.2889 0.0 0 +M V30 3 O -0.7686 1.3125 0.0 0 +M V30 4 N 1.2889 0.7686 0.0 0 +M V30 5 C -1.3125 -0.7331 0.0 0 +M V30 6 C 1.2771 2.2822 0.0 0 CFG=2 +M V30 7 C 2.566 3.0508 0.0 0 +M V30 8 C -0.0354 3.0508 0.0 0 +M V30 9 N 2.5542 4.5644 0.0 0 +M V30 10 C -0.0473 4.5644 0.0 0 +M V30 11 C 1.2416 5.3331 0.0 0 +M V30 12 C 3.8431 5.3331 0.0 0 +M V30 13 C 5.132 4.5881 0.0 0 +M V30 14 N 6.421 5.3567 0.0 0 +M V30 15 N 5.1202 3.0981 0.0 0 +M V30 16 C 7.7099 4.6117 0.0 0 +M V30 17 C 6.4092 2.3531 0.0 0 +M V30 18 C 7.6981 3.1218 0.0 0 +M V30 19 C 8.9988 5.3804 0.0 0 +M V30 20 O 6.3973 0.8632 0.0 0 +M V30 21 C 8.987 2.3768 0.0 0 +M V30 22 C 10.2878 4.6354 0.0 0 +M V30 23 C 10.276 3.1454 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 6 4 CFG=1 +M V30 6 1 6 7 +M V30 7 1 6 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 1 9 12 +M V30 12 1 12 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 2 21 23 +M V30 23 1 10 11 +M V30 24 1 17 18 +M V30 25 1 22 23 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 6) +M V30 END COLLECTION +M V30 END CTAB +M END +> +794 + +> +Z1002477940 + +> +336.409 + +> +0.171 + +> +2 + +> +90.870 + +> +3 + +> +parp1 + +> + + +> + + +$$$$ +Compound 795 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2941 0.7598 0.0 0 +M V30 3 C 2.5883 0.0237 0.0 0 +M V30 4 C 1.2823 2.2796 0.0 0 +M V30 5 C 3.8825 0.7836 0.0 0 +M V30 6 C 2.5765 3.0514 0.0 0 +M V30 7 C 3.8707 2.3034 0.0 0 +M V30 8 S 5.1649 3.0752 0.0 0 +M V30 9 C 6.4591 2.3271 0.0 0 +M V30 10 C 7.7533 3.0989 0.0 0 +M V30 11 N 9.0475 2.3509 0.0 0 +M V30 12 C 10.3416 3.1226 0.0 0 +M V30 13 C 9.0356 0.8548 0.0 0 +M V30 14 C 11.6358 2.3746 0.0 0 +M V30 15 N 12.93 3.1464 0.0 0 +M V30 16 N 11.624 0.8786 0.0 0 +M V30 17 C 14.2242 2.3984 0.0 0 +M V30 18 C 12.9182 0.1424 0.0 0 +M V30 19 C 14.2124 0.9023 0.0 0 +M V30 20 C 15.5184 3.1701 0.0 0 +M V30 21 O 12.9063 -1.3535 0.0 0 +M V30 22 C 15.5066 0.1662 0.0 0 +M V30 23 C 16.8126 2.4221 0.0 0 +M V30 24 C 16.8008 0.9261 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 1 9 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 17 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 1 19 22 +M V30 22 2 20 23 +M V30 23 2 22 24 +M V30 24 1 6 7 +M V30 25 1 18 19 +M V30 26 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +795 + +> +Z46302583 + +> +359.873 + +> +3.422 + +> +1 + +> +44.700 + +> +6 + +> +parp3 + +> + + +> + + +$$$$ +Compound 796 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.4438 -0.4497 0.0 0 +M V30 3 C -2.5681 0.568 0.0 0 +M V30 4 C -1.7633 -1.9053 0.0 0 +M V30 5 N -2.2722 2.0473 0.0 0 +M V30 6 C -4.0119 0.1183 0.0 0 +M V30 7 C -3.2071 -2.355 0.0 0 +M V30 8 C -0.852 2.5207 0.0 0 +M V30 9 C -4.3314 -1.3373 0.0 0 +M V30 10 Cl -3.5267 -3.8107 0.0 0 +M V30 11 O 0.2485 1.5266 0.0 0 +M V30 12 C -0.5562 4.0 0.0 0 CFG=1 +M V30 13 N 0.8047 4.6273 0.0 0 +M V30 14 C -1.574 5.1243 0.0 0 +M V30 15 C 0.639 6.1303 0.0 0 +M V30 16 C 2.0947 3.8817 0.0 0 +M V30 17 C -0.8402 6.438 0.0 0 +M V30 18 C 3.3846 4.6509 0.0 0 +M V30 19 C 4.6746 3.9054 0.0 0 +M V30 20 O 4.6628 2.4142 0.0 0 +M V30 21 N 5.9646 4.6746 0.0 0 +M V30 22 C 7.2546 3.929 0.0 0 +M V30 23 C 8.5445 4.6983 0.0 0 +M V30 24 C 7.2427 2.4379 0.0 0 +M V30 25 C 9.8345 3.9527 0.0 0 +M V30 26 C 8.5327 1.7041 0.0 0 +M V30 27 C 9.8227 2.4615 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 12 8 CFG=3 +M V30 12 1 12 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 1 16 18 +M V30 18 1 18 19 +M V30 19 2 19 20 +M V30 20 1 19 21 +M V30 21 1 21 22 +M V30 22 2 22 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 2 24 26 +M V30 26 2 25 27 +M V30 27 1 7 9 +M V30 28 1 15 17 +M V30 29 1 26 27 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 12) +M V30 END COLLECTION +M V30 END CTAB +M END +> +796 + +> +Z369065574 + +> +406.306 + +> +4.015 + +> +2 + +> +61.440 + +> +6 + +> +ATM + +> + + +> + + +$$$$ +Compound 797 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4907 0.0 0 +M V30 3 N -1.325 -2.236 0.0 0 +M V30 4 N 1.2777 -2.236 0.0 0 +M V30 5 C -1.3369 -3.7268 0.0 0 +M V30 6 C -2.6383 -1.4788 0.0 0 +M V30 7 C 1.2659 -3.7268 0.0 0 +M V30 8 O -2.6501 -4.4603 0.0 0 +M V30 9 C -0.0473 -4.4603 0.0 0 +M V30 10 C -3.9516 -2.2242 0.0 0 +M V30 11 C 2.5555 -4.4603 0.0 0 +M V30 12 C -0.0591 -5.951 0.0 0 +M V30 13 N -5.2648 -1.467 0.0 0 +M V30 14 C 2.5436 -5.951 0.0 0 +M V30 15 C 1.2304 -6.6845 0.0 0 +M V30 16 C -6.5781 -2.2124 0.0 0 +M V30 17 O -6.5899 -3.7031 0.0 0 +M V30 18 C -7.8913 -1.4552 0.0 0 +M V30 19 C -9.2046 -2.2005 0.0 0 +M V30 20 C -7.9032 0.0591 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 9 12 +M V30 12 1 10 13 +M V30 13 2 11 14 +M V30 14 2 12 15 +M V30 15 1 13 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 18 19 +M V30 19 1 18 20 +M V30 20 2 7 9 +M V30 21 1 14 15 +M V30 END BOND +M V30 END CTAB +M END +> +797 + +> +Z278333554 + +> +275.303 + +> +1.487 + +> +2 + +> +78.510 + +> +4 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 798 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.5158 0.0 0 +M V30 3 C -1.3263 2.2856 0.0 0 +M V30 4 C 1.2789 2.2856 0.0 0 +M V30 5 F -2.6408 1.5395 0.0 0 +M V30 6 C -1.3382 3.8014 0.0 0 +M V30 7 C 1.2671 3.8014 0.0 0 +M V30 8 C -0.0473 4.5593 0.0 0 +M V30 9 C 2.5579 4.5593 0.0 0 +M V30 10 N 3.8488 3.8251 0.0 0 +M V30 11 C 5.1396 4.583 0.0 0 +M V30 12 O 5.1278 6.0989 0.0 0 +M V30 13 C 6.4305 3.8488 0.0 0 +M V30 14 N 6.4186 2.3566 0.0 0 +M V30 15 C 7.7213 4.6067 0.0 0 +M V30 16 C 7.7095 1.6105 0.0 0 +M V30 17 C 9.0121 3.8725 0.0 0 +M V30 18 O 7.6976 0.1184 0.0 0 +M V30 19 C 9.0003 2.3803 0.0 0 +M V30 20 C 10.303 4.6304 0.0 0 +M V30 21 C 10.2911 1.6342 0.0 0 +M V30 22 C 11.5938 3.8962 0.0 0 +M V30 23 C 11.582 2.404 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 1 9 10 +M V30 10 1 10 11 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 1 13 14 +M V30 14 2 13 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 1 17 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 2 21 23 +M V30 23 1 7 8 +M V30 24 2 17 19 +M V30 25 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +798 + +> +Z1001840594 + +> +330.741 + +> +2.939 + +> +2 + +> +58.200 + +> +3 + +> +parp15, Tankyrase1 + +> + + +> + + +$$$$ +Compound 799 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 31 33 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2912 -0.7344 0.0 0 +M V30 3 N 1.2794 -2.2271 0.0 0 +M V30 4 N 2.5825 0.0236 0.0 0 +M V30 5 C 2.5706 -2.9616 0.0 0 +M V30 6 C -0.0355 -2.9616 0.0 0 +M V30 7 C 3.8737 -0.7107 0.0 0 +M V30 8 N 2.8668 -4.4187 0.0 0 +M V30 9 C 3.8619 -2.2034 0.0 0 +M V30 10 C -1.3504 -2.2034 0.0 0 +M V30 11 O 5.165 0.0473 0.0 0 +M V30 12 C 4.3476 -4.5608 0.0 0 +M V30 13 N 4.9636 -3.1985 0.0 0 +M V30 14 C -2.6654 -2.9379 0.0 0 +M V30 15 C 5.0821 -5.8521 0.0 0 +M V30 16 C 6.4207 -2.8786 0.0 0 +M V30 17 C -3.9803 -2.1797 0.0 0 +M V30 18 N 4.3239 -7.1433 0.0 0 +M V30 19 C 6.8709 -1.4334 0.0 0 +M V30 20 C 5.0584 -8.4346 0.0 0 +M V30 21 C 2.8075 -7.1315 0.0 0 +M V30 22 C 6.551 -8.4227 0.0 0 +M V30 23 C 2.0494 -8.4227 0.0 0 +M V30 24 O 7.2973 -7.1078 0.0 0 +M V30 25 N 7.2973 -9.714 0.0 0 +M V30 26 C 0.533 -8.4109 0.0 0 +M V30 27 C -0.225 -9.7022 0.0 0 +M V30 28 C -0.225 -7.0959 0.0 0 +M V30 29 C -1.7414 -9.6903 0.0 0 +M V30 30 C -1.7414 -7.0841 0.0 0 +M V30 31 C -2.4995 -8.3754 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 8 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 1 12 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 1 18 20 +M V30 20 1 18 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 2 22 24 +M V30 24 1 22 25 +M V30 25 1 23 26 +M V30 26 1 26 27 +M V30 27 1 26 28 +M V30 28 1 27 29 +M V30 29 1 28 30 +M V30 30 1 29 31 +M V30 31 1 7 9 +M V30 32 1 12 13 +M V30 33 1 30 31 +M V30 END BOND +M V30 END CTAB +M END +> +799 + +> +Z1003133018 + +> +432.560 + +> +3.390 + +> +2 + +> +113.560 + +> +11 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 800 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 19 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3078 0.754 0.0 0 +M V30 3 N -2.6156 0.0235 0.0 0 +M V30 4 C -1.3195 2.2621 0.0 0 +M V30 5 C -2.6274 -1.4609 0.0 0 +M V30 6 N -3.8527 -2.3328 0.0 0 +M V30 7 C -1.4256 -2.3328 0.0 0 +M V30 8 N -3.405 -3.7466 0.0 0 +M V30 9 C -5.2901 -1.8615 0.0 0 +M V30 10 C -1.8969 -3.7466 0.0 0 +M V30 11 C -5.6082 -0.3888 0.0 0 +M V30 12 C -6.4094 -2.8512 0.0 0 +M V30 13 C -1.025 -4.9484 0.0 0 +M V30 14 C -7.0456 0.0824 0.0 0 +M V30 15 C -4.5125 0.6244 0.0 0 +M V30 16 C -7.8468 -2.3799 0.0 0 +M V30 17 C -8.1649 -0.9072 0.0 0 +M V30 18 C -9.6023 -0.4359 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 5 6 +M V30 6 2 5 7 +M V30 7 1 6 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 9 11 +M V30 11 1 9 12 +M V30 12 1 10 13 +M V30 13 1 11 14 +M V30 14 1 11 15 +M V30 15 2 12 16 +M V30 16 2 14 17 +M V30 17 1 17 18 +M V30 18 2 8 10 +M V30 19 1 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +800 + +> +Z203671472 + +> +243.304 + +> +2.889 + +> +1 + +> +46.920 + +> +2 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL551657 + +> +0.87 + +$$$$ +Compound 801 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5068 0.0 0 +M V30 3 N -1.3185 2.2721 0.0 0 +M V30 4 C 1.2714 2.2721 0.0 0 +M V30 5 C -1.3303 3.779 0.0 0 +M V30 6 C 1.2596 3.779 0.0 0 +M V30 7 C 2.5546 1.5304 0.0 0 +M V30 8 N -0.047 4.5324 0.0 0 +M V30 9 C -2.637 4.5324 0.0 0 +M V30 10 C 2.5428 4.5324 0.0 0 +M V30 11 C 3.8378 2.2956 0.0 0 +M V30 12 C -3.9438 3.8025 0.0 0 +M V30 13 C 3.8261 3.8025 0.0 0 +M V30 14 C -5.2505 4.556 0.0 0 +M V30 15 C -6.5573 3.8261 0.0 0 +M V30 16 O -6.5691 2.3427 0.0 0 +M V30 17 N -7.8641 4.5795 0.0 0 +M V30 18 C -9.1708 3.8496 0.0 0 +M V30 19 C -7.8758 6.0864 0.0 0 +M V30 20 C -10.4776 4.6031 0.0 0 CFG=1 +M V30 21 C -9.1826 6.8516 0.0 0 +M V30 22 C -11.7844 3.8731 0.0 0 +M V30 23 C -10.4894 6.1099 0.0 0 +M V30 24 O -11.7961 2.3898 0.0 0 +M V30 25 O -13.0911 4.6266 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 20 22 CFG=1 +M V30 22 1 20 23 +M V30 23 2 22 24 +M V30 24 1 22 25 +M V30 25 1 6 8 +M V30 26 1 11 13 +M V30 27 1 21 23 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 20) +M V30 END COLLECTION +M V30 END CTAB +M END +> +801 + +> +Z1443622248 + +> +343.377 + +> +0.177 + +> +2 + +> +99.070 + +> +5 + +> +Tankyrase1, parp2 + +> + + +> + + +$$$$ +Compound 802 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.4237 -0.4508 0.0 0 +M V30 3 C -0.9017 -1.2102 0.0 0 +M V30 4 C 2.717 0.3084 0.0 0 +M V30 5 C 1.4119 -1.9458 0.0 0 +M V30 6 C -0.0237 -2.4204 0.0 0 +M V30 7 O 2.7051 1.8271 0.0 0 +M V30 8 N 4.0102 -0.4271 0.0 0 +M V30 9 N 2.7051 -2.6814 0.0 0 +M V30 10 C 3.9984 -1.922 0.0 0 +M V30 11 C 5.2916 -2.6577 0.0 0 +M V30 12 N 6.5849 -1.8983 0.0 0 +M V30 13 C 7.8781 -2.6339 0.0 0 +M V30 14 C 6.573 -0.3796 0.0 0 +M V30 15 C 9.1714 -1.8746 0.0 0 +M V30 16 C 7.8663 0.3796 0.0 0 +M V30 17 N 9.1595 -0.3559 0.0 0 +M V30 18 C 10.4528 0.4034 0.0 0 +M V30 19 O 11.746 -0.3322 0.0 0 +M V30 20 C 10.4409 1.922 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 1 8 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 17 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 5 6 +M V30 21 2 9 10 +M V30 22 1 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +802 + +> +Z44499719 + +> +292.357 + +> +-0.548 + +> +1 + +> +65.010 + +> +2 + +> +parp2 + +> + + +> + + +$$$$ +Compound 803 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4913 0.0 0 +M V30 3 N -1.3256 -2.2251 0.0 0 +M V30 4 N 1.2782 -2.2251 0.0 0 +M V30 5 C -1.3374 -3.7164 0.0 0 +M V30 6 C -2.6394 -1.4676 0.0 0 +M V30 7 C 1.2664 -3.7164 0.0 0 +M V30 8 C 2.5683 -1.4676 0.0 0 +M V30 9 O -2.6512 -4.4621 0.0 0 +M V30 10 C -0.0473 -4.4621 0.0 0 +M V30 11 C -3.9531 -2.2014 0.0 0 +M V30 12 N 2.3671 -4.7106 0.0 0 +M V30 13 N 0.2485 -5.9179 0.0 0 +M V30 14 C -5.2669 -1.4439 0.0 0 +M V30 15 C 1.7398 -6.0718 0.0 0 +M V30 16 C -0.7693 -7.0186 0.0 0 +M V30 17 N -6.5807 -2.1778 0.0 0 +M V30 18 C -7.8945 -1.4203 0.0 0 +M V30 19 C -6.5925 -3.6691 0.0 0 +M V30 20 C -9.2083 -2.1541 0.0 0 +M V30 21 C -10.5221 -1.3966 0.0 0 +M V30 22 C -9.2201 -3.6454 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 5 10 +M V30 10 1 6 11 +M V30 11 1 7 12 +M V30 12 1 10 13 +M V30 13 1 11 14 +M V30 14 2 12 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 1 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 20 21 +M V30 21 1 20 22 +M V30 22 2 7 10 +M V30 23 1 13 15 +M V30 END BOND +M V30 END CTAB +M END +> +803 + +> +Z909880602 + +> +307.391 + +> +2.077 + +> +0 + +> +61.680 + +> +6 + +> +Serine-protein kinase ATM + +> +CHEMBL113 + +> +0.92 + +$$$$ +Compound 804 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.6012 1.3792 0.0 0 +M V30 3 N 2.0747 1.5442 0.0 0 +M V30 4 C -0.2947 2.6052 0.0 0 +M V30 5 C 2.9471 0.3418 0.0 0 +M V30 6 C 2.6759 2.9235 0.0 0 +M V30 7 N -1.8036 2.617 0.0 0 +M V30 8 C 0.1532 4.0434 0.0 0 +M V30 9 C 4.4207 0.5069 0.0 0 CFG=1 +M V30 10 C 4.1495 3.0885 0.0 0 +M V30 11 N -2.2751 4.0552 0.0 0 +M V30 12 C -1.0727 4.9393 0.0 0 +M V30 13 C 5.293 -0.6955 0.0 0 +M V30 14 C 5.0219 1.8861 0.0 0 +M V30 15 C -1.0845 6.4483 0.0 0 +M V30 16 O 6.7666 -0.5304 0.0 0 +M V30 17 O 4.6682 -2.0512 0.0 0 +M V30 18 C 0.2004 7.2027 0.0 0 +M V30 19 C -2.393 7.2027 0.0 0 +M V30 20 C 0.1886 8.7117 0.0 0 +M V30 21 C -2.4048 8.7117 0.0 0 +M V30 22 C -1.1199 9.4661 0.0 0 +M V30 23 O -1.1316 10.9751 0.0 0 +M V30 24 C -2.4402 11.7295 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 9 13 CFG=3 +M V30 13 1 9 14 +M V30 14 1 12 15 +M V30 15 2 13 16 +M V30 16 1 13 17 +M V30 17 2 15 18 +M V30 18 1 15 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 2 20 22 +M V30 22 1 22 23 +M V30 23 1 23 24 +M V30 24 1 10 14 +M V30 25 2 11 12 +M V30 26 1 21 22 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 9) +M V30 END COLLECTION +M V30 END CTAB +M END +> +804 + +> +Z1603198224 + +> +329.350 + +> +1.973 + +> +2 + +> +95.520 + +> +4 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 805 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.1029 -0.9962 0.0 0 +M V30 3 N 0.9369 -2.4786 0.0 0 +M V30 4 N 2.5616 -0.6759 0.0 0 +M V30 5 C 2.3007 -3.0834 0.0 0 +M V30 6 C -0.3795 -3.2139 0.0 0 +M V30 7 C 3.2969 -1.9686 0.0 0 CFG=2 +M V30 8 O 2.5972 -4.5421 0.0 0 +M V30 9 C -1.6959 -2.4549 0.0 0 +M V30 10 C 4.2931 -3.0716 0.0 0 +M V30 11 C 4.5066 -1.0673 0.0 0 +M V30 12 C -3.0123 -3.1902 0.0 0 +M V30 13 C 5.7518 -2.7514 0.0 0 +M V30 14 O -3.0241 -4.6845 0.0 0 +M V30 15 N -4.3287 -2.4312 0.0 0 +M V30 16 C 6.748 -3.8543 0.0 0 +M V30 17 C 6.2025 -1.3045 0.0 0 +M V30 18 C -5.6451 -3.1664 0.0 0 +M V30 19 C 8.2067 -3.5341 0.0 0 +M V30 20 C 7.6612 -0.9843 0.0 0 +M V30 21 O -5.6569 -4.6607 0.0 0 +M V30 22 N -6.9615 -2.4074 0.0 0 +M V30 23 O 9.4164 -4.4117 0.0 0 +M V30 24 C 8.6574 -2.0872 0.0 0 +M V30 25 C -8.2779 -3.1427 0.0 0 +M V30 26 C 10.6261 -3.5104 0.0 0 +M V30 27 O 10.1517 -2.0754 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 1 7 11 CFG=3 +M V30 11 1 9 12 +M V30 12 1 10 13 +M V30 13 2 12 14 +M V30 14 1 12 15 +M V30 15 2 13 16 +M V30 16 1 13 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 2 18 21 +M V30 21 1 18 22 +M V30 22 1 19 23 +M V30 23 2 19 24 +M V30 24 1 22 25 +M V30 25 1 23 26 +M V30 26 1 24 27 +M V30 27 1 5 7 +M V30 28 1 20 24 +M V30 29 1 26 27 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 7) +M V30 END COLLECTION +M V30 END CTAB +M END +> +805 + +> +Z930842458 + +> +376.364 + +> +1.108 + +> +3 + +> +126.070 + +> +5 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 806 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5083 0.0 0 +M V30 3 N -1.3197 2.2624 0.0 0 +M V30 4 C 1.2726 2.2624 0.0 0 +M V30 5 C -1.3315 3.7707 0.0 0 +M V30 6 C 1.2608 3.7707 0.0 0 +M V30 7 C 2.557 1.5318 0.0 0 +M V30 8 N -0.0471 4.5249 0.0 0 +M V30 9 C -2.6395 4.5249 0.0 0 +M V30 10 C 2.5452 4.5249 0.0 0 +M V30 11 C 3.8414 2.286 0.0 0 +M V30 12 N -3.9475 3.7943 0.0 0 +M V30 13 C 3.8297 3.7943 0.0 0 +M V30 14 C -5.2555 4.5485 0.0 0 +M V30 15 C -3.9593 2.3096 0.0 0 +M V30 16 C -6.5635 3.8179 0.0 0 +M V30 17 C -7.8715 4.572 0.0 0 +M V30 18 O -7.8833 6.0804 0.0 0 +M V30 19 N -9.1795 3.8414 0.0 0 +M V30 20 C -10.4875 4.5956 0.0 0 +M V30 21 O -11.7955 3.865 0.0 0 +M V30 22 N -10.4993 6.1039 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 1 12 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 2 20 21 +M V30 21 1 20 22 +M V30 22 1 6 8 +M V30 23 1 11 13 +M V30 END BOND +M V30 END CTAB +M END +> +806 + +> +Z199661662 + +> +303.316 + +> +-0.578 + +> +3 + +> +116.890 + +> +5 + +> +parp1 + +> + + +> + + +$$$$ +Compound 807 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.288 0.7562 0.0 0 +M V30 3 C -1.3116 0.7562 0.0 0 +M V30 4 N 1.2762 2.2688 0.0 0 +M V30 5 C 2.576 0.0236 0.0 0 +M V30 6 C -2.6233 0.0236 0.0 0 +M V30 7 C 2.5642 3.0369 0.0 0 +M V30 8 C 3.864 0.7799 0.0 0 +M V30 9 C 2.5642 -1.4652 0.0 0 +M V30 10 C -3.9349 0.7799 0.0 0 +M V30 11 N 3.8522 2.2924 0.0 0 +M V30 12 C 2.5524 4.5494 0.0 0 +M V30 13 N 5.1521 0.0472 0.0 0 +M V30 14 O 1.2525 -2.1979 0.0 0 +M V30 15 N 3.8522 -2.1979 0.0 0 +M V30 16 O -3.9468 2.2924 0.0 0 +M V30 17 N -5.2466 0.0472 0.0 0 +M V30 18 C 1.7961 5.8611 0.0 0 +M V30 19 C 3.2968 5.8611 0.0 0 +M V30 20 C 5.1402 -1.4416 0.0 0 +M V30 21 C 6.4401 0.8035 0.0 0 +M V30 22 C -6.5583 0.8035 0.0 0 +M V30 23 O 6.4283 -2.1742 0.0 0 +M V30 24 C 7.1845 2.1152 0.0 0 +M V30 25 C 7.929 0.8153 0.0 0 +M V30 26 O -6.5701 2.316 0.0 0 +M V30 27 N -7.8699 0.0709 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 7 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 9 15 +M V30 15 2 10 16 +M V30 16 1 10 17 +M V30 17 1 12 18 +M V30 18 1 12 19 +M V30 19 1 13 20 +M V30 20 1 13 21 +M V30 21 1 17 22 +M V30 22 2 20 23 +M V30 23 1 21 24 +M V30 24 1 21 25 +M V30 25 2 22 26 +M V30 26 1 22 27 +M V30 27 1 8 11 +M V30 28 1 15 20 +M V30 29 1 18 19 +M V30 30 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +807 + +> +Z237863526 + +> +390.417 + +> +0.638 + +> +3 + +> +147.380 + +> +6 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 808 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 1.296 -0.7372 0.0 0 +M V30 3 F 0.535 -2.0333 0.0 0 +M V30 4 F 2.0452 0.5826 0.0 0 +M V30 5 C 2.5921 -1.4863 0.0 0 +M V30 6 C 3.8882 -0.7134 0.0 0 +M V30 7 C 2.5802 -2.9845 0.0 0 +M V30 8 C 5.1843 -1.4625 0.0 0 +M V30 9 C 3.8763 -3.7217 0.0 0 +M V30 10 C 5.1724 -2.9607 0.0 0 +M V30 11 C 6.4685 -3.698 0.0 0 +M V30 12 C 7.7646 -2.937 0.0 0 +M V30 13 N 9.0607 -3.6742 0.0 0 +M V30 14 C 10.3568 -2.9132 0.0 0 +M V30 15 O 10.3449 -1.3912 0.0 0 +M V30 16 C 11.6528 -3.6504 0.0 0 +M V30 17 N 11.6409 -5.1486 0.0 0 +M V30 18 C 12.9489 -2.8894 0.0 0 +M V30 19 C 12.937 -5.8977 0.0 0 +M V30 20 C 14.245 -3.6266 0.0 0 +M V30 21 O 12.9251 -7.396 0.0 0 +M V30 22 C 14.2331 -5.1248 0.0 0 +M V30 23 C 15.5411 -2.8656 0.0 0 +M V30 24 C 15.5292 -5.874 0.0 0 +M V30 25 C 16.8372 -3.6028 0.0 0 +M V30 26 C 16.8253 -5.1011 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 2 5 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 2 8 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 1 13 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 2 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 1 19 22 +M V30 22 1 20 23 +M V30 23 1 22 24 +M V30 24 2 23 25 +M V30 25 2 24 26 +M V30 26 1 9 10 +M V30 27 2 20 22 +M V30 28 1 25 26 +M V30 END BOND +M V30 END CTAB +M END +> +808 + +> +Z65692087 + +> +360.330 + +> +3.095 + +> +2 + +> +58.200 + +> +5 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 809 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2881 -0.7445 0.0 0 +M V30 3 N 1.2763 -2.2336 0.0 0 +M V30 4 C 2.5763 0.0236 0.0 0 +M V30 5 C -0.0354 -2.9663 0.0 0 +M V30 6 C 3.9353 -0.579 0.0 0 +M V30 7 C 2.7181 1.5245 0.0 0 +M V30 8 C -1.3472 -2.2099 0.0 0 +M V30 9 C -0.0472 -4.4553 0.0 0 +M V30 10 N 4.9281 0.5436 0.0 0 +M V30 11 C 4.2308 -2.0326 0.0 0 +M V30 12 N 4.1717 1.8436 0.0 0 +M V30 13 C -2.659 -2.9426 0.0 0 +M V30 14 C -1.359 -5.1881 0.0 0 +M V30 15 C 6.4053 0.4018 0.0 0 +M V30 16 N -4.1008 -2.4699 0.0 0 +M V30 17 C -2.6708 -4.4317 0.0 0 +M V30 18 C 7.2798 1.6308 0.0 0 +M V30 19 N -4.999 -3.6754 0.0 0 +M V30 20 N -4.1126 -4.8808 0.0 0 +M V30 21 C 6.6535 3.0135 0.0 0 +M V30 22 C 8.7571 1.489 0.0 0 +M V30 23 C 7.528 4.2426 0.0 0 +M V30 24 C 9.6316 2.7181 0.0 0 +M V30 25 C 9.0053 4.1008 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 6 11 +M V30 11 2 7 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 10 15 +M V30 15 1 13 16 +M V30 16 2 13 17 +M V30 17 1 15 18 +M V30 18 2 16 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 1 18 22 +M V30 22 1 21 23 +M V30 23 2 22 24 +M V30 24 2 23 25 +M V30 25 1 10 12 +M V30 26 1 14 17 +M V30 27 1 19 20 +M V30 28 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +809 + +> +Z1420691934 + +> +332.359 + +> +2.715 + +> +2 + +> +88.490 + +> +4 + +> +parp14 + +> + + +> + + +$$$$ +Compound 810 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -1.3096 -0.7315 0.0 0 +M V30 3 C -2.6193 0.0353 0.0 0 +M V30 4 C -1.3214 -2.2181 0.0 0 +M V30 5 C -3.9289 -0.6961 0.0 0 +M V30 6 C -2.6311 -2.9614 0.0 0 +M V30 7 N -5.3683 -0.2241 0.0 0 +M V30 8 C -3.9407 -2.1827 0.0 0 +M V30 9 C -6.265 -1.4276 0.0 0 +M V30 10 N -5.3801 -2.6311 0.0 0 +M V30 11 C -7.7753 -1.4158 0.0 0 +M V30 12 N -8.5304 -2.7018 0.0 0 +M V30 13 C -10.0406 -2.69 0.0 0 +M V30 14 O -10.8075 -1.3804 0.0 0 +M V30 15 C -10.8075 -3.9761 0.0 0 +M V30 16 C -10.2058 -5.333 0.0 0 +M V30 17 C -12.306 -4.1177 0.0 0 +M V30 18 N -8.7546 -5.6279 0.0 0 CHG=1 +M V30 19 N -11.3267 -6.324 0.0 0 +M V30 20 N -12.6245 -5.5689 0.0 0 +M V30 21 O -7.7635 -4.507 0.0 0 +M V30 22 O -8.3062 -7.0438 0.0 0 CHG=-1 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 2 15 17 +M V30 17 1 16 18 +M V30 18 2 16 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 1 18 22 +M V30 22 1 6 8 +M V30 23 1 9 10 +M V30 24 1 19 20 +M V30 END BOND +M V30 END CTAB +M END +> +810 + +> +Z1916268302 + +> +304.237 + +> +0.642 + +> +3 + +> +129.600 + +> +4 + +> +ATM + +> + + +> + + +$$$$ +Compound 811 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4949 0.0 0 +M V30 3 N 1.2813 -2.2305 0.0 0 +M V30 4 C -1.3288 -2.2305 0.0 0 +M V30 5 C 1.2695 -3.7255 0.0 0 +M V30 6 C -1.3407 -3.7255 0.0 0 +M V30 7 C -2.6458 -1.4712 0.0 0 +M V30 8 C 2.5627 -4.4611 0.0 0 +M V30 9 C -0.0474 -4.4611 0.0 0 +M V30 10 C -2.6576 -4.4611 0.0 0 +M V30 11 C -3.9627 -2.2068 0.0 0 +M V30 12 O 2.5509 -5.956 0.0 0 +M V30 13 N 3.856 -3.7017 0.0 0 +M V30 14 C -3.9746 -3.7017 0.0 0 +M V30 15 C 5.1492 -4.4373 0.0 0 +M V30 16 C 6.4425 -3.678 0.0 0 +M V30 17 C 7.7357 -4.4136 0.0 0 +M V30 18 C 9.029 -3.6543 0.0 0 +M V30 19 C 9.0171 -2.1356 0.0 0 +M V30 20 C 10.3222 -4.3899 0.0 0 +M V30 21 C 10.3103 -1.3762 0.0 0 +M V30 22 C 11.6154 -3.6305 0.0 0 +M V30 23 C 11.6036 -2.1119 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 2 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 1 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 6 9 +M V30 24 1 11 14 +M V30 25 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +811 + +> +Z397188004 + +> +312.406 + +> +3.823 + +> +2 + +> +58.200 + +> +5 + +> +parp15, Tankyrase1 + +> + + +> + + +$$$$ +Compound 812 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.505 0.0 0 +M V30 3 C -1.3169 2.2576 0.0 0 +M V30 4 O -1.3287 3.7627 0.0 0 +M V30 5 C -2.6339 4.527 0.0 0 +M V30 6 C -2.6456 6.0321 0.0 0 +M V30 7 C -3.939 3.7862 0.0 0 +M V30 8 C -3.9508 6.7846 0.0 0 +M V30 9 C -5.2442 4.5505 0.0 0 +M V30 10 N -3.9626 8.2897 0.0 0 +M V30 11 C -5.256 6.0556 0.0 0 +M V30 12 C -5.2678 9.0422 0.0 0 +M V30 13 O -6.5729 8.3014 0.0 0 +M V30 14 C -5.2795 10.5473 0.0 0 +M V30 15 C -3.9978 11.2999 0.0 0 +M V30 16 C -6.5847 11.2999 0.0 0 +M V30 17 C -4.0096 12.8049 0.0 0 +M V30 18 C -2.7162 10.5708 0.0 0 +M V30 19 N -6.5965 12.8049 0.0 0 +M V30 20 C -5.3148 13.5575 0.0 0 +M V30 21 C -2.7279 13.5575 0.0 0 +M V30 22 C -1.4345 11.3234 0.0 0 +M V30 23 O -5.3265 15.0626 0.0 0 +M V30 24 C -1.4462 12.8285 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 2 3 +M V30 3 1 3 4 +M V30 4 1 4 5 +M V30 5 2 5 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 1 8 10 +M V30 10 2 8 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 2 14 16 +M V30 16 2 15 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 1 17 20 +M V30 20 1 17 21 +M V30 21 2 18 22 +M V30 22 2 20 23 +M V30 23 2 21 24 +M V30 24 1 9 11 +M V30 25 1 19 20 +M V30 26 1 22 24 +M V30 END BOND +M V30 END CTAB +M END +> +812 + +> +Z1816889275 + +> +326.322 + +> +2.130 + +> +2 + +> +67.430 + +> +5 + +> +parp2 + +> + + +> + + +$$$$ +Compound 813 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5055 0.0 0 +M V30 3 C -1.3173 2.2582 0.0 0 +M V30 4 C 1.2702 2.2582 0.0 0 +M V30 5 C -1.329 3.7638 0.0 0 +M V30 6 C 1.2585 3.7638 0.0 0 +M V30 7 N -2.6346 4.5283 0.0 0 +M V30 8 C -0.047 4.5283 0.0 0 +M V30 9 C -3.9402 3.7873 0.0 0 +M V30 10 O -3.952 2.3053 0.0 0 +M V30 11 C -5.2458 4.5518 0.0 0 +M V30 12 N -6.5514 3.8108 0.0 0 +M V30 13 C -7.8569 4.5753 0.0 0 +M V30 14 C -6.5631 2.3288 0.0 0 +M V30 15 C -9.1625 3.8343 0.0 0 +M V30 16 C -7.8687 6.0809 0.0 0 +M V30 17 O -9.1743 2.3523 0.0 0 +M V30 18 N -10.4681 4.5989 0.0 0 +M V30 19 C -11.7737 3.8579 0.0 0 +M V30 20 C -13.0792 4.6224 0.0 0 +M V30 21 C -11.7854 2.3759 0.0 0 +M V30 22 C -14.3848 3.8814 0.0 0 +M V30 23 C -13.091 6.1279 0.0 0 +M V30 24 C -13.091 1.6466 0.0 0 +M V30 25 C -14.3966 2.3994 0.0 0 +M V30 26 C -14.3966 6.8807 0.0 0 +M V30 27 C -11.8089 6.8807 0.0 0 +M V30 28 C -15.7021 6.1514 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 7 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 1 13 16 +M V30 16 2 15 17 +M V30 17 1 15 18 +M V30 18 1 18 19 +M V30 19 2 19 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 20 23 +M V30 23 2 21 24 +M V30 24 2 22 25 +M V30 25 1 23 26 +M V30 26 1 23 27 +M V30 27 1 26 28 +M V30 28 1 6 8 +M V30 29 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +813 + +> +Z44458029 + +> +385.475 + +> +4.143 + +> +2 + +> +61.440 + +> +8 + +> +ATM + +> + + +> + + +$$$$ +Compound 814 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4889 0.0 0 +M V30 3 N -1.3234 -2.2215 0.0 0 +M V30 4 C 1.2762 -2.2215 0.0 0 +M V30 5 C -2.6351 -1.4652 0.0 0 +M V30 6 N 1.418 -3.6986 0.0 0 +M V30 7 C 2.6351 -1.5952 0.0 0 +M V30 8 C -3.9467 -2.1979 0.0 0 +M V30 9 C 2.8714 -3.994 0.0 0 +M V30 10 C 3.6277 -2.6942 0.0 0 +M V30 11 O -3.9585 -3.6868 0.0 0 +M V30 12 N -5.2584 -1.4416 0.0 0 +M V30 13 C 3.6159 -5.282 0.0 0 +M V30 14 C 5.1166 -2.6823 0.0 0 +M V30 15 C -6.57 -2.1742 0.0 0 +M V30 16 C 5.1048 -5.2702 0.0 0 +M V30 17 C 5.861 -3.9704 0.0 0 +M V30 18 C -7.8817 -1.418 0.0 0 CFG=2 +M V30 19 O -8.6379 -2.706 0.0 0 +M V30 20 C -9.1933 -0.6499 0.0 0 +M V30 21 C -7.149 -0.1063 0.0 0 +M V30 22 C -10.505 -1.3943 0.0 0 +M V30 23 C -9.2051 0.8626 0.0 0 +M V30 24 C -11.8166 -0.6262 0.0 0 +M V30 25 C -10.5168 1.6188 0.0 0 +M V30 26 C -11.8284 0.8862 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 1 12 15 +M V30 15 2 13 16 +M V30 16 2 14 17 +M V30 17 1 15 18 +M V30 18 1 18 19 +M V30 19 1 18 20 CFG=3 +M V30 20 1 18 21 +M V30 21 1 20 22 +M V30 22 1 20 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 1 24 26 +M V30 26 2 9 10 +M V30 27 1 16 17 +M V30 28 1 25 26 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 18) +M V30 END COLLECTION +M V30 END CTAB +M END +> +814 + +> +Z1841982601 + +> +357.447 + +> +3.094 + +> +4 + +> +94.220 + +> +6 + +> +ATM + +> + + +> + + +$$$$ +Compound 815 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.5144 0.0 0 +M V30 3 C -1.3132 -0.7335 0.0 0 CFG=2 +M V30 4 C 1.2777 2.2716 0.0 0 +M V30 5 C -1.3251 2.2716 0.0 0 +M V30 6 C -1.3251 -2.2242 0.0 0 +M V30 7 C -2.6265 0.0236 0.0 0 +M V30 8 C 2.5673 1.5262 0.0 0 +M V30 9 C 1.2659 3.786 0.0 0 +M V30 10 C -1.3369 3.786 0.0 0 +M V30 11 O -2.6383 -2.9578 0.0 0 +M V30 12 N -0.0354 -2.9578 0.0 0 +M V30 13 O 2.5555 0.0354 0.0 0 +M V30 14 N 3.857 2.2834 0.0 0 +M V30 15 C -0.0473 4.5432 0.0 0 +M V30 16 C 5.1466 1.538 0.0 0 +M V30 17 C 6.4362 2.2952 0.0 0 +M V30 18 O 6.4243 3.8096 0.0 0 +M V30 19 N 7.7258 1.5499 0.0 0 +M V30 20 C 9.0154 2.3071 0.0 0 +M V30 21 C 10.305 1.5617 0.0 0 +M V30 22 C 9.0036 3.8215 0.0 0 +M V30 23 F 10.2932 0.0709 0.0 0 +M V30 24 C 11.5946 2.3189 0.0 0 +M V30 25 C 10.2932 4.5787 0.0 0 +M V30 26 F 12.8842 1.5735 0.0 0 +M V30 27 C 11.5828 3.8333 0.0 0 +M V30 28 F 12.8724 4.5905 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 3 1 CFG=3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 3 7 +M V30 7 1 4 8 +M V30 8 1 4 9 +M V30 9 2 5 10 +M V30 10 2 6 11 +M V30 11 1 6 12 +M V30 12 2 8 13 +M V30 13 1 8 14 +M V30 14 2 9 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 2 20 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 21 24 +M V30 24 2 22 25 +M V30 25 1 24 26 +M V30 26 2 24 27 +M V30 27 1 27 28 +M V30 28 1 10 15 +M V30 29 1 25 27 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 3) +M V30 END COLLECTION +M V30 END CTAB +M END +> +815 + +> +Z2010151393 + +> +411.398 + +> +0.452 + +> +3 + +> +101.290 + +> +7 + +> +DNA_pk + +> + + +> + + +$$$$ +Compound 816 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4912 0.0 0 +M V30 3 N -1.3255 -2.225 0.0 0 +M V30 4 C 1.2782 -2.225 0.0 0 +M V30 5 C -2.6393 -1.4676 0.0 0 +M V30 6 N 1.4202 -3.7045 0.0 0 +M V30 7 C 2.6393 -1.5978 0.0 0 +M V30 8 C -3.953 -2.2014 0.0 0 CFG=2 +M V30 9 C 2.876 -4.0004 0.0 0 +M V30 10 C 3.6335 -2.6985 0.0 0 +M V30 11 C -5.2668 -1.4439 0.0 0 +M V30 12 C -3.9649 -3.6927 0.0 0 +M V30 13 C 5.1129 -2.5328 0.0 0 +M V30 14 C -6.5805 -2.1777 0.0 0 +M V30 15 C -5.2786 -4.4265 0.0 0 +M V30 16 N 6.5924 -2.3671 0.0 0 +M V30 17 N -8.0245 -1.7043 0.0 0 +M V30 18 N -6.5924 -3.669 0.0 0 +M V30 19 C -8.924 -2.9115 0.0 0 +M V30 20 C -8.0363 -4.1187 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 4 7 +M V30 7 1 8 5 CFG=1 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 1 8 11 +M V30 11 1 8 12 +M V30 12 1 10 13 +M V30 13 1 11 14 +M V30 14 1 12 15 +M V30 15 3 13 16 +M V30 16 2 14 17 +M V30 17 1 14 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 2 9 10 +M V30 21 1 15 18 +M V30 22 2 19 20 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 8) +M V30 END COLLECTION +M V30 END CTAB +M END +> +816 + +> +Z1756483376 + +> +269.302 + +> +0.116 + +> +2 + +> +86.500 + +> +3 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 817 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2938 -0.7359 0.0 0 +M V30 3 N 2.5876 0.0237 0.0 0 +M V30 4 C 1.2819 -2.2315 0.0 0 +M V30 5 C 3.8814 -0.7121 0.0 0 +M V30 6 C 2.5757 -2.9793 0.0 0 CFG=2 +M V30 7 C 3.8695 -2.2077 0.0 0 +M V30 8 C 5.1752 0.0474 0.0 0 +M V30 9 C 2.5638 -4.4749 0.0 0 +M V30 10 C 5.1633 -2.9555 0.0 0 +M V30 11 C 6.469 -0.6884 0.0 0 +M V30 12 O 3.8577 -5.2227 0.0 0 +M V30 13 N 1.2463 -5.2227 0.0 0 +M V30 14 C 6.4572 -2.1959 0.0 0 +M V30 15 C 1.2344 -6.7183 0.0 0 +M V30 16 C 0.0 -7.5967 0.0 0 +M V30 17 C 2.4451 -7.5967 0.0 0 +M V30 18 O 0.451 -9.0211 0.0 0 +M V30 19 C -1.4481 -7.1219 0.0 0 +M V30 20 C 1.9703 -9.0211 0.0 0 +M V30 21 C 3.9051 -7.2762 0.0 0 +M V30 22 O -2.5757 -8.1189 0.0 0 +M V30 23 C -1.7686 -5.6381 0.0 0 +M V30 24 C 2.9674 -10.1249 0.0 0 +M V30 25 C 4.9022 -8.3801 0.0 0 +M V30 26 C 4.4274 -9.8045 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 6 9 CFG=3 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 13 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 1 17 21 +M V30 21 2 19 22 +M V30 22 1 19 23 +M V30 23 1 20 24 +M V30 24 2 21 25 +M V30 25 2 24 26 +M V30 26 1 6 7 +M V30 27 1 11 14 +M V30 28 1 18 20 +M V30 29 1 25 26 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 6) +M V30 END COLLECTION +M V30 END CTAB +M END +> +817 + +> +Z1779706998 + +> +348.352 + +> +2.704 + +> +2 + +> +88.410 + +> +3 + +> +ATM + +> + + +> + + +$$$$ +Compound 818 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O -0.6272 -1.361 0.0 0 +M V30 3 O -1.4438 0.4734 0.0 0 +M V30 4 C 0.2958 1.4793 0.0 0 +M V30 5 C 1.29 -0.7337 0.0 0 +M V30 6 C 1.7752 1.645 0.0 0 +M V30 7 C 2.3906 0.284 0.0 0 +M V30 8 N 2.5208 2.9587 0.0 0 +M V30 9 C 4.012 2.9705 0.0 0 +M V30 10 O 4.7576 1.6805 0.0 0 +M V30 11 C 4.7576 4.2842 0.0 0 +M V30 12 N 6.2488 4.2961 0.0 0 +M V30 13 C 3.9883 5.5979 0.0 0 +M V30 14 C 7.3021 5.373 0.0 0 +M V30 15 C 7.3021 3.2427 0.0 0 +M V30 16 C 8.3555 4.3197 0.0 0 +M V30 17 C 9.8467 4.3316 0.0 0 +M V30 18 N 10.7225 5.5624 0.0 0 +M V30 19 N 10.7225 3.1244 0.0 0 +M V30 20 C 12.1427 5.1127 0.0 0 +M V30 21 C 12.1427 3.5978 0.0 0 +M V30 22 C 13.4327 5.8819 0.0 0 +M V30 23 C 13.4327 2.8522 0.0 0 +M V30 24 C 14.7227 5.1482 0.0 0 +M V30 25 C 14.7227 3.6215 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 1 8 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 1 11 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 1 12 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 2 22 24 +M V30 24 2 23 25 +M V30 25 1 6 7 +M V30 26 1 15 16 +M V30 27 2 20 21 +M V30 28 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +818 + +> +Z1492357606 + +> +362.447 + +> +-0.371 + +> +2 + +> +95.160 + +> +4 + +> +ATM + +> + + +> + + +$$$$ +Compound 819 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3073 0.7655 0.0 0 +M V30 3 C 1.2837 0.7655 0.0 0 +M V30 4 N -2.6853 0.1648 0.0 0 +M V30 5 N -1.4722 2.2613 0.0 0 +M V30 6 C 2.5675 0.0235 0.0 0 +M V30 7 N -3.6982 1.2837 0.0 0 +M V30 8 C -0.4828 3.3802 0.0 0 +M V30 9 C -2.9444 2.5793 0.0 0 +M V30 10 N 3.8513 0.7891 0.0 0 +M V30 11 C -0.954 4.8171 0.0 0 +M V30 12 C 0.9657 3.0858 0.0 0 +M V30 13 N -3.4155 4.0162 0.0 0 +M V30 14 C 5.1351 0.0471 0.0 0 +M V30 15 C -2.4262 5.1351 0.0 0 +M V30 16 C 0.0353 5.936 0.0 0 +M V30 17 C 1.9551 4.2047 0.0 0 +M V30 18 C -4.8878 4.3342 0.0 0 +M V30 19 C 6.4189 0.8126 0.0 0 +M V30 20 C 5.1233 -1.4369 0.0 0 +M V30 21 O -2.8973 6.572 0.0 0 +M V30 22 C 1.484 5.6416 0.0 0 +M V30 23 C -5.3589 5.7711 0.0 0 +M V30 24 C -6.8311 6.0891 0.0 0 +M V30 25 O -7.3023 7.526 0.0 0 +M V30 26 C -8.7745 7.844 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 11 16 +M V30 16 2 12 17 +M V30 17 1 13 18 +M V30 18 1 14 19 +M V30 19 1 14 20 +M V30 20 2 15 21 +M V30 21 2 16 22 +M V30 22 1 18 23 +M V30 23 1 23 24 +M V30 24 1 24 25 +M V30 25 1 25 26 +M V30 26 2 7 9 +M V30 27 1 13 15 +M V30 28 1 17 22 +M V30 END BOND +M V30 END CTAB +M END +> +819 + +> +Z1981566717 + +> +375.488 + +> +1.614 + +> +1 + +> +72.280 + +> +9 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 820 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 N -1.4672 0.3169 0.0 0 CHG=1 +M V30 3 O -2.4767 -0.7747 0.0 0 CHG=-1 +M V30 4 C -1.9367 1.7489 0.0 0 +M V30 5 C -0.9507 2.864 0.0 0 +M V30 6 C -3.404 2.0658 0.0 0 +M V30 7 C -1.4203 4.2961 0.0 0 +M V30 8 C -3.8735 3.4979 0.0 0 +M V30 9 N -0.4343 5.4112 0.0 0 +M V30 10 C -2.8875 4.613 0.0 0 +M V30 11 C -0.9038 6.8432 0.0 0 +M V30 12 C -3.357 6.045 0.0 0 +M V30 13 O -2.371 7.1601 0.0 0 +M V30 14 N 0.0821 7.9583 0.0 0 +M V30 15 C -0.3873 9.3904 0.0 0 +M V30 16 N 0.4812 10.6111 0.0 0 +M V30 17 C -1.8193 9.8599 0.0 0 +M V30 18 N -0.4108 11.8319 0.0 0 +M V30 19 C -1.8311 11.3624 0.0 0 +M V30 20 C -3.2514 9.4138 0.0 0 +M V30 21 N -3.2631 11.8319 0.0 0 +M V30 22 N -4.1435 10.6346 0.0 0 +M V30 23 C -3.7209 8.0053 0.0 0 +M V30 24 C -3.7326 13.2639 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 1 7 9 +M V30 9 2 7 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 17 19 +M V30 19 1 17 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 1 20 23 +M V30 23 1 21 24 +M V30 24 1 8 10 +M V30 25 1 18 19 +M V30 26 1 21 22 +M V30 END BOND +M V30 END CTAB +M END +> +820 + +> +Z1913010981 + +> +329.314 + +> +1.835 + +> +3 + +> +130.770 + +> +3 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 821 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.5046 0.0117 0.0 0 +M V30 3 C 0.4467 1.4341 0.0 0 +M V30 4 N -1.9749 1.4459 0.0 0 +M V30 5 C -2.3981 -1.1872 0.0 0 +M V30 6 C -0.7758 2.3275 0.0 0 +M V30 7 C -0.7876 3.8322 0.0 0 +M V30 8 O 0.4937 4.5846 0.0 0 +M V30 9 C 0.4819 6.0892 0.0 0 +M V30 10 C 1.7633 6.8416 0.0 0 +M V30 11 C -0.8228 6.8416 0.0 0 +M V30 12 C 1.7515 8.3463 0.0 0 +M V30 13 C -0.8346 8.3463 0.0 0 +M V30 14 C 0.4467 9.0986 0.0 0 +M V30 15 C 0.4349 10.6033 0.0 0 +M V30 16 O 1.7162 11.3557 0.0 0 +M V30 17 N -0.8698 11.3557 0.0 0 +M V30 18 C -0.8816 12.8604 0.0 0 +M V30 19 C 0.3996 13.6127 0.0 0 +M V30 20 C -2.1865 13.6127 0.0 0 +M V30 21 C 0.3879 15.1174 0.0 0 +M V30 22 C 1.681 12.8721 0.0 0 +M V30 23 C -2.1982 15.1174 0.0 0 +M V30 24 C -0.9169 15.8697 0.0 0 +M V30 25 O -3.5031 15.8697 0.0 0 +M V30 26 O -0.9286 17.3744 0.0 0 +M V30 27 C -3.5148 17.3744 0.0 0 +M V30 28 C -2.2335 18.1268 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 6 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 2 11 13 +M V30 13 2 12 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 17 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 19 22 +M V30 22 2 20 23 +M V30 23 2 21 24 +M V30 24 1 23 25 +M V30 25 1 24 26 +M V30 26 1 25 27 +M V30 27 1 26 28 +M V30 28 1 4 6 +M V30 29 1 13 14 +M V30 30 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +821 + +> +Z95670656 + +> +398.475 + +> +2.954 + +> +1 + +> +69.680 + +> +7 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL549527 + +> +0.87 + +$$$$ +Compound 822 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 29 32 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -1.3047 0.7523 0.0 0 +M V30 3 C -2.6095 0.0235 0.0 0 +M V30 4 C -1.3165 2.2569 0.0 0 +M V30 5 C -3.9143 0.7758 0.0 0 +M V30 6 C -2.6213 3.0092 0.0 0 +M V30 7 C -3.9261 2.2804 0.0 0 +M V30 8 C -5.2309 3.0327 0.0 0 +M V30 9 O -5.2426 4.5373 0.0 0 +M V30 10 C -6.5474 5.3014 0.0 0 +M V30 11 C -6.5591 6.806 0.0 0 +M V30 12 C -7.8522 4.5608 0.0 0 +M V30 13 C -7.8639 7.5583 0.0 0 +M V30 14 C -9.157 5.3249 0.0 0 +M V30 15 N -7.8757 9.0629 0.0 0 +M V30 16 C -9.1687 6.8295 0.0 0 +M V30 17 C -9.1805 9.8152 0.0 0 +M V30 18 O -10.4853 9.0747 0.0 0 +M V30 19 C -9.1922 11.3199 0.0 0 +M V30 20 C -7.911 12.0722 0.0 0 +M V30 21 C -10.497 12.0722 0.0 0 +M V30 22 C -7.9227 13.5768 0.0 0 +M V30 23 C -6.6297 11.3434 0.0 0 +M V30 24 N -10.5088 13.5768 0.0 0 +M V30 25 C -9.2275 14.3291 0.0 0 +M V30 26 C -6.6414 14.3291 0.0 0 +M V30 27 C -5.3484 12.0957 0.0 0 +M V30 28 O -9.2393 15.8337 0.0 0 +M V30 29 C -5.3602 13.6003 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 1 9 10 +M V30 10 2 10 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 2 12 14 +M V30 14 1 13 15 +M V30 15 2 13 16 +M V30 16 1 15 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 2 19 21 +M V30 21 2 20 22 +M V30 22 1 20 23 +M V30 23 1 21 24 +M V30 24 1 22 25 +M V30 25 1 22 26 +M V30 26 2 23 27 +M V30 27 2 25 28 +M V30 28 2 26 29 +M V30 29 1 6 7 +M V30 30 1 14 16 +M V30 31 1 24 25 +M V30 32 1 27 29 +M V30 END BOND +M V30 END CTAB +M END +> +822 + +> +Z1815436119 + +> +388.391 + +> +3.789 + +> +2 + +> +67.430 + +> +5 + +> +parp14 + +> + + +> + + +$$$$ +Compound 823 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5069 0.0 0 +M V30 3 N -1.3185 2.2721 0.0 0 +M V30 4 C 1.2714 2.2721 0.0 0 +M V30 5 C -1.3303 3.779 0.0 0 +M V30 6 C 1.2596 3.779 0.0 0 +M V30 7 C 2.5547 1.5304 0.0 0 +M V30 8 N -0.047 4.5325 0.0 0 +M V30 9 N -2.6371 4.5325 0.0 0 +M V30 10 C 2.5429 4.5325 0.0 0 +M V30 11 C 3.8379 2.2957 0.0 0 +M V30 12 C -3.9439 3.7908 0.0 0 +M V30 13 C 3.8261 3.7908 0.0 0 +M V30 14 C -5.2507 4.5443 0.0 0 +M V30 15 C -6.5575 3.8026 0.0 0 +M V30 16 N -7.8642 4.5561 0.0 0 +M V30 17 C -9.2417 3.9556 0.0 0 +M V30 18 C -8.0291 6.0512 0.0 0 +M V30 19 N -10.2541 5.0741 0.0 0 +M V30 20 C -9.5595 2.5076 0.0 0 +M V30 21 C -9.5007 6.3691 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 1 16 18 +M V30 18 2 17 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 1 6 8 +M V30 22 1 11 13 +M V30 23 1 19 21 +M V30 END BOND +M V30 END CTAB +M END +> +823 + +> +Z1272450836 + +> +283.328 + +> +1.374 + +> +2 + +> +71.310 + +> +5 + +> +parp2 + +> + + +> + + +$$$$ +Compound 824 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 33 36 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O 0.7348 1.3155 0.0 0 +M V30 3 O -0.7585 -1.2918 0.0 0 +M V30 4 N -1.3155 0.7585 0.0 0 +M V30 5 C 1.2918 -0.7348 0.0 0 +M V30 6 C -2.6311 0.0237 0.0 0 +M V30 7 C 2.5837 0.0237 0.0 0 +M V30 8 C 1.28 -2.2282 0.0 0 +M V30 9 C -3.9467 0.7822 0.0 0 CFG=2 +M V30 10 C 3.8756 -0.7111 0.0 0 +M V30 11 C 2.5719 -2.9749 0.0 0 +M V30 12 O -4.1127 2.2874 0.0 0 +M V30 13 C -5.3334 0.1777 0.0 0 +M V30 14 C 5.1675 0.0474 0.0 0 +M V30 15 C 3.8638 -2.2045 0.0 0 +M V30 16 C -5.5942 2.6074 0.0 0 +M V30 17 C -6.3527 1.3037 0.0 0 +M V30 18 O 5.1557 1.5644 0.0 0 +M V30 19 N 6.4594 -0.6874 0.0 0 +M V30 20 C 7.7513 0.0711 0.0 0 +M V30 21 C 6.4476 -2.1808 0.0 0 +M V30 22 C 9.0432 -0.6637 0.0 0 +M V30 23 C 5.132 -2.9274 0.0 0 +M V30 24 N 10.3351 0.0948 0.0 0 +M V30 25 N 9.0313 -2.1571 0.0 0 +M V30 26 C 11.627 -0.64 0.0 0 +M V30 27 C 10.3232 -2.9037 0.0 0 +M V30 28 C 11.6151 -2.1333 0.0 0 +M V30 29 C 12.9189 0.1185 0.0 0 +M V30 30 O 10.3114 -4.3971 0.0 0 +M V30 31 C 12.907 -2.88 0.0 0 +M V30 32 C 14.2107 -0.6163 0.0 0 +M V30 33 C 14.1989 -2.1096 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 9 6 CFG=1 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 9 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 2 10 15 +M V30 15 1 12 16 +M V30 16 1 13 17 +M V30 17 2 14 18 +M V30 18 1 14 19 +M V30 19 1 19 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 2 22 24 +M V30 24 1 22 25 +M V30 25 1 24 26 +M V30 26 1 25 27 +M V30 27 2 26 28 +M V30 28 1 26 29 +M V30 29 2 27 30 +M V30 30 1 28 31 +M V30 31 2 29 32 +M V30 32 2 31 33 +M V30 33 1 11 15 +M V30 34 1 16 17 +M V30 35 1 27 28 +M V30 36 1 32 33 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 9) +M V30 END COLLECTION +M V30 END CTAB +M END +> +824 + +> +Z101492120 + +> +470.541 + +> +1.538 + +> +2 + +> +117.170 + +> +7 + +> +parp1 + +> + + +> + + +$$$$ +Compound 825 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 33 35 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.6277 -1.362 0.0 0 +M V30 3 C -1.1251 1.0185 0.0 0 +M V30 4 C -2.1319 -1.1962 0.0 0 +M V30 5 C 0.1065 -2.653 0.0 0 CFG=1 +M V30 6 C -2.4398 0.2842 0.0 0 +M V30 7 N 1.5989 -2.6412 0.0 0 +M V30 8 C -0.6514 -3.944 0.0 0 +M V30 9 C 2.3332 -3.9321 0.0 0 +M V30 10 C 0.0829 -5.235 0.0 0 +M V30 11 C -2.1674 -3.9321 0.0 0 +M V30 12 C 3.8256 -3.9203 0.0 0 +M V30 13 O 4.5717 -2.6056 0.0 0 +M V30 14 N 4.5717 -5.2113 0.0 0 +M V30 15 C 6.0641 -5.1995 0.0 0 +M V30 16 C 3.8019 -6.5023 0.0 0 +M V30 17 C 6.7984 -6.4904 0.0 0 +M V30 18 C 4.548 -7.7933 0.0 0 +M V30 19 N 6.0404 -7.7814 0.0 0 +M V30 20 C 6.7747 -9.0724 0.0 0 +M V30 21 C 6.0167 -10.3634 0.0 0 +M V30 22 O 6.751 -11.6544 0.0 0 +M V30 23 N 4.5007 -10.3516 0.0 0 +M V30 24 C 3.7308 -11.6426 0.0 0 +M V30 25 C 2.2148 -11.6307 0.0 0 +M V30 26 C 4.477 -12.9336 0.0 0 +M V30 27 C 1.4568 -12.9217 0.0 0 +M V30 28 C 1.4568 -10.3161 0.0 0 +M V30 29 C 3.7071 -14.2246 0.0 0 +M V30 30 C 2.1911 -14.2127 0.0 0 +M V30 31 F 0.6987 -9.0014 0.0 0 +M V30 32 F 2.7478 -9.558 0.0 0 +M V30 33 F 0.1421 -11.0504 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 5 7 CFG=1 +M V30 7 1 5 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 1 8 11 +M V30 11 1 9 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 1 20 21 +M V30 21 2 21 22 +M V30 22 1 21 23 +M V30 23 1 23 24 +M V30 24 2 24 25 +M V30 25 1 24 26 +M V30 26 1 25 27 +M V30 27 1 25 28 +M V30 28 2 26 29 +M V30 29 2 27 30 +M V30 30 1 28 31 +M V30 31 1 28 32 +M V30 32 1 28 33 +M V30 33 1 4 6 +M V30 34 1 18 19 +M V30 35 1 29 30 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 5) +M V30 END COLLECTION +M V30 END CTAB +M END +> +825 + +> +Z106903142 + +> +482.562 + +> +3.555 + +> +2 + +> +64.680 + +> +9 + +> +ATM + +> + + +> + + +$$$$ +Compound 826 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3137 0.7574 0.0 0 +M V30 3 C 1.29 0.7574 0.0 0 +M V30 4 C -2.6274 0.0236 0.0 0 +M V30 5 C -1.3255 2.2724 0.0 0 +M V30 6 C 2.5801 0.0236 0.0 0 +M V30 7 C -3.9412 0.7811 0.0 0 +M V30 8 C -2.6393 3.0298 0.0 0 +M V30 9 N 3.8701 0.7811 0.0 0 +M V30 10 N 2.5682 -1.4675 0.0 0 +M V30 11 O -5.2549 0.0473 0.0 0 +M V30 12 C -3.953 2.296 0.0 0 +M V30 13 C 5.1602 0.0473 0.0 0 +M V30 14 C 3.8583 -2.2132 0.0 0 +M V30 15 C -6.5686 0.8048 0.0 0 +M V30 16 C 5.1484 -1.4439 0.0 0 +M V30 17 C 6.4503 0.8048 0.0 0 +M V30 18 O 3.8465 -3.7044 0.0 0 +M V30 19 C 6.4384 -2.1895 0.0 0 +M V30 20 C 7.7403 0.071 0.0 0 +M V30 21 C 7.7285 -1.4202 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 7 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 2 13 16 +M V30 16 1 13 17 +M V30 17 2 14 18 +M V30 18 1 16 19 +M V30 19 2 17 20 +M V30 20 2 19 21 +M V30 21 1 8 12 +M V30 22 1 14 16 +M V30 23 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +826 + +> +Z290602952 + +> +298.360 + +> +2.385 + +> +1 + +> +50.690 + +> +4 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 827 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 30 33 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4949 0.0 0 +M V30 3 N 1.2813 -2.2305 0.0 0 +M V30 4 C -1.3288 -2.2305 0.0 0 +M V30 5 C 1.2695 -3.7255 0.0 0 +M V30 6 C -1.3407 -3.7255 0.0 0 +M V30 7 C -2.6458 -1.4712 0.0 0 +M V30 8 N -0.0474 -4.4611 0.0 0 +M V30 9 C 2.5627 -4.4611 0.0 0 +M V30 10 C -2.6576 -4.4611 0.0 0 +M V30 11 C -3.9627 -2.2068 0.0 0 +M V30 12 C 3.856 -3.7017 0.0 0 +M V30 13 C -3.9746 -3.7017 0.0 0 +M V30 14 C 5.1492 -4.4373 0.0 0 +M V30 15 O 5.1373 -5.9323 0.0 0 +M V30 16 N 6.4425 -3.678 0.0 0 +M V30 17 C 7.7357 -4.4136 0.0 0 +M V30 18 C 9.029 -3.6543 0.0 0 +M V30 19 C 10.3222 -4.3899 0.0 0 +M V30 20 C 9.0171 -2.1356 0.0 0 +M V30 21 C 11.6154 -3.6305 0.0 0 +M V30 22 C 10.3103 -1.3763 0.0 0 +M V30 23 O 12.9087 -4.3661 0.0 0 +M V30 24 N 11.6036 -2.1119 0.0 0 +M V30 25 C 14.2019 -3.6068 0.0 0 +M V30 26 C 14.1901 -2.0881 0.0 0 +M V30 27 C 15.4952 -4.3424 0.0 0 +M V30 28 C 15.4833 -1.3288 0.0 0 +M V30 29 C 16.7884 -3.5831 0.0 0 +M V30 30 C 16.7766 -2.0644 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 1 21 23 +M V30 23 2 21 24 +M V30 24 1 23 25 +M V30 25 2 25 26 +M V30 26 1 25 27 +M V30 27 1 26 28 +M V30 28 2 27 29 +M V30 29 2 28 30 +M V30 30 1 6 8 +M V30 31 1 11 13 +M V30 32 1 22 24 +M V30 33 1 29 30 +M V30 END BOND +M V30 END CTAB +M END +> +827 + +> +Z424311150 + +> +400.430 + +> +1.811 + +> +2 + +> +92.680 + +> +7 + +> +parp10 + +> + + +> + + +$$$$ +Compound 828 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3144 -0.7341 0.0 0 +M V30 3 N -1.3262 -2.2262 0.0 0 +M V30 4 C -2.6288 0.0236 0.0 0 +M V30 5 N -3.9433 -0.7105 0.0 0 +M V30 6 C -5.2577 0.0473 0.0 0 +M V30 7 C -3.9551 -2.2025 0.0 0 +M V30 8 C -6.5722 -0.6868 0.0 0 CFG=2 +M V30 9 O -7.8866 0.071 0.0 0 +M V30 10 C -6.584 -2.1788 0.0 0 +M V30 11 C -9.201 -0.6631 0.0 0 +M V30 12 N -7.8984 -2.9249 0.0 0 +M V30 13 C -9.2129 -2.1552 0.0 0 +M V30 14 C -7.9103 -4.4169 0.0 0 +M V30 15 N -6.6195 -5.1511 0.0 0 +M V30 16 N -9.2247 -5.1511 0.0 0 +M V30 17 C -6.6314 -6.6432 0.0 0 +M V30 18 C -9.2366 -6.6432 0.0 0 +M V30 19 O -5.3406 -7.3774 0.0 0 +M V30 20 C -7.9458 -7.3774 0.0 0 +M V30 21 C -10.551 -7.3774 0.0 0 +M V30 22 C -7.9576 -8.8695 0.0 0 +M V30 23 C -10.5628 -8.8695 0.0 0 +M V30 24 C -9.2721 -9.6037 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 4 5 +M V30 5 1 5 6 +M V30 6 1 5 7 +M V30 7 1 8 6 CFG=1 +M V30 8 1 8 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 2 14 16 +M V30 16 2 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 2 20 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 12 13 +M V30 25 1 18 20 +M V30 26 2 23 24 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 8) +M V30 END COLLECTION +M V30 END CTAB +M END +> +828 + +> +Z2010615568 + +> +331.370 + +> +1.210 + +> +2 + +> +104.810 + +> +5 + +> +parp10, Tankyrase1, parp2, parp3 + +> + + +> + + +$$$$ +Compound 829 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.5161 0.0 0 +M V30 3 C -1.3266 2.2742 0.0 0 +M V30 4 C 1.2792 2.2742 0.0 0 +M V30 5 C -2.6414 1.5398 0.0 0 +M V30 6 C -1.3384 3.7904 0.0 0 +M V30 7 C 1.2674 3.7904 0.0 0 +M V30 8 O -2.6532 0.0473 0.0 0 +M V30 9 N -3.9562 2.2979 0.0 0 +M V30 10 C -0.0473 4.5484 0.0 0 +M V30 11 N 2.5585 4.5484 0.0 0 +M V30 12 C 3.8496 3.814 0.0 0 +M V30 13 O 3.8377 2.3216 0.0 0 +M V30 14 N 5.1407 4.5721 0.0 0 +M V30 15 C 6.4318 3.8377 0.0 0 +M V30 16 C 7.7229 4.5958 0.0 0 +M V30 17 C 9.014 3.8614 0.0 0 +M V30 18 C 7.7111 6.112 0.0 0 +M V30 19 C 10.3051 4.6195 0.0 0 +M V30 20 C 9.0022 2.369 0.0 0 +M V30 21 C 9.0022 6.8701 0.0 0 +M V30 22 N 10.2933 6.1357 0.0 0 +M V30 23 C 11.5962 3.8851 0.0 0 +M V30 24 C 10.2933 1.6346 0.0 0 +M V30 25 C 11.5844 2.3926 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 7 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 2 16 18 +M V30 18 1 17 19 +M V30 19 2 17 20 +M V30 20 1 18 21 +M V30 21 1 19 22 +M V30 22 2 19 23 +M V30 23 1 20 24 +M V30 24 1 23 25 +M V30 25 1 7 10 +M V30 26 2 21 22 +M V30 27 2 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +829 + +> +Z1339215554 + +> +354.790 + +> +1.654 + +> +3 + +> +97.110 + +> +4 + +> +parp14 + +> + + +> + + +$$$$ +Compound 830 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O 1.2804 -0.7283 0.0 0 +M V30 3 O -0.7518 -1.2804 0.0 0 +M V30 4 N 0.74 1.3039 0.0 0 +M V30 5 C -1.3039 0.7518 0.0 0 +M V30 6 C -1.3156 2.2554 0.0 0 +M V30 7 C -2.6078 0.0234 0.0 0 +M V30 8 C -2.6196 3.019 0.0 0 +M V30 9 C -3.9118 0.7753 0.0 0 +M V30 10 C -3.9235 2.2789 0.0 0 +M V30 11 N -5.2275 3.0425 0.0 0 +M V30 12 C -6.5314 2.3024 0.0 0 +M V30 13 O -6.5432 0.8223 0.0 0 +M V30 14 C -7.8354 3.066 0.0 0 CFG=1 +M V30 15 S -9.1393 2.3259 0.0 0 +M V30 16 C -7.8471 4.5696 0.0 0 +M V30 17 C -10.4432 3.0895 0.0 0 +M V30 18 N -11.8177 2.4904 0.0 0 +M V30 19 N -10.6077 4.5814 0.0 0 +M V30 20 N -12.8279 3.6064 0.0 0 +M V30 21 C -12.0761 4.8985 0.0 0 +M V30 22 C -12.6987 6.273 0.0 0 +M V30 23 C -11.8294 7.4947 0.0 0 +M V30 24 C -14.1906 6.4374 0.0 0 +M V30 25 F -10.361 7.3537 0.0 0 +M V30 26 C -12.452 8.8691 0.0 0 +M V30 27 C -14.8132 7.8119 0.0 0 +M V30 28 C -13.9439 9.0336 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 2 5 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 2 8 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 CFG=1 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 21 22 +M V30 22 2 22 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 1 23 26 +M V30 26 2 24 27 +M V30 27 2 26 28 +M V30 28 1 9 10 +M V30 29 2 20 21 +M V30 30 1 27 28 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 14) +M V30 END COLLECTION +M V30 END CTAB +M END +> +830 + +> +Z17357088 + +> +421.469 + +> +1.520 + +> +3 + +> +130.830 + +> +6 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 831 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2918 0.7585 0.0 0 +M V30 3 N 2.5837 0.0118 0.0 0 +M V30 4 C 1.28 2.2755 0.0 0 +M V30 5 C 3.8755 0.7703 0.0 0 +M V30 6 O 2.5718 3.034 0.0 0 +M V30 7 C -0.0355 3.034 0.0 0 +M V30 8 C 3.8637 2.2874 0.0 0 +M V30 9 C 5.1674 0.0237 0.0 0 +M V30 10 C 5.1555 3.0459 0.0 0 +M V30 11 C 6.4592 0.7822 0.0 0 +M V30 12 C 6.4474 2.3111 0.0 0 +M V30 13 N 7.7511 0.0355 0.0 0 +M V30 14 C 7.7392 -1.4577 0.0 0 +M V30 15 O 6.4237 -2.2044 0.0 0 +M V30 16 N 9.0311 -2.2044 0.0 0 +M V30 17 C 9.0192 -3.6977 0.0 0 +M V30 18 C 10.323 -1.434 0.0 0 +M V30 19 C 10.3111 -4.4326 0.0 0 +M V30 20 C 11.6148 -2.1807 0.0 0 +M V30 21 N 10.2992 -5.9259 0.0 0 +M V30 22 C 11.603 -3.674 0.0 0 +M V30 23 C 11.5081 -6.8029 0.0 0 +M V30 24 C 9.0667 -6.8029 0.0 0 +M V30 25 C 11.0341 -8.2252 0.0 0 +M V30 26 C 9.517 -8.2252 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 2 10 12 +M V30 12 1 11 13 +M V30 13 1 13 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 19 22 +M V30 22 1 21 23 +M V30 23 1 21 24 +M V30 24 1 23 25 +M V30 25 1 24 26 +M V30 26 1 6 8 +M V30 27 1 11 12 +M V30 28 1 20 22 +M V30 29 1 25 26 +M V30 END BOND +M V30 END CTAB +M END +> +831 + +> +Z594856436 + +> +358.435 + +> +1.327 + +> +2 + +> +73.910 + +> +2 + +> +parp3 + +> + + +> + + +$$$$ +Compound 832 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 29 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O -0.7694 -1.2903 0.0 0 +M V30 3 O 0.7458 1.314 0.0 0 +M V30 4 N -1.314 0.7694 0.0 0 +M V30 5 C 1.2903 -0.7339 0.0 0 +M V30 6 C -2.6281 0.0236 0.0 0 +M V30 7 C 2.5807 0.0236 0.0 0 +M V30 8 C 1.2785 -2.2256 0.0 0 +M V30 9 C -3.9422 0.7931 0.0 0 +M V30 10 C -2.6399 -1.4679 0.0 0 +M V30 11 C 3.8711 -0.7103 0.0 0 +M V30 12 C 2.5689 -2.9596 0.0 0 +M V30 13 C -5.2562 0.0473 0.0 0 +M V30 14 C -3.954 -2.2137 0.0 0 +M V30 15 C 3.8593 -2.2019 0.0 0 +M V30 16 C -5.2681 -1.4442 0.0 0 +M V30 17 C -6.5821 -2.1901 0.0 0 +M V30 18 O -7.8962 -1.4324 0.0 0 +M V30 19 N -6.594 -3.6817 0.0 0 +M V30 20 C -7.908 -4.4157 0.0 0 +M V30 21 C -5.3036 -4.4157 0.0 0 +M V30 22 C -7.9199 -5.9073 0.0 0 +M V30 23 C -9.2221 -3.658 0.0 0 +M V30 24 C -5.3154 -5.9073 0.0 0 +M V30 25 C -4.025 -6.6413 0.0 0 +M V30 26 C -6.6295 -6.6413 0.0 0 +M V30 27 C -4.0369 -8.133 0.0 0 +M V30 28 C -6.6413 -8.133 0.0 0 +M V30 29 C -5.3509 -8.8669 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 2 11 15 +M V30 15 2 13 16 +M V30 16 1 16 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 19 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 20 23 +M V30 23 1 21 24 +M V30 24 2 24 25 +M V30 25 1 24 26 +M V30 26 1 25 27 +M V30 27 2 26 28 +M V30 28 2 27 29 +M V30 29 1 12 15 +M V30 30 1 14 16 +M V30 31 1 28 29 +M V30 END BOND +M V30 END CTAB +M END +> +832 + +> +Z594991404 + +> +408.513 + +> +4.428 + +> +1 + +> +66.480 + +> +6 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL3355482 + +> +0.94 + +$$$$ +Compound 833 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.7573 1.3135 0.0 0 +M V30 3 N -0.0236 2.6271 0.0 0 +M V30 4 C -2.2721 1.3253 0.0 0 +M V30 5 C 1.4674 2.6389 0.0 0 +M V30 6 C -3.0294 0.0355 0.0 0 +M V30 7 N 2.3431 3.8696 0.0 0 +M V30 8 N 2.3431 1.4319 0.0 0 +M V30 9 C -4.5442 0.0473 0.0 0 +M V30 10 C -2.2957 -1.2543 0.0 0 +M V30 11 C 3.7631 3.4199 0.0 0 +M V30 12 C 3.7631 1.9052 0.0 0 +M V30 13 C -5.3015 -1.2425 0.0 0 +M V30 14 C -3.0531 -2.5442 0.0 0 +M V30 15 C 5.053 4.1892 0.0 0 +M V30 16 C 5.053 1.1715 0.0 0 +M V30 17 O -6.8163 -1.2307 0.0 0 +M V30 18 C -4.5678 -2.5324 0.0 0 +M V30 19 C 6.3429 3.4436 0.0 0 +M V30 20 C 6.3429 1.9289 0.0 0 +M V30 21 C -7.5736 -2.5206 0.0 0 +M V30 22 O -5.3252 -3.8223 0.0 0 +M V30 23 C -6.8281 -3.8105 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 1 13 17 +M V30 17 2 13 18 +M V30 18 2 15 19 +M V30 19 2 16 20 +M V30 20 1 17 21 +M V30 21 1 18 22 +M V30 22 1 21 23 +M V30 23 2 11 12 +M V30 24 1 14 18 +M V30 25 1 19 20 +M V30 26 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +833 + +> +Z27664977 + +> +309.319 + +> +2.874 + +> +2 + +> +76.240 + +> +3 + +> +DNA_pk + +> + + +> + + +$$$$ +Compound 834 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O 1.2803 -0.7282 0.0 0 +M V30 3 O -0.7517 -1.2803 0.0 0 +M V30 4 N -1.3038 0.7635 0.0 0 +M V30 5 C 0.74 1.3038 0.0 0 +M V30 6 C -1.3156 2.267 0.0 0 CFG=2 +M V30 7 C -2.6194 3.0306 0.0 0 +M V30 8 C -0.0352 3.0306 0.0 0 +M V30 9 N -2.6312 4.5341 0.0 0 +M V30 10 C -0.0469 4.5341 0.0 0 +M V30 11 C -3.935 5.2859 0.0 0 +M V30 12 C -1.3508 5.2859 0.0 0 +M V30 13 O -5.2389 4.5576 0.0 0 +M V30 14 N -3.9468 6.7895 0.0 0 +M V30 15 C -5.2507 7.5412 0.0 0 +M V30 16 C -5.2624 9.0448 0.0 0 +M V30 17 C -6.5545 6.8129 0.0 0 +M V30 18 C -6.5663 9.7966 0.0 0 +M V30 19 C -7.8584 7.5647 0.0 0 +M V30 20 C -7.8701 9.0683 0.0 0 +M V30 21 C -9.174 9.8201 0.0 0 +M V30 22 N -9.1858 11.3236 0.0 0 +M V30 23 C -10.4779 9.0918 0.0 0 +M V30 24 C -10.4896 12.0871 0.0 0 +M V30 25 C -11.7817 9.8436 0.0 0 +M V30 26 N -11.7935 11.3471 0.0 0 +M V30 27 C -10.5014 13.5907 0.0 0 +M V30 28 O -13.0856 9.1153 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 6 4 CFG=3 +M V30 6 1 6 7 +M V30 7 1 6 8 +M V30 8 1 7 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 1 9 12 +M V30 12 2 11 13 +M V30 13 1 11 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 17 19 +M V30 19 2 18 20 +M V30 20 1 20 21 +M V30 21 1 21 22 +M V30 22 2 21 23 +M V30 23 2 22 24 +M V30 24 1 23 25 +M V30 25 1 24 26 +M V30 26 1 24 27 +M V30 27 2 25 28 +M V30 28 1 10 12 +M V30 29 1 19 20 +M V30 30 1 25 26 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 6) +M V30 END COLLECTION +M V30 END CTAB +M END +> +834 + +> +Z595488312 + +> +405.471 + +> +0.269 + +> +3 + +> +119.970 + +> +3 + +> +parp14 + +> + + +> + + +$$$$ +Compound 835 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2944 0.76 0.0 0 +M V30 3 N 1.2825 2.2801 0.0 0 +M V30 4 C 2.5888 0.0237 0.0 0 +M V30 5 C 2.577 3.052 0.0 0 +M V30 6 C 3.8833 0.7837 0.0 0 +M V30 7 N 3.8714 2.3038 0.0 0 +M V30 8 C 2.5651 4.572 0.0 0 +M V30 9 C 5.1777 0.0475 0.0 0 +M V30 10 C 6.4721 0.8075 0.0 0 +M V30 11 C 5.1658 -1.4488 0.0 0 +M V30 12 C 7.7666 0.0712 0.0 0 +M V30 13 C 6.4603 -2.1851 0.0 0 +M V30 14 C 7.7547 -1.425 0.0 0 +M V30 15 N 9.0491 -2.1613 0.0 0 +M V30 16 C 9.0373 -3.6576 0.0 0 +M V30 17 O 7.7191 -4.3939 0.0 0 +M V30 18 N 10.3317 -4.3939 0.0 0 +M V30 19 C 10.3198 -5.8902 0.0 0 +M V30 20 C 11.6261 -3.6339 0.0 0 +M V30 21 C 11.6143 -6.6265 0.0 0 CFG=1 +M V30 22 C 12.9206 -4.3702 0.0 0 +M V30 23 N 11.6024 -8.1228 0.0 0 +M V30 24 C 12.9087 -5.8665 0.0 0 +M V30 25 C 12.8137 -9.0016 0.0 0 +M V30 26 C 10.3673 -9.0016 0.0 0 +M V30 27 C 12.3387 -10.4267 0.0 0 +M V30 28 C 10.8186 -10.4267 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 2 11 13 +M V30 13 2 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 1 21 23 CFG=3 +M V30 23 1 21 24 +M V30 24 1 23 25 +M V30 25 1 23 26 +M V30 26 1 25 27 +M V30 27 1 26 28 +M V30 28 1 6 7 +M V30 29 1 13 14 +M V30 30 1 22 24 +M V30 31 1 27 28 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 21) +M V30 END COLLECTION +M V30 END CTAB +M END +> +835 + +> +Z595488380 + +> +381.471 + +> +1.403 + +> +2 + +> +77.040 + +> +3 + +> +parp14 + +> + + +> + + +$$$$ +Compound 836 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 32 36 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3103 0.7555 0.0 0 +M V30 3 C 1.2867 0.7555 0.0 0 CFG=2 +M V30 4 N -2.6915 0.1534 0.0 0 +M V30 5 N -1.4756 2.2547 0.0 0 +M V30 6 C 2.5734 0.0236 0.0 0 +M V30 7 C 1.2749 2.2665 0.0 0 +M V30 8 N -3.7067 1.2749 0.0 0 +M V30 9 C -2.9512 2.5734 0.0 0 +M V30 10 C -0.3777 3.2699 0.0 0 +M V30 11 N 3.8601 0.7791 0.0 0 +M V30 12 N 2.5616 -1.4638 0.0 0 +M V30 13 C -3.5768 3.9546 0.0 0 +M V30 14 C -0.6964 4.7455 0.0 0 +M V30 15 C 5.1469 0.0472 0.0 0 +M V30 16 C 3.8483 -2.2075 0.0 0 +M V30 17 N -5.076 4.1198 0.0 0 +M V30 18 C -2.1366 5.2177 0.0 0 +M V30 19 C 0.4013 5.7607 0.0 0 +M V30 20 C 5.1351 -1.4401 0.0 0 +M V30 21 C 6.4336 0.8027 0.0 0 +M V30 22 O 3.8365 -3.6949 0.0 0 +M V30 23 C -6.0913 3.022 0.0 0 +M V30 24 C -5.8434 5.4302 0.0 0 +M V30 25 C -2.4554 6.6933 0.0 0 +M V30 26 C 0.0826 7.2363 0.0 0 +M V30 27 C 6.4218 -2.1838 0.0 0 +M V30 28 C 7.7203 0.0708 0.0 0 +M V30 29 C -7.4724 3.6476 0.0 0 +M V30 30 C -7.319 5.1351 0.0 0 +M V30 31 C -1.3575 7.7085 0.0 0 +M V30 32 C 7.7085 -1.4165 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 3 1 CFG=1 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 3 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 1 5 10 +M V30 10 2 6 11 +M V30 11 1 6 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 1 13 17 +M V30 17 2 14 18 +M V30 18 1 14 19 +M V30 19 2 15 20 +M V30 20 1 15 21 +M V30 21 2 16 22 +M V30 22 1 17 23 +M V30 23 1 17 24 +M V30 24 1 18 25 +M V30 25 2 19 26 +M V30 26 1 20 27 +M V30 27 2 21 28 +M V30 28 1 23 29 +M V30 29 1 24 30 +M V30 30 2 25 31 +M V30 31 2 27 32 +M V30 32 2 8 9 +M V30 33 1 16 20 +M V30 34 1 26 31 +M V30 35 1 28 32 +M V30 36 1 29 30 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 3) +M V30 END COLLECTION +M V30 END CTAB +M END +> +836 + +> +Z17436133 + +> +446.568 + +> +1.775 + +> +1 + +> +75.410 + +> +7 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 837 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 29 33 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.0122 1.1181 0.0 0 +M V30 3 C 1.3535 0.6238 0.0 0 +M V30 4 C -2.507 0.9769 0.0 0 +M V30 5 C -0.2824 2.4246 0.0 0 +M V30 6 C 1.1887 2.1186 0.0 0 +M V30 7 C 2.7071 0.0235 0.0 0 +M V30 8 N -3.2603 -0.306 0.0 0 +M V30 9 N -3.5192 2.095 0.0 0 +M V30 10 C 2.3893 3.0131 0.0 0 +M V30 11 C 3.9076 0.918 0.0 0 +M V30 12 N -4.7316 0.0117 0.0 0 +M V30 13 C -4.8963 1.4948 0.0 0 +M V30 14 C -3.225 3.5663 0.0 0 +M V30 15 C 3.7429 2.4128 0.0 0 CFG=2 +M V30 16 S -6.2028 2.2481 0.0 0 +M V30 17 C 4.9434 3.3074 0.0 0 +M V30 18 C -7.5093 1.5183 0.0 0 +M V30 19 C 6.297 2.7071 0.0 0 +M V30 20 C -8.8158 2.2716 0.0 0 +M V30 21 N -10.1223 1.5418 0.0 0 +M V30 22 N -8.8276 3.7782 0.0 0 +M V30 23 C -11.4288 2.2951 0.0 0 +M V30 24 C -10.1341 4.5315 0.0 0 +M V30 25 C -11.4406 3.8017 0.0 0 +M V30 26 C -12.8647 1.8479 0.0 0 +M V30 27 O -10.1458 6.038 0.0 0 +M V30 28 S -12.8765 4.2725 0.0 0 +M V30 29 C -13.7593 3.072 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 2 4 8 +M V30 8 1 4 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 1 9 14 +M V30 14 1 10 15 +M V30 15 1 13 16 +M V30 16 1 15 17 CFG=1 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 2 20 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 2 23 25 +M V30 25 1 23 26 +M V30 26 2 24 27 +M V30 27 1 25 28 +M V30 28 2 26 29 +M V30 29 1 5 6 +M V30 30 1 11 15 +M V30 31 2 12 13 +M V30 32 1 24 25 +M V30 33 1 28 29 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 15) +M V30 END COLLECTION +M V30 END CTAB +M END +> +837 + +> +Z17460152 + +> +443.609 + +> +3.918 + +> +1 + +> +72.170 + +> +5 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 838 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -1.302 0.7507 0.0 0 +M V30 3 F -2.0528 -0.5278 0.0 0 +M V30 4 F -0.563 2.0528 0.0 0 +M V30 5 C -2.6041 1.5014 0.0 0 +M V30 6 C -2.6158 3.0029 0.0 0 +M V30 7 C -3.9062 0.7742 0.0 0 +M V30 8 C -3.9179 3.7654 0.0 0 +M V30 9 C -5.2083 1.5249 0.0 0 +M V30 10 C -5.22 3.0264 0.0 0 +M V30 11 N -6.5221 3.7889 0.0 0 +M V30 12 C -7.8242 3.0499 0.0 0 +M V30 13 C -9.1263 3.8124 0.0 0 +M V30 14 O -9.138 5.3139 0.0 0 +M V30 15 N -10.4283 3.0733 0.0 0 +M V30 16 C -11.7304 3.8358 0.0 0 +M V30 17 C -13.0325 3.0968 0.0 0 +M V30 18 C -11.7421 5.3373 0.0 0 +M V30 19 C -14.3346 3.8593 0.0 0 +M V30 20 C -13.0442 6.0881 0.0 0 +M V30 21 C -15.6367 3.1203 0.0 0 +M V30 22 C -14.3463 5.3608 0.0 0 +M V30 23 O -15.6484 1.6422 0.0 0 +M V30 24 C -16.9387 3.8827 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 2 5 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 2 8 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 1 19 21 +M V30 21 2 19 22 +M V30 22 2 21 23 +M V30 23 1 21 24 +M V30 24 1 9 10 +M V30 25 1 20 22 +M V30 END BOND +M V30 END CTAB +M END +> +838 + +> +Z30984288 + +> +336.308 + +> +3.712 + +> +2 + +> +58.200 + +> +6 + +> +parp14 + +> + + +> + + +$$$$ +Compound 839 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.7527 -1.282 0.0 0 +M V30 3 C -1.0115 1.1174 0.0 0 +M V30 4 N -2.223 -0.9645 0.0 0 +M V30 5 C -0.1529 -2.6347 0.0 0 +M V30 6 C -2.3877 0.5175 0.0 0 +M V30 7 C -1.0468 -3.8345 0.0 0 +M V30 8 C -3.6933 1.282 0.0 0 +M V30 9 C -0.4469 -5.1871 0.0 0 +M V30 10 C -2.5406 -3.6698 0.0 0 +M V30 11 C -4.999 0.541 0.0 0 +M V30 12 C -3.7051 2.7876 0.0 0 +M V30 13 C -1.3409 -6.3869 0.0 0 +M V30 14 C -3.4346 -4.8696 0.0 0 +M V30 15 C -6.3046 1.3056 0.0 0 +M V30 16 C -5.0107 3.5522 0.0 0 +M V30 17 C -2.8347 -6.2222 0.0 0 +M V30 18 N -7.7396 0.8586 0.0 0 +M V30 19 C -6.3163 2.8112 0.0 0 +M V30 20 C -8.6335 2.0819 0.0 0 +M V30 21 N -7.7513 3.2816 0.0 0 +M V30 22 O -10.1391 2.0936 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 2 10 14 +M V30 14 1 11 15 +M V30 15 2 12 16 +M V30 16 2 13 17 +M V30 17 1 15 18 +M V30 18 2 15 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 1 4 6 +M V30 23 1 14 17 +M V30 24 1 16 19 +M V30 25 1 20 21 +M V30 END BOND +M V30 END CTAB +M END +> +839 + +> +Z118608364 + +> +307.370 + +> +3.637 + +> +2 + +> +54.020 + +> +3 + +> +parp14 + +> + + +> + + +$$$$ +Compound 840 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 18 20 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -1.3065 0.7533 0.0 0 +M V30 3 C -1.3182 2.2599 0.0 0 +M V30 4 C -2.613 0.0235 0.0 0 +M V30 5 N -0.0353 3.025 0.0 0 +M V30 6 C -2.6248 3.025 0.0 0 +M V30 7 C -3.9195 0.7768 0.0 0 +M V30 8 C -0.047 4.5316 0.0 0 +M V30 9 C -3.9313 2.2834 0.0 0 +M V30 10 N -1.3536 5.2849 0.0 0 +M V30 11 C 1.2358 5.2849 0.0 0 +M V30 12 C -1.3653 6.7915 0.0 0 +M V30 13 C 1.2241 6.7915 0.0 0 +M V30 14 C 2.5188 4.5434 0.0 0 +M V30 15 N -0.0823 7.5448 0.0 0 +M V30 16 C 2.5071 7.5448 0.0 0 +M V30 17 C 3.8018 5.2967 0.0 0 +M V30 18 C 3.79 6.8151 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 2 8 10 +M V30 10 1 8 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 2 11 14 +M V30 14 2 12 15 +M V30 15 2 13 16 +M V30 16 1 14 17 +M V30 17 1 16 18 +M V30 18 1 7 9 +M V30 19 1 13 15 +M V30 20 2 17 18 +M V30 END BOND +M V30 END CTAB +M END +> +840 + +> +Z62503121 + +> +239.248 + +> +4.042 + +> +1 + +> +37.810 + +> +2 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL289959 + +> +0.85 + +$$$$ +Compound 841 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 17 18 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2868 0.7555 0.0 0 +M V30 3 N 2.5736 0.0236 0.0 0 +M V30 4 N 1.2749 2.2666 0.0 0 +M V30 5 C 2.5617 -1.4638 0.0 0 +M V30 6 C -0.0354 3.0222 0.0 0 +M V30 7 C 1.2513 -2.2076 0.0 0 +M V30 8 C 3.8486 -2.2076 0.0 0 +M V30 9 C -1.2868 2.184 0.0 0 +M V30 10 C 0.059 4.5333 0.0 0 +M V30 11 N 1.2395 -3.6951 0.0 0 +M V30 12 C 3.8367 -3.6951 0.0 0 +M V30 13 C -2.727 2.6326 0.0 0 +M V30 14 C -1.0506 5.5604 0.0 0 +M V30 15 C 2.5263 -4.427 0.0 0 +M V30 16 C -3.2819 4.0374 0.0 0 +M V30 17 C -2.5499 5.3479 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 2 11 15 +M V30 15 1 13 16 +M V30 16 1 14 17 +M V30 17 1 12 15 +M V30 18 1 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +841 + +> +Z166730522 + +> +233.309 + +> +3.166 + +> +2 + +> +54.020 + +> +2 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL1998193 + +> +0.86 + +$$$$ +Compound 842 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.472 -0.2849 0.0 0 +M V30 3 C -0.7479 -1.3058 0.0 0 +M V30 4 N 2.671 0.6291 0.0 0 +M V30 5 C 1.6382 -1.7688 0.0 0 +M V30 6 C 0.2611 -2.398 0.0 0 +M V30 7 C -2.2318 -1.6263 0.0 0 +M V30 8 C 4.0481 0.0474 0.0 0 +M V30 9 C 3.0153 -2.3505 0.0 0 +M V30 10 C -0.1899 -3.8226 0.0 0 +M V30 11 C -2.6829 -3.0509 0.0 0 CFG=2 +M V30 12 O 5.2472 0.9615 0.0 0 +M V30 13 N 4.2143 -1.4364 0.0 0 +M V30 14 N 3.4902 -3.7632 0.0 0 +M V30 15 C -1.6738 -4.1431 0.0 0 +M V30 16 C -4.1669 -3.3715 0.0 0 +M V30 17 N 5.4371 -2.2912 0.0 0 +M V30 18 C 4.986 -3.7276 0.0 0 +M V30 19 C 5.8763 -4.9266 0.0 0 +M V30 20 C 7.3603 -4.7367 0.0 0 +M V30 21 C 5.2709 -6.3037 0.0 0 +M V30 22 C 8.2507 -5.9357 0.0 0 +M V30 23 C 6.1613 -7.5027 0.0 0 +M V30 24 C 7.6452 -7.3128 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 10 15 +M V30 15 1 11 16 CFG=3 +M V30 16 1 13 17 +M V30 17 1 14 18 +M V30 18 1 18 19 +M V30 19 2 19 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 2 21 23 +M V30 23 2 22 24 +M V30 24 1 5 6 +M V30 25 1 9 13 +M V30 26 1 11 15 +M V30 27 2 17 18 +M V30 28 1 23 24 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 11) +M V30 END COLLECTION +M V30 END CTAB +M END +> +842 + +> +Z55410908 + +> +336.411 + +> +2.908 + +> +1 + +> +59.810 + +> +1 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL2007592 + +> +0.98 + +$$$$ +Compound 843 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.4669 0.3403 0.0 0 +M V30 3 C 0.751 1.2908 0.0 0 +M V30 4 N -2.6991 -0.5046 0.0 0 +M V30 5 C -1.6077 1.8424 0.0 0 +M V30 6 C -0.2347 2.4291 0.0 0 +M V30 7 C 2.2296 1.373 0.0 0 +M V30 8 C -4.0603 0.1408 0.0 0 +M V30 9 C -2.969 2.4878 0.0 0 +M V30 10 C 0.0234 3.9078 0.0 0 +M V30 11 C 3.0981 2.5934 0.0 0 +M V30 12 O -5.2925 -0.7041 0.0 0 +M V30 13 N -4.2012 1.6429 0.0 0 +M V30 14 N -3.4149 3.9195 0.0 0 +M V30 15 C 1.326 4.6236 0.0 0 +M V30 16 C 2.6873 4.0369 0.0 0 +M V30 17 N -5.4099 2.5465 0.0 0 +M V30 18 C -4.917 3.9547 0.0 0 +M V30 19 C -5.7854 5.1869 0.0 0 +M V30 20 C -5.1635 6.5482 0.0 0 +M V30 21 C -7.2875 5.0696 0.0 0 +M V30 22 C -6.0319 7.7804 0.0 0 +M V30 23 C -8.1559 6.3018 0.0 0 +M V30 24 C -7.534 7.6631 0.0 0 +M V30 25 F -8.4024 8.8953 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 1 2 4 +M V30 4 2 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 1 10 15 +M V30 15 1 11 16 +M V30 16 1 13 17 +M V30 17 1 14 18 +M V30 18 1 18 19 +M V30 19 2 19 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 2 21 23 +M V30 23 2 22 24 +M V30 24 1 24 25 +M V30 25 1 5 6 +M V30 26 1 9 13 +M V30 27 1 15 16 +M V30 28 2 17 18 +M V30 29 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +843 + +> +Z55411176 + +> +354.401 + +> +3.098 + +> +1 + +> +59.810 + +> +1 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL2007592 + +> +0.95 + +$$$$ +Compound 844 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 30 33 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4488 -1.4174 0.0 0 +M V30 3 N -0.4488 -2.6222 0.0 0 +M V30 4 C 1.8662 -1.8662 0.0 0 +M V30 5 C 0.4252 -3.827 0.0 0 +M V30 6 C -1.9607 -2.6103 0.0 0 +M V30 7 C 1.8544 -3.3545 0.0 0 +M V30 8 C 3.1537 -1.1103 0.0 0 +M V30 9 O -0.0472 -5.2444 0.0 0 +M V30 10 C 3.1419 -4.0868 0.0 0 +M V30 11 C 4.4412 -1.8426 0.0 0 +M V30 12 C 4.4294 -3.3309 0.0 0 +M V30 13 C 5.7287 -1.0866 0.0 0 +M V30 14 O 7.0161 -1.819 0.0 0 +M V30 15 N 5.7168 0.4252 0.0 0 +M V30 16 C 7.0043 1.1811 0.0 0 +M V30 17 C 6.9925 2.693 0.0 0 +M V30 18 C 5.6814 3.449 0.0 0 +M V30 19 C 8.28 3.449 0.0 0 +M V30 20 C 5.6696 4.9609 0.0 0 +M V30 21 C 4.3703 2.7167 0.0 0 +M V30 22 C 8.2682 4.9609 0.0 0 +M V30 23 C 6.9571 5.7168 0.0 0 +M V30 24 N 3.0592 3.4726 0.0 0 +M V30 25 C 1.7481 2.7403 0.0 0 +M V30 26 C 3.0474 4.9845 0.0 0 +M V30 27 C 0.437 3.4962 0.0 0 CFG=2 +M V30 28 C 1.7363 5.7405 0.0 0 +M V30 29 C 0.4252 5.0081 0.0 0 +M V30 30 C -0.874 2.7639 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 2 10 12 +M V30 12 1 11 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 16 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 2 20 23 +M V30 23 1 21 24 +M V30 24 1 24 25 +M V30 25 1 24 26 +M V30 26 1 25 27 +M V30 27 1 26 28 +M V30 28 1 27 29 +M V30 29 1 27 30 CFG=1 +M V30 30 1 5 7 +M V30 31 1 11 12 +M V30 32 1 22 23 +M V30 33 1 28 29 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 27) +M V30 END COLLECTION +M V30 END CTAB +M END +> +844 + +> +Z424498332 + +> +405.489 + +> +4.151 + +> +1 + +> +69.720 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL598088 + +> +0.89 + +$$$$ +Compound 845 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4959 0.0 0 +M V30 3 C 1.2822 -2.232 0.0 0 +M V30 4 C -1.3297 -2.232 0.0 0 +M V30 5 C 1.2703 -3.7279 0.0 0 +M V30 6 C -1.3415 -3.7279 0.0 0 +M V30 7 C -0.0474 -4.4758 0.0 0 +M V30 8 C 2.5644 -4.4758 0.0 0 +M V30 9 C 3.8585 -3.7041 0.0 0 CFG=1 +M V30 10 N 5.1525 -4.4521 0.0 0 +M V30 11 C 3.8466 -2.1845 0.0 0 +M V30 12 C 6.4466 -3.6804 0.0 0 +M V30 13 O 6.4348 -2.1607 0.0 0 +M V30 14 C 7.7407 -4.4283 0.0 0 +M V30 15 C 9.0348 -3.6566 0.0 0 +M V30 16 C 10.3289 -4.4046 0.0 0 +M V30 17 N 11.623 -3.6329 0.0 0 +M V30 18 N 10.317 -5.9005 0.0 0 +M V30 19 C 12.9171 -4.3808 0.0 0 +M V30 20 C 11.6111 -6.6366 0.0 0 +M V30 21 C 12.9052 -5.8768 0.0 0 +M V30 22 C 14.2111 -3.6091 0.0 0 +M V30 23 O 11.5992 -8.1325 0.0 0 +M V30 24 C 14.1993 -6.6128 0.0 0 +M V30 25 C 15.5052 -4.3571 0.0 0 +M V30 26 C 15.4934 -5.853 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 8 9 +M V30 9 1 9 10 CFG=3 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 1 19 22 +M V30 22 2 20 23 +M V30 23 1 21 24 +M V30 24 2 22 25 +M V30 25 2 24 26 +M V30 26 1 6 7 +M V30 27 1 20 21 +M V30 28 1 25 26 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 9) +M V30 END COLLECTION +M V30 END CTAB +M END +> +845 + +> +Z424623072 + +> +369.845 + +> +2.301 + +> +2 + +> +70.560 + +> +6 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3092553 + +> +0.87 + +$$$$ +Compound 846 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.3153 -0.7347 0.0 0 +M V30 3 C -1.3272 -2.2278 0.0 0 +M V30 4 C -2.6307 0.0237 0.0 0 +M V30 5 C -2.6426 -2.9744 0.0 0 +M V30 6 C -0.0355 -2.9744 0.0 0 +M V30 7 C -3.9461 -0.711 0.0 0 +M V30 8 C -3.9579 -2.2041 0.0 0 +M V30 9 C 1.2561 -2.2041 0.0 0 CFG=1 +M V30 10 N 2.5478 -2.9507 0.0 0 +M V30 11 C 1.2442 -0.6873 0.0 0 +M V30 12 C 3.8394 -2.1804 0.0 0 +M V30 13 O 3.8276 -0.6636 0.0 0 +M V30 14 C 5.1311 -2.927 0.0 0 +M V30 15 C 6.4228 -2.1567 0.0 0 +M V30 16 C 7.7145 -2.9033 0.0 0 +M V30 17 N 9.0061 -2.133 0.0 0 +M V30 18 N 7.7026 -4.3964 0.0 0 +M V30 19 C 10.2978 -2.8796 0.0 0 +M V30 20 C 8.9943 -5.1311 0.0 0 +M V30 21 C 10.286 -4.3727 0.0 0 +M V30 22 C 11.5895 -2.1093 0.0 0 +M V30 23 O 8.9824 -6.6242 0.0 0 +M V30 24 C 11.5776 -5.1074 0.0 0 +M V30 25 C 12.8812 -2.8559 0.0 0 +M V30 26 C 12.8693 -4.349 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 1 9 10 CFG=3 +M V30 10 1 9 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 1 19 22 +M V30 22 2 20 23 +M V30 23 1 21 24 +M V30 24 2 22 25 +M V30 25 2 24 26 +M V30 26 1 7 8 +M V30 27 1 20 21 +M V30 28 1 25 26 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 9) +M V30 END COLLECTION +M V30 END CTAB +M END +> +846 + +> +Z424464254 + +> +369.845 + +> +2.301 + +> +2 + +> +70.560 + +> +6 + +> +parp15 + +> + + +> + + +$$$$ +Compound 847 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 27 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -1.3062 0.7649 0.0 0 +M V30 3 C -1.318 2.2712 0.0 0 +M V30 4 C -2.6125 0.0235 0.0 0 +M V30 5 C -2.6243 3.0244 0.0 0 +M V30 6 C -3.9188 0.7884 0.0 0 +M V30 7 C -3.9305 2.2947 0.0 0 +M V30 8 C -5.2368 3.0479 0.0 0 +M V30 9 N -6.5431 2.3183 0.0 0 +M V30 10 C -7.8493 3.0714 0.0 0 +M V30 11 O -7.8611 4.5778 0.0 0 +M V30 12 N -9.1556 2.3418 0.0 0 +M V30 13 C -10.4619 3.095 0.0 0 +M V30 14 C -11.7681 2.3654 0.0 0 +M V30 15 C -13.0744 3.1185 0.0 0 +M V30 16 C -11.7799 0.8826 0.0 0 +M V30 17 C -14.3807 2.3889 0.0 0 +M V30 18 C -13.0862 0.1412 0.0 0 +M V30 19 C -14.3924 0.9061 0.0 0 +M V30 20 C -15.6987 0.1647 0.0 0 +M V30 21 O -17.005 0.9296 0.0 0 +M V30 22 N -15.7105 -1.318 0.0 0 +M V30 23 C -17.0167 -2.0476 0.0 0 +M V30 24 C -17.0285 -3.5304 0.0 0 +M V30 25 O -15.7458 -4.26 0.0 0 +M V30 26 N -18.3348 -4.26 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 1 9 10 +M V30 10 2 10 11 +M V30 11 1 10 12 +M V30 12 1 12 13 +M V30 13 1 13 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 2 17 19 +M V30 19 1 19 20 +M V30 20 2 20 21 +M V30 21 1 20 22 +M V30 22 1 22 23 +M V30 23 1 23 24 +M V30 24 2 24 25 +M V30 25 1 24 26 +M V30 26 1 6 7 +M V30 27 1 18 19 +M V30 END BOND +M V30 END CTAB +M END +> +847 + +> +Z595788254 + +> +358.367 + +> +0.629 + +> +4 + +> +113.320 + +> +7 + +> +ATM + +> + + +> + + +$$$$ +Compound 848 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 30 33 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.4843 -0.1425 0.0 0 +M V30 3 C 2.09 -1.5081 0.0 0 +M V30 4 C 2.3631 1.0925 0.0 0 +M V30 5 C 3.5743 -1.6506 0.0 0 +M V30 6 C 3.8475 0.95 0.0 0 +M V30 7 C 4.4531 -0.4156 0.0 0 +M V30 8 N 5.9375 -0.5581 0.0 0 +M V30 9 C 6.8162 0.6768 0.0 0 +M V30 10 O 6.1868 2.0662 0.0 0 +M V30 11 C 8.3006 0.5343 0.0 0 +M V30 12 S 9.2981 1.6625 0.0 0 +M V30 13 N 9.0368 -0.76 0.0 0 +M V30 14 C 10.6637 1.0568 0.0 0 +M V30 15 N 10.4975 -0.4393 0.0 0 +M V30 16 C 11.9581 1.8287 0.0 0 +M V30 17 N 13.2525 1.0806 0.0 0 +M V30 18 C 14.5469 1.8525 0.0 0 +M V30 19 C 13.2406 -0.4156 0.0 0 +M V30 20 C 15.8412 1.1043 0.0 0 +M V30 21 N 17.1356 1.8762 0.0 0 +M V30 22 N 15.8294 -0.3918 0.0 0 +M V30 23 C 18.43 1.1281 0.0 0 +M V30 24 C 17.1237 -1.14 0.0 0 +M V30 25 C 18.4181 -0.3681 0.0 0 +M V30 26 C 19.7244 1.9 0.0 0 +M V30 27 O 17.1119 -2.6362 0.0 0 +M V30 28 C 19.7125 -1.1162 0.0 0 +M V30 29 C 21.0187 1.1518 0.0 0 +M V30 30 C 21.0069 -0.3443 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 1 11 12 +M V30 12 2 11 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 2 20 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 2 23 25 +M V30 25 1 23 26 +M V30 26 2 24 27 +M V30 27 1 25 28 +M V30 28 2 26 29 +M V30 29 2 28 30 +M V30 30 1 6 7 +M V30 31 2 14 15 +M V30 32 1 24 25 +M V30 33 1 29 30 +M V30 END BOND +M V30 END CTAB +M END +> +848 + +> +Z195595720 + +> +440.906 + +> +1.448 + +> +2 + +> +99.580 + +> +6 + +> +parp2 + +> + + +> + + +$$$$ +Compound 849 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 33 35 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3096 -0.7433 0.0 0 +M V30 3 C 1.286 -0.7433 0.0 0 +M V30 4 N -1.4748 -2.2181 0.0 0 +M V30 5 N -2.6901 -0.1179 0.0 0 +M V30 6 C 2.5721 0.0235 0.0 0 +M V30 7 C -2.9497 -2.5131 0.0 0 +M V30 8 C -3.7048 -1.2152 0.0 0 +M V30 9 C -3.0087 1.3568 0.0 0 +M V30 10 O 2.5603 1.5338 0.0 0 +M V30 11 N 3.8582 -0.7197 0.0 0 +M V30 12 C -3.7048 -3.7992 0.0 0 +M V30 13 C -5.215 -1.2034 0.0 0 +M V30 14 C -4.4481 1.8288 0.0 0 +M V30 15 C 5.1442 0.0471 0.0 0 +M V30 16 C -5.215 -3.7874 0.0 0 +M V30 17 C -5.982 -2.4895 0.0 0 +M V30 18 C -4.7667 3.3036 0.0 0 +M V30 19 C 5.1324 1.5574 0.0 0 +M V30 20 C 6.4303 -0.6961 0.0 0 +M V30 21 C -7.4922 -2.4777 0.0 0 +M V30 22 C 6.4185 2.3125 0.0 0 +M V30 23 C 7.7164 0.0707 0.0 0 +M V30 24 F -9.0025 -2.4659 0.0 0 +M V30 25 F -7.504 -0.9675 0.0 0 +M V30 26 F -7.504 -3.9644 0.0 0 +M V30 27 C 6.4067 3.8228 0.0 0 +M V30 28 C 7.7046 1.5692 0.0 0 +M V30 29 C 9.0025 -0.6725 0.0 0 +M V30 30 O 7.6928 4.5779 0.0 0 +M V30 31 N 5.097 4.5779 0.0 0 +M V30 32 O 10.2885 0.0943 0.0 0 +M V30 33 N 8.9907 -2.1591 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 1 7 12 +M V30 12 1 8 13 +M V30 13 1 9 14 +M V30 14 1 11 15 +M V30 15 2 12 16 +M V30 16 2 13 17 +M V30 17 1 14 18 +M V30 18 2 15 19 +M V30 19 1 15 20 +M V30 20 1 17 21 +M V30 21 1 19 22 +M V30 22 2 20 23 +M V30 23 1 21 24 +M V30 24 1 21 25 +M V30 25 1 21 26 +M V30 26 1 22 27 +M V30 27 2 22 28 +M V30 28 1 23 29 +M V30 29 2 27 30 +M V30 30 1 27 31 +M V30 31 2 29 32 +M V30 32 1 29 33 +M V30 33 2 7 8 +M V30 34 1 16 17 +M V30 35 1 23 28 +M V30 END BOND +M V30 END CTAB +M END +> +849 + +> +Z17554810 + +> +479.475 + +> +2.802 + +> +3 + +> +133.100 + +> +9 + +> +parp14 + +> + + +> + + +$$$$ +Compound 850 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 1.2836 -0.7301 0.0 0 +M V30 3 N 1.2718 -2.2139 0.0 0 +M V30 4 C 2.5672 0.0235 0.0 0 +M V30 5 C 2.5555 1.5309 0.0 0 +M V30 6 C 3.8509 -0.7065 0.0 0 +M V30 7 O 1.2483 2.2964 0.0 0 +M V30 8 C 3.8391 2.2964 0.0 0 +M V30 9 C 5.1345 0.0471 0.0 0 +M V30 10 C -0.0588 1.5545 0.0 0 +M V30 11 C 5.1227 1.5545 0.0 0 +M V30 12 C -1.366 2.3199 0.0 0 +M V30 13 C 6.4064 2.3199 0.0 0 +M V30 14 O -1.3778 3.8273 0.0 0 +M V30 15 N -2.6732 1.578 0.0 0 +M V30 16 C -3.9804 2.3435 0.0 0 +M V30 17 C -5.2876 1.6016 0.0 0 +M V30 18 C -3.9922 3.8509 0.0 0 +M V30 19 C -6.5948 2.367 0.0 0 +M V30 20 C -5.2994 4.6046 0.0 0 +M V30 21 C -7.902 1.6251 0.0 0 +M V30 22 C -6.6066 3.8744 0.0 0 +M V30 23 O -7.9138 0.1413 0.0 0 +M V30 24 C -9.2092 2.3906 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 2 4 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 2 12 14 +M V30 14 1 12 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 1 19 21 +M V30 21 2 19 22 +M V30 22 2 21 23 +M V30 23 1 21 24 +M V30 24 1 9 11 +M V30 25 1 20 22 +M V30 END BOND +M V30 END CTAB +M END +> +850 + +> +Z217920874 + +> +326.347 + +> +1.790 + +> +2 + +> +98.490 + +> +6 + +> +parp14 + +> + + +> + + +$$$$ +Compound 851 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 21 22 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.3651 -0.6054 0.0 0 +M V30 3 C -1.0209 -1.104 0.0 0 +M V30 4 N 1.1989 -2.0893 0.0 0 +M V30 5 N 2.6591 0.1543 0.0 0 +M V30 6 C -0.273 -2.3979 0.0 0 +M V30 7 C 3.9531 -0.5935 0.0 0 +M V30 8 C -0.9022 -3.7631 0.0 0 +M V30 9 O 3.9412 -2.0893 0.0 0 +M V30 10 C 5.2471 0.1661 0.0 0 +M V30 11 C -2.4098 -3.9056 0.0 0 +M V30 12 C -0.0237 -4.974 0.0 0 +M V30 13 C 6.541 -0.5816 0.0 0 +M V30 14 C -3.039 -5.2708 0.0 0 +M V30 15 C -0.6529 -6.3392 0.0 0 +M V30 16 C 7.835 0.178 0.0 0 +M V30 17 C -2.1605 -6.4817 0.0 0 +M V30 18 O 9.129 -0.5698 0.0 0 +M V30 19 C -2.7897 -7.8469 0.0 0 +M V30 20 C 10.4229 0.1899 0.0 0 +M V30 21 C -1.9112 -9.0577 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 10 13 +M V30 13 1 11 14 +M V30 14 2 12 15 +M V30 15 1 13 16 +M V30 16 2 14 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 1 4 6 +M V30 22 1 15 17 +M V30 END BOND +M V30 END CTAB +M END +> +851 + +> +Z441879904 + +> +304.407 + +> +3.976 + +> +1 + +> +51.220 + +> +7 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL452158 + +> +0.9 + +$$$$ +Compound 852 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O -1.2235 -0.8706 0.0 0 +M V30 3 O 1.2 0.8941 0.0 0 +M V30 4 N 0.8706 -1.2 0.0 0 +M V30 5 C -0.8941 1.2235 0.0 0 +M V30 6 C 0.247 -2.553 0.0 0 +M V30 7 C -2.4001 1.2353 0.0 0 +M V30 8 C -0.447 2.6589 0.0 0 +M V30 9 C -1.2471 -2.6942 0.0 0 +M V30 10 C 1.1177 -3.7531 0.0 0 +M V30 11 N -2.8707 2.6707 0.0 0 +M V30 12 C -1.6706 3.5531 0.0 0 +M V30 13 F -2.1412 -1.4706 0.0 0 +M V30 14 C -1.8706 -4.0472 0.0 0 +M V30 15 C 0.4941 -5.1061 0.0 0 +M V30 16 C -1.6824 5.059 0.0 0 +M V30 17 C -1.0 -5.2473 0.0 0 +M V30 18 O -0.4 5.812 0.0 0 +M V30 19 N -2.9883 5.812 0.0 0 +M V30 20 C -3.0001 7.318 0.0 0 +M V30 21 C -4.3061 8.071 0.0 0 +M V30 22 C -5.612 7.3297 0.0 0 +M V30 23 C -4.3178 9.5769 0.0 0 +M V30 24 C -6.918 8.0827 0.0 0 +M V30 25 C -5.6238 5.8473 0.0 0 +M V30 26 C -5.6238 10.3299 0.0 0 +M V30 27 C -6.9297 9.5887 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 2 6 9 +M V30 9 1 6 10 +M V30 10 1 7 11 +M V30 11 2 8 12 +M V30 12 1 9 13 +M V30 13 1 9 14 +M V30 14 2 10 15 +M V30 15 1 12 16 +M V30 16 2 14 17 +M V30 17 2 16 18 +M V30 18 1 16 19 +M V30 19 1 19 20 +M V30 20 1 20 21 +M V30 21 2 21 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 22 25 +M V30 25 2 23 26 +M V30 26 2 24 27 +M V30 27 1 11 12 +M V30 28 1 15 17 +M V30 29 1 26 27 +M V30 END BOND +M V30 END CTAB +M END +> +852 + +> +Z425067588 + +> +387.428 + +> +3.152 + +> +3 + +> +91.060 + +> +5 + +> +CHK1 + +> + + +> + + +$$$$ +Compound 853 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 24 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C 0.9788 -1.1303 0.0 0 +M V30 3 F 2.1091 -0.1281 0.0 0 +M V30 4 F -0.1747 -2.1091 0.0 0 +M V30 5 C 1.9577 -2.2606 0.0 0 +M V30 6 C 3.4259 -1.9693 0.0 0 +M V30 7 C 1.4566 -3.6706 0.0 0 +M V30 8 C 4.4048 -3.0997 0.0 0 +M V30 9 C 2.4354 -4.801 0.0 0 +M V30 10 C 3.9037 -4.5097 0.0 0 +M V30 11 C 5.8731 -2.8083 0.0 0 +M V30 12 C 6.8519 -3.9387 0.0 0 +M V30 13 C 8.3202 -3.6473 0.0 0 +M V30 14 N 9.2991 -4.7777 0.0 0 +M V30 15 C 10.7673 -4.4864 0.0 0 +M V30 16 O 11.2451 -3.053 0.0 0 +M V30 17 N 11.7462 -5.6167 0.0 0 +M V30 18 C 13.2145 -5.3254 0.0 0 +M V30 19 C 14.1933 -6.4557 0.0 0 +M V30 20 C 13.6922 -3.8921 0.0 0 +M V30 21 O 13.6922 -7.8657 0.0 0 +M V30 22 N 15.6616 -6.1644 0.0 0 +M V30 23 O 12.6901 -2.7384 0.0 0 +M V30 24 N 15.1605 -3.6007 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 2 5 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 2 8 10 +M V30 10 1 8 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 17 18 +M V30 18 1 18 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 1 19 22 +M V30 22 2 20 23 +M V30 23 1 20 24 +M V30 24 1 9 10 +M V30 END BOND +M V30 END CTAB +M END +> +853 + +> +Z840884170 + +> +346.305 + +> +0.525 + +> +4 + +> +127.310 + +> +8 + +> +ATM + +> + + +> + + +$$$$ +Compound 854 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 33 37 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.4354 -0.4471 0.0 0 +M V30 3 C 0.2941 1.4707 0.0 0 +M V30 4 N -1.9061 -1.859 0.0 0 +M V30 5 N -2.6591 0.4471 0.0 0 +M V30 6 C 1.706 1.9414 0.0 0 +M V30 7 N -3.4121 -1.8472 0.0 0 +M V30 8 C -2.6709 1.9531 0.0 0 +M V30 9 C -3.8828 -0.4235 0.0 0 +M V30 10 O 2.9062 1.0707 0.0 0 +M V30 11 N 2.1531 3.3768 0.0 0 +M V30 12 C -1.3884 2.7062 0.0 0 +M V30 13 C -3.9769 2.7062 0.0 0 +M V30 14 C -5.3182 0.047 0.0 0 +M V30 15 N 4.1063 1.9649 0.0 0 +M V30 16 C 3.6357 3.3886 0.0 0 +M V30 17 C -1.4001 4.2122 0.0 0 +M V30 18 C -3.9887 4.2122 0.0 0 +M V30 19 C -6.436 -0.9412 0.0 0 +M V30 20 C -5.6359 1.5178 0.0 0 +M V30 21 C 4.5064 4.6123 0.0 0 +M V30 22 C -2.7062 4.9653 0.0 0 +M V30 23 C -7.8715 -0.4706 0.0 0 +M V30 24 C -7.0714 1.9884 0.0 0 +M V30 25 C 3.8828 5.9889 0.0 0 +M V30 26 C 5.9771 4.4711 0.0 0 +M V30 27 O -2.7179 6.4713 0.0 0 +M V30 28 N -8.1892 1.0001 0.0 0 +M V30 29 C 4.7535 7.2126 0.0 0 +M V30 30 C 6.8478 5.6948 0.0 0 +M V30 31 C -4.024 7.2243 0.0 0 +M V30 32 C 6.2242 7.0714 0.0 0 +M V30 33 C 7.0949 8.2951 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 6 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 1 9 14 +M V30 14 1 10 15 +M V30 15 1 11 16 +M V30 16 1 12 17 +M V30 17 2 13 18 +M V30 18 2 14 19 +M V30 19 1 14 20 +M V30 20 1 16 21 +M V30 21 2 17 22 +M V30 22 1 19 23 +M V30 23 2 20 24 +M V30 24 2 21 25 +M V30 25 1 21 26 +M V30 26 1 22 27 +M V30 27 2 23 28 +M V30 28 1 25 29 +M V30 29 2 26 30 +M V30 30 1 27 31 +M V30 31 2 29 32 +M V30 32 1 32 33 +M V30 33 2 7 9 +M V30 34 2 15 16 +M V30 35 1 18 22 +M V30 36 1 24 28 +M V30 37 1 30 32 +M V30 END BOND +M V30 END CTAB +M END +> +854 + +> +Z17617106 + +> +456.520 + +> +4.054 + +> +0 + +> +91.750 + +> +7 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1898239 + +> +0.9 + +$$$$ +Compound 855 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3119 0.7564 0.0 0 +M V30 3 C 1.2883 0.7564 0.0 0 CFG=2 +M V30 4 N -2.6947 0.1536 0.0 0 +M V30 5 N -1.4774 2.2574 0.0 0 +M V30 6 C 2.5766 0.0236 0.0 0 +M V30 7 C 1.2764 2.2693 0.0 0 +M V30 8 N -3.7112 1.2764 0.0 0 +M V30 9 C -2.9548 2.5766 0.0 0 +M V30 10 C -0.3782 3.2739 0.0 0 +M V30 11 N 3.8649 0.78 0.0 0 +M V30 12 N 2.5647 -1.4655 0.0 0 +M V30 13 C -3.5812 3.9594 0.0 0 +M V30 14 C 5.1532 0.0472 0.0 0 +M V30 15 C 3.853 -2.2102 0.0 0 +M V30 16 O -2.8366 5.2713 0.0 0 +M V30 17 C -5.0586 4.2785 0.0 0 +M V30 18 C 5.1413 -1.4419 0.0 0 +M V30 19 C 6.4415 0.8037 0.0 0 +M V30 20 O 3.8412 -3.6994 0.0 0 +M V30 21 C -3.853 6.3942 0.0 0 +M V30 22 C -5.2241 5.7796 0.0 0 +M V30 23 C 6.4296 -2.1865 0.0 0 +M V30 24 C 7.7298 0.0709 0.0 0 +M V30 25 C 7.7179 -1.4183 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 3 1 CFG=1 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 3 7 +M V30 7 1 4 8 +M V30 8 1 5 9 +M V30 9 1 5 10 +M V30 10 2 6 11 +M V30 11 1 6 12 +M V30 12 1 9 13 +M V30 13 1 11 14 +M V30 14 1 12 15 +M V30 15 1 13 16 +M V30 16 2 13 17 +M V30 17 2 14 18 +M V30 18 1 14 19 +M V30 19 2 15 20 +M V30 20 1 16 21 +M V30 21 1 17 22 +M V30 22 1 18 23 +M V30 23 2 19 24 +M V30 24 2 23 25 +M V30 25 2 8 9 +M V30 26 1 15 18 +M V30 27 2 21 22 +M V30 28 1 24 25 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 3) +M V30 END COLLECTION +M V30 END CTAB +M END +> +855 + +> +Z17623828 + +> +353.398 + +> +1.273 + +> +1 + +> +85.310 + +> +4 + +> +parp10 + +> + + +> + + +$$$$ +Compound 856 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4901 0.0 0 +M V30 3 N -1.3246 -2.2352 0.0 0 +M V30 4 N 1.2772 -2.2352 0.0 0 +M V30 5 C -2.6373 -1.4665 0.0 0 +M V30 6 C 2.5664 -1.4665 0.0 0 +M V30 7 C -4.0211 -2.0696 0.0 0 +M V30 8 C -2.8029 0.0354 0.0 0 +M V30 9 C 3.8555 -2.2116 0.0 0 +M V30 10 N -5.0382 -0.9461 0.0 0 +M V30 11 N -4.2813 0.3548 0.0 0 +M V30 12 C 3.8437 -3.7017 0.0 0 +M V30 13 C 5.1446 -1.4428 0.0 0 +M V30 14 C -4.9081 1.7385 0.0 0 +M V30 15 C 5.1328 -4.435 0.0 0 +M V30 16 C 6.4337 -2.1879 0.0 0 +M V30 17 C -6.4101 1.9041 0.0 0 +M V30 18 O 5.4285 -5.8897 0.0 0 +M V30 19 C 6.4219 -3.6781 0.0 0 +M V30 20 O -7.3089 0.6977 0.0 0 +M V30 21 N -7.0369 3.2878 0.0 0 +M V30 22 C 6.9068 -6.0316 0.0 0 +M V30 23 O 7.5218 -4.6716 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 1 11 14 +M V30 14 1 12 15 +M V30 15 2 13 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 2 15 19 +M V30 19 2 17 20 +M V30 20 1 17 21 +M V30 21 1 18 22 +M V30 22 1 19 23 +M V30 23 1 10 11 +M V30 24 1 16 19 +M V30 25 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +856 + +> +Z595834340 + +> +317.300 + +> +0.267 + +> +3 + +> +120.500 + +> +5 + +> +DNA_pk + +> + + +> + + +$$$$ +Compound 857 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4914 0.0 0 +M V30 3 N -1.3257 -2.2371 0.0 0 +M V30 4 N 1.2783 -2.2371 0.0 0 +M V30 5 C -2.6395 -1.4795 0.0 0 +M V30 6 C 2.5685 -1.4795 0.0 0 +M V30 7 C -4.0244 -2.0832 0.0 0 +M V30 8 C -2.8053 0.0236 0.0 0 +M V30 9 C 3.8587 -2.2253 0.0 0 +M V30 10 N -5.0424 -0.9587 0.0 0 +M V30 11 N -4.2849 0.3432 0.0 0 +M V30 12 C 3.8469 -3.7167 0.0 0 +M V30 13 C 5.1489 -1.4677 0.0 0 +M V30 14 C -4.9122 1.7281 0.0 0 +M V30 15 C 5.1371 -4.4506 0.0 0 +M V30 16 C 6.4391 -2.2134 0.0 0 +M V30 17 C -6.4155 1.8938 0.0 0 +M V30 18 O 5.1253 -5.942 0.0 0 +M V30 19 C 6.4273 -3.693 0.0 0 +M V30 20 O -7.3151 0.6865 0.0 0 +M V30 21 N -7.0428 3.2787 0.0 0 +M V30 22 C 6.4155 -6.6759 0.0 0 +M V30 23 O 7.7175 -4.4269 0.0 0 +M V30 24 C 7.7057 -5.9183 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 9 13 +M V30 13 1 11 14 +M V30 14 1 12 15 +M V30 15 2 13 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 2 15 19 +M V30 19 2 17 20 +M V30 20 1 17 21 +M V30 21 1 18 22 +M V30 22 1 19 23 +M V30 23 1 22 24 +M V30 24 1 10 11 +M V30 25 1 16 19 +M V30 26 1 23 24 +M V30 END BOND +M V30 END CTAB +M END +> +857 + +> +Z595835678 + +> +331.327 + +> +0.226 + +> +3 + +> +120.500 + +> +5 + +> +DNA_pk + +> + + +> + + +$$$$ +Compound 858 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3044 0.7638 0.0 0 +M V30 3 N -2.6088 0.0235 0.0 0 +M V30 4 C -1.3161 2.268 0.0 0 +M V30 5 C -3.9132 0.7873 0.0 0 +M V30 6 C -2.6206 3.0201 0.0 0 +M V30 7 N -3.925 2.2915 0.0 0 +M V30 8 C -5.2176 0.047 0.0 0 +M V30 9 C -2.6323 4.5243 0.0 0 +M V30 10 C -6.5221 0.8108 0.0 0 +M V30 11 C -5.2294 -1.4336 0.0 0 +M V30 12 C -1.3514 5.2764 0.0 0 +M V30 13 C -7.8265 0.0705 0.0 0 +M V30 14 C -6.5338 -2.1622 0.0 0 +M V30 15 N -9.1309 0.8343 0.0 0 +M V30 16 C -7.8383 -1.4101 0.0 0 +M V30 17 C -9.1427 2.3385 0.0 0 +M V30 18 O -7.8618 3.0906 0.0 0 +M V30 19 N -10.4471 3.0906 0.0 0 +M V30 20 C -10.4589 4.5948 0.0 0 CFG=2 +M V30 21 C -9.1779 5.3469 0.0 0 +M V30 22 C -11.7633 5.3469 0.0 0 +M V30 23 N -9.1897 6.8511 0.0 0 +M V30 24 C -11.775 6.8511 0.0 0 +M V30 25 C -10.4941 7.6032 0.0 0 +M V30 26 O -10.5059 9.1074 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 8 10 +M V30 10 1 8 11 +M V30 11 1 9 12 +M V30 12 1 10 13 +M V30 13 2 11 14 +M V30 14 1 13 15 +M V30 15 2 13 16 +M V30 16 1 15 17 +M V30 17 2 17 18 +M V30 18 1 17 19 +M V30 19 1 20 19 CFG=1 +M V30 20 1 20 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 2 25 26 +M V30 26 1 6 7 +M V30 27 1 14 16 +M V30 28 1 24 25 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 20) +M V30 END COLLECTION +M V30 END CTAB +M END +> +858 + +> +Z1004109894 + +> +355.391 + +> +0.241 + +> +4 + +> +111.690 + +> +4 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 859 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 Br 0.0 0.0 0.0 0 +M V30 2 C 0.8552 -1.2234 0.0 0 +M V30 3 C 2.3519 -1.0809 0.0 0 +M V30 4 C 0.2019 -2.5776 0.0 0 +M V30 5 C 3.2072 -2.3044 0.0 0 +M V30 6 C 1.0571 -3.8011 0.0 0 +M V30 7 C 2.5539 -3.6586 0.0 0 +M V30 8 C 3.4091 -4.8821 0.0 0 +M V30 9 N 2.9102 -6.2956 0.0 0 +M V30 10 N 4.9058 -4.8939 0.0 0 +M V30 11 N 4.11 -7.1865 0.0 0 +M V30 12 C 5.3334 -6.3194 0.0 0 +M V30 13 C 4.2287 -8.6832 0.0 0 +M V30 14 C 6.6876 -6.9489 0.0 0 +M V30 15 O 2.9815 -9.5385 0.0 0 +M V30 16 N 5.5829 -9.3128 0.0 0 +M V30 17 C 6.8064 -8.4456 0.0 0 +M V30 18 C 8.0655 -6.3431 0.0 0 +M V30 19 S 8.2675 -8.7664 0.0 0 +M V30 20 C 9.0396 -7.4716 0.0 0 +M V30 21 C 8.3269 -4.8464 0.0 0 +M V30 22 C 10.5363 -7.3884 0.0 0 +M V30 23 C 9.6454 -4.1218 0.0 0 +M V30 24 C 11.4153 -6.1531 0.0 0 +M V30 25 C 11.0233 -4.7039 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 7 8 +M V30 8 2 8 9 +M V30 9 1 8 10 +M V30 10 1 9 11 +M V30 11 2 10 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 2 13 15 +M V30 15 1 13 16 +M V30 16 2 14 17 +M V30 17 1 14 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 1 18 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 1 6 7 +M V30 26 1 11 12 +M V30 27 1 16 17 +M V30 28 1 19 20 +M V30 29 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +859 + +> +Z55411173 + +> +415.307 + +> +3.818 + +> +1 + +> +59.810 + +> +1 + +> +Serine/threonine-protein kinase Chk2 + +> +CHEMBL2007592 + +> +0.96 + +$$$$ +Compound 860 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 0.1423 -1.4825 0.0 0 +M V30 3 C -1.4825 0.3202 0.0 0 +M V30 4 N -1.2453 -2.0874 0.0 0 +M V30 5 N 1.4351 -2.2297 0.0 0 +M V30 6 C -2.2416 -0.9725 0.0 0 +M V30 7 C -2.04 1.7316 0.0 0 +M V30 8 C 1.4232 -3.7242 0.0 0 +M V30 9 C -3.7479 -1.186 0.0 0 +M V30 10 C -3.487 2.1823 0.0 0 +M V30 11 O 0.1067 -4.4595 0.0 0 +M V30 12 C 2.716 -4.4595 0.0 0 +M V30 13 C -4.8628 -0.1541 0.0 0 +M V30 14 C -4.7442 1.3402 0.0 0 +M V30 15 C 2.7042 -5.954 0.0 0 +M V30 16 C 3.997 -6.6893 0.0 0 +M V30 17 C 5.2898 -5.9302 0.0 0 +M V30 18 C 3.9851 -8.1838 0.0 0 +M V30 19 C 6.5826 -6.6656 0.0 0 +M V30 20 C 5.2779 -8.931 0.0 0 +M V30 21 O 7.8754 -5.9065 0.0 0 +M V30 22 C 6.5707 -8.16 0.0 0 +M V30 23 O 5.266 -10.4254 0.0 0 +M V30 24 C 9.1682 -6.6419 0.0 0 +M V30 25 O 7.8635 -8.9072 0.0 0 +M V30 26 C 6.5589 -11.1608 0.0 0 +M V30 27 C 9.1563 -8.1363 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 2 3 6 +M V30 6 1 3 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 1 10 14 +M V30 14 2 12 15 CFG=2 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 1 19 21 +M V30 21 2 19 22 +M V30 22 1 20 23 +M V30 23 1 21 24 +M V30 24 1 22 25 +M V30 25 1 23 26 +M V30 26 1 24 27 +M V30 27 1 4 6 +M V30 28 1 13 14 +M V30 29 1 20 22 +M V30 30 1 25 27 +M V30 END BOND +M V30 END CTAB +M END +> +860 + +> +Z228299332 + +> +386.465 + +> +4.948 + +> +1 + +> +69.680 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL516766 + +> +0.92 + +$$$$ +Compound 861 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 1.4169 0.4723 0.0 0 +M V30 3 C -0.8973 1.228 0.0 0 +M V30 4 C 1.4051 1.9836 0.0 0 +M V30 5 C 2.7039 -0.2597 0.0 0 +M V30 6 C -2.4087 1.2398 0.0 0 +M V30 7 C -0.0236 2.456 0.0 0 +M V30 8 C 2.6921 2.7511 0.0 0 +M V30 9 C 3.991 0.4959 0.0 0 +M V30 10 O -3.1644 2.5504 0.0 0 +M V30 11 O -3.1644 -0.0472 0.0 0 +M V30 12 C 3.9791 2.0073 0.0 0 +M V30 13 C -4.6758 -0.0354 0.0 0 +M V30 14 N 5.2662 2.7748 0.0 0 +M V30 15 C -5.4315 -1.3224 0.0 0 +M V30 16 C 5.2544 4.2861 0.0 0 +M V30 17 O 3.9437 5.0418 0.0 0 +M V30 18 C 6.5414 5.0418 0.0 0 +M V30 19 N 7.8285 4.3098 0.0 0 +M V30 20 C 6.5296 6.5532 0.0 0 +M V30 21 C 9.1155 5.0655 0.0 0 +M V30 22 C 7.8167 7.3207 0.0 0 +M V30 23 O 10.4025 4.3334 0.0 0 +M V30 24 C 9.1037 6.5768 0.0 0 +M V30 25 C 7.8048 8.8321 0.0 0 +M V30 26 C 10.3907 7.3443 0.0 0 +M V30 27 C 9.0919 9.5878 0.0 0 +M V30 28 C 10.3789 8.8557 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 2 3 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 2 8 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 18 19 +M V30 19 2 18 20 +M V30 20 1 19 21 +M V30 21 1 20 22 +M V30 22 2 21 23 +M V30 23 1 21 24 +M V30 24 1 22 25 +M V30 25 1 24 26 +M V30 26 2 25 27 +M V30 27 2 26 28 +M V30 28 1 4 7 +M V30 29 1 9 12 +M V30 30 2 22 24 +M V30 31 1 27 28 +M V30 END BOND +M V30 END CTAB +M END +> +861 + +> +Z368305856 + +> +392.428 + +> +3.595 + +> +2 + +> +84.500 + +> +5 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 862 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 31 34 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O -0.7582 -1.2914 0.0 0 +M V30 3 O 0.7345 1.3151 0.0 0 +M V30 4 N 1.2914 -0.7345 0.0 0 +M V30 5 C -1.3151 0.7701 0.0 0 +M V30 6 C 1.2796 -2.2274 0.0 0 +M V30 7 C -1.327 2.2867 0.0 0 +M V30 8 C 2.571 -2.962 0.0 0 +M V30 9 C -0.0355 -2.962 0.0 0 +M V30 10 C 2.5592 -4.4549 0.0 0 +M V30 11 C -0.0473 -4.4549 0.0 0 +M V30 12 C 1.244 -5.1895 0.0 0 +M V30 13 C 1.2322 -6.6824 0.0 0 +M V30 14 N 2.4407 -7.5591 0.0 0 +M V30 15 C 0.0 -7.5591 0.0 0 +M V30 16 N 1.9668 -8.9809 0.0 0 +M V30 17 C 0.4502 -8.9809 0.0 0 CFG=2 +M V30 18 C 2.8435 -10.1894 0.0 0 +M V30 19 C -0.4502 -10.1894 0.0 0 +M V30 20 O 2.2156 -11.552 0.0 0 +M V30 21 C 4.3246 -10.0236 0.0 0 +M V30 22 C 0.154 -11.552 0.0 0 +M V30 23 C -1.9549 -10.0236 0.0 0 +M V30 24 S 5.3198 -11.1254 0.0 0 +M V30 25 C 5.0591 -8.7084 0.0 0 +M V30 26 C -0.7464 -12.7605 0.0 0 +M V30 27 C -2.8554 -11.2321 0.0 0 +M V30 28 C 6.6824 -10.4975 0.0 0 +M V30 29 C 6.5165 -9.0046 0.0 0 +M V30 30 C -2.2511 -12.5946 0.0 0 +M V30 31 F -3.1516 -13.8031 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 2 6 8 +M V30 8 1 6 9 +M V30 9 1 8 10 +M V30 10 2 9 11 +M V30 11 2 10 12 +M V30 12 1 12 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 CFG=3 +M V30 19 2 18 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 1 19 23 +M V30 23 1 21 24 +M V30 24 2 21 25 +M V30 25 1 22 26 +M V30 26 2 23 27 +M V30 27 1 24 28 +M V30 28 1 25 29 +M V30 29 2 26 30 +M V30 30 1 30 31 +M V30 31 1 11 12 +M V30 32 1 16 17 +M V30 33 1 27 30 +M V30 34 2 28 29 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 17) +M V30 END COLLECTION +M V30 END CTAB +M END +> +862 + +> +Z57514595 + +> +457.541 + +> +3.719 + +> +1 + +> +78.840 + +> +5 + +> +ATM + +> + + +> + + +$$$$ +Compound 863 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.3125 -0.7449 0.0 0 +M V30 3 N -1.3243 -2.2348 0.0 0 +M V30 4 C -2.625 0.0236 0.0 0 +M V30 5 C -0.0354 -2.968 0.0 0 +M V30 6 C -2.6369 1.5372 0.0 0 +M V30 7 C -3.9376 -0.7213 0.0 0 +M V30 8 C -0.0472 -4.4579 0.0 0 +M V30 9 C -3.9494 2.2939 0.0 0 +M V30 10 C -5.2501 0.0472 0.0 0 +M V30 11 C 1.2415 -5.2028 0.0 0 +M V30 12 C -1.3598 -5.2028 0.0 0 +M V30 13 C -3.9612 3.8075 0.0 0 +M V30 14 C -5.2619 1.549 0.0 0 +M V30 15 C 1.2297 -6.6927 0.0 0 +M V30 16 C 2.5304 -4.4342 0.0 0 +M V30 17 C -1.3716 -6.6927 0.0 0 +M V30 18 O -5.2738 4.5643 0.0 0 +M V30 19 N -2.6723 4.5643 0.0 0 +M V30 20 C -0.0827 -7.4259 0.0 0 +M V30 21 N 3.8193 -5.1792 0.0 0 +M V30 22 C 5.1082 -4.4106 0.0 0 CFG=2 +M V30 23 C 3.8075 -6.6691 0.0 0 +M V30 24 C 6.3971 -5.1555 0.0 0 +M V30 25 C 5.0964 -2.897 0.0 0 +M V30 26 C 5.0964 -7.4022 0.0 0 +M V30 27 C 6.3853 -6.6454 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 2 8 11 +M V30 11 1 8 12 +M V30 12 1 9 13 +M V30 13 2 9 14 +M V30 14 1 11 15 +M V30 15 1 11 16 +M V30 16 2 12 17 +M V30 17 2 13 18 +M V30 18 1 13 19 +M V30 19 2 15 20 +M V30 20 1 16 21 +M V30 21 1 21 22 +M V30 22 1 21 23 +M V30 23 1 22 24 +M V30 24 1 22 25 CFG=1 +M V30 25 1 23 26 +M V30 26 1 24 27 +M V30 27 1 10 14 +M V30 28 1 17 20 +M V30 29 1 26 27 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 22) +M V30 END COLLECTION +M V30 END CTAB +M END +> +863 + +> +Z424506426 + +> +365.469 + +> +3.260 + +> +2 + +> +75.430 + +> +6 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3605996 + +> +0.85 + +$$$$ +Compound 864 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.509 0.0 0 +M V30 3 N -1.3204 2.2635 0.0 0 +M V30 4 C 1.2732 2.2635 0.0 0 +M V30 5 C -1.3322 3.7726 0.0 0 +M V30 6 C 1.2614 3.7726 0.0 0 +M V30 7 C 2.5583 1.5326 0.0 0 +M V30 8 N -0.0471 4.5271 0.0 0 +M V30 9 C -2.6408 4.5271 0.0 0 +M V30 10 C 2.5465 4.5271 0.0 0 +M V30 11 C 3.8433 2.2871 0.0 0 +M V30 12 C -3.9494 3.7962 0.0 0 +M V30 13 C 3.8316 3.7962 0.0 0 +M V30 14 C -5.2581 4.5507 0.0 0 +M V30 15 O -5.2699 6.0598 0.0 0 +M V30 16 N -6.5667 3.8198 0.0 0 +M V30 17 C -7.8754 4.5743 0.0 0 +M V30 18 C -9.184 3.8433 0.0 0 +M V30 19 O -10.4926 4.5979 0.0 0 +M V30 20 C -11.8013 3.8669 0.0 0 +M V30 21 C -11.8131 2.3814 0.0 0 +M V30 22 C -13.1099 4.6215 0.0 0 +M V30 23 C -13.1217 1.6505 0.0 0 +M V30 24 C -10.528 1.6505 0.0 0 +M V30 25 C -14.4186 3.8905 0.0 0 +M V30 26 C -13.1217 6.1305 0.0 0 +M V30 27 C -14.4303 2.405 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 16 17 +M V30 17 1 17 18 +M V30 18 1 18 19 +M V30 19 1 19 20 +M V30 20 2 20 21 +M V30 21 1 20 22 +M V30 22 1 21 23 +M V30 23 1 21 24 +M V30 24 2 22 25 +M V30 25 1 22 26 +M V30 26 2 23 27 +M V30 27 1 6 8 +M V30 28 1 11 13 +M V30 29 1 25 27 +M V30 END BOND +M V30 END CTAB +M END +> +864 + +> +Z424729862 + +> +365.426 + +> +2.410 + +> +2 + +> +79.790 + +> +7 + +> +Tankyrase1 + +> + + +> + + +$$$$ +Compound 865 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 30 33 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5076 0.0 0 +M V30 3 N -1.3191 2.2614 0.0 0 +M V30 4 C 1.272 2.2614 0.0 0 +M V30 5 C -1.3309 3.7691 0.0 0 +M V30 6 C 1.2602 3.7691 0.0 0 +M V30 7 C 2.5559 1.5194 0.0 0 +M V30 8 N -0.0471 4.5229 0.0 0 +M V30 9 C -2.6383 4.5229 0.0 0 +M V30 10 C 2.5441 4.5229 0.0 0 +M V30 11 C 3.8397 2.2732 0.0 0 +M V30 12 N -3.9457 3.7926 0.0 0 +M V30 13 C 3.828 3.7926 0.0 0 +M V30 14 C -5.2531 4.5464 0.0 0 +M V30 15 C -3.9575 2.3085 0.0 0 +M V30 16 O -6.5606 3.8162 0.0 0 +M V30 17 C -5.2649 6.0541 0.0 0 +M V30 18 C -5.2649 1.5665 0.0 0 +M V30 19 C -6.5723 6.8079 0.0 0 +M V30 20 C -3.9811 6.8079 0.0 0 +M V30 21 C -5.2767 0.0824 0.0 0 +M V30 22 C -6.5723 2.3203 0.0 0 +M V30 23 C -6.5841 8.3156 0.0 0 +M V30 24 C -3.9929 8.3156 0.0 0 +M V30 25 C -6.5841 -0.6595 0.0 0 +M V30 26 C -7.8797 1.5783 0.0 0 +M V30 27 O -7.8915 9.0694 0.0 0 +M V30 28 N -5.3003 9.0694 0.0 0 +M V30 29 C -7.8915 0.106 0.0 0 +M V30 30 C -5.312 10.577 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 1 12 15 +M V30 15 2 14 16 +M V30 16 1 14 17 +M V30 17 1 15 18 +M V30 18 2 17 19 +M V30 19 1 17 20 +M V30 20 2 18 21 +M V30 21 1 18 22 +M V30 22 1 19 23 +M V30 23 2 20 24 +M V30 24 1 21 25 +M V30 25 2 22 26 +M V30 26 2 23 27 +M V30 27 1 23 28 +M V30 28 2 25 29 +M V30 29 1 28 30 +M V30 30 1 6 8 +M V30 31 1 11 13 +M V30 32 1 24 28 +M V30 33 1 26 29 +M V30 END BOND +M V30 END CTAB +M END +> +865 + +> +Z316504718 + +> +400.430 + +> +0.858 + +> +1 + +> +82.080 + +> +5 + +> +parp15 + +> + + +> + + +$$$$ +Compound 866 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0117 1.5061 0.0 0 +M V30 3 N -1.3178 2.271 0.0 0 +M V30 4 C 1.2708 2.271 0.0 0 +M V30 5 C -1.3296 3.7771 0.0 0 +M V30 6 C 1.259 3.7771 0.0 0 +M V30 7 C 2.5534 1.5296 0.0 0 +M V30 8 N -0.047 4.5302 0.0 0 +M V30 9 C -2.6357 4.5302 0.0 0 +M V30 10 C 2.5416 4.5302 0.0 0 +M V30 11 C 3.836 2.2945 0.0 0 +M V30 12 C -3.9419 3.8007 0.0 0 +M V30 13 C 3.8242 3.8007 0.0 0 +M V30 14 C -5.248 4.5537 0.0 0 +M V30 15 C -6.5541 3.8242 0.0 0 +M V30 16 O -6.5659 2.3416 0.0 0 +M V30 17 N -7.8602 4.5773 0.0 0 +M V30 18 C -7.872 6.0834 0.0 0 +M V30 19 C -9.1664 3.8477 0.0 0 +M V30 20 C -9.1781 6.8483 0.0 0 +M V30 21 C -10.4725 4.6008 0.0 0 +M V30 22 O -9.1899 8.3544 0.0 0 +M V30 23 N -10.4842 6.107 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 9 12 +M V30 12 2 10 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 17 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 1 20 23 +M V30 23 1 6 8 +M V30 24 1 11 13 +M V30 25 1 21 23 +M V30 END BOND +M V30 END CTAB +M END +> +866 + +> +Z85251174 + +> +314.339 + +> +-0.253 + +> +2 + +> +90.870 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3105413 + +> +0.97 + +$$$$ +Compound 867 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 20 21 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.2889 -0.7331 0.0 0 +M V30 3 C 2.5778 0.0236 0.0 0 +M V30 4 C 1.2771 -2.2231 0.0 0 +M V30 5 C 3.8668 -0.7095 0.0 0 +M V30 6 C 2.566 -2.9562 0.0 0 +M V30 7 C 3.8549 -2.1994 0.0 0 +M V30 8 C 5.1557 0.0473 0.0 0 +M V30 9 N 5.1439 1.5609 0.0 0 +M V30 10 C 6.4328 2.3295 0.0 0 +M V30 11 O 7.7218 1.5845 0.0 0 +M V30 12 C 6.421 3.8431 0.0 0 +M V30 13 C 7.7099 4.5999 0.0 0 +M V30 14 C 5.1084 4.5999 0.0 0 +M V30 15 C 7.6981 6.1135 0.0 0 +M V30 16 C 5.0966 6.1135 0.0 0 +M V30 17 C 6.3855 6.8703 0.0 0 +M V30 18 C 6.3737 8.384 0.0 0 +M V30 19 O 7.6626 9.1408 0.0 0 +M V30 20 N 5.0611 9.1408 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 2 5 7 +M V30 7 1 5 8 +M V30 8 1 8 9 +M V30 9 1 9 10 +M V30 10 2 10 11 +M V30 11 1 10 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 2 14 16 +M V30 16 2 15 17 +M V30 17 1 17 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 6 7 +M V30 21 1 16 17 +M V30 END BOND +M V30 END CTAB +M END +> +867 + +> +Z52020040 + +> +288.729 + +> +2.477 + +> +2 + +> +72.190 + +> +4 + +> +parp14 + +> + + +> + + +$$$$ +Compound 868 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -0.3656 1.4627 0.0 0 +M V30 3 C 1.4273 -0.401 0.0 0 +M V30 4 N -1.7693 2.0407 0.0 0 +M V30 5 N 0.5897 2.6187 0.0 0 +M V30 6 C 1.7693 -1.8401 0.0 0 +M V30 7 N -1.675 3.5505 0.0 0 +M V30 8 C 2.076 2.6776 0.0 0 +M V30 9 C -0.2123 3.9044 0.0 0 +M V30 10 O 3.1967 -2.2412 0.0 0 +M V30 11 N 0.6723 -2.8664 0.0 0 +M V30 12 C 2.772 4.0106 0.0 0 +M V30 13 C 2.8546 1.4155 0.0 0 +M V30 14 N 0.4836 5.2374 0.0 0 +M V30 15 C 1.9699 5.2964 0.0 0 +M V30 16 C 4.2583 4.0696 0.0 0 +M V30 17 C 4.3409 1.4744 0.0 0 +M V30 18 C -0.3184 6.5231 0.0 0 +M V30 19 O 2.6658 6.6293 0.0 0 +M V30 20 C 5.0368 2.8074 0.0 0 +M V30 21 C 0.3774 7.8561 0.0 0 +M V30 22 C -0.4246 9.1418 0.0 0 +M V30 23 C 0.2713 10.4748 0.0 0 +M V30 24 C -1.9345 9.1065 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 1 9 14 +M V30 14 1 12 15 +M V30 15 1 12 16 +M V30 16 2 13 17 +M V30 17 1 14 18 +M V30 18 2 15 19 +M V30 19 2 16 20 +M V30 20 1 18 21 +M V30 21 1 21 22 +M V30 22 1 22 23 +M V30 23 1 22 24 +M V30 24 2 7 9 +M V30 25 1 14 15 +M V30 26 1 17 20 +M V30 END BOND +M V30 END CTAB +M END +> +868 + +> +Z17752482 + +> +345.419 + +> +1.295 + +> +1 + +> +94.110 + +> +6 + +> +CHK2 + +> + + +> + + +$$$$ +Compound 869 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 22 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4916 0.0 0 +M V30 3 N -1.3259 -2.2257 0.0 0 +M V30 4 N 1.2785 -2.2257 0.0 0 +M V30 5 C -1.3377 -3.7173 0.0 0 +M V30 6 C -2.64 -1.468 0.0 0 +M V30 7 C 1.2667 -3.7173 0.0 0 +M V30 8 C -0.0473 -4.4514 0.0 0 +M V30 9 C -2.6519 -4.4514 0.0 0 +M V30 10 C -3.9541 -2.202 0.0 0 +M V30 11 O 2.5571 -4.4514 0.0 0 +M V30 12 C -0.0591 -5.943 0.0 0 +M V30 13 C -2.6637 -5.943 0.0 0 +M V30 14 O -3.966 -3.6937 0.0 0 +M V30 15 N -5.2682 -1.4443 0.0 0 +M V30 16 C -1.3733 -6.6771 0.0 0 +M V30 17 C -6.5823 -2.1783 0.0 0 CFG=2 +M V30 18 C -5.2801 0.071 0.0 0 +M V30 19 C -7.8965 -1.4206 0.0 0 +M V30 20 C -6.5942 -3.67 0.0 0 +M V30 21 O -9.2106 -2.1546 0.0 0 +M V30 22 C -10.5247 -1.3969 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 7 11 +M V30 11 1 8 12 +M V30 12 2 9 13 +M V30 13 2 10 14 +M V30 14 1 10 15 +M V30 15 2 12 16 +M V30 16 1 17 15 CFG=1 +M V30 17 1 15 18 +M V30 18 1 17 19 +M V30 19 1 17 20 +M V30 20 1 19 21 +M V30 21 1 21 22 +M V30 22 1 7 8 +M V30 23 1 13 16 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 17) +M V30 END COLLECTION +M V30 END CTAB +M END +> +869 + +> +Z599749506 + +> +305.329 + +> +1.152 + +> +1 + +> +78.950 + +> +5 + +> +parp2 + +> + + +> + + +$$$$ +Compound 870 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4992 0.0 0 +M V30 3 N -1.3326 -2.237 0.0 0 +M V30 4 N 1.2851 -2.237 0.0 0 +M V30 5 C -1.3445 -3.7363 0.0 0 +M V30 6 C -2.6653 -4.4859 0.0 0 +M V30 7 C -0.0475 -4.4859 0.0 0 +M V30 8 C -2.6772 -5.9852 0.0 0 +M V30 9 C -0.0594 -5.9852 0.0 0 +M V30 10 C -1.3802 -6.7229 0.0 0 +M V30 11 C -1.3921 -8.2222 0.0 0 +M V30 12 N -0.0951 -8.96 0.0 0 +M V30 13 C -0.107 -10.4592 0.0 0 +M V30 14 O -1.4278 -11.197 0.0 0 +M V30 15 C 1.1899 -11.197 0.0 0 +M V30 16 N 1.178 -12.6963 0.0 0 +M V30 17 C 2.4869 -10.4354 0.0 0 +M V30 18 C 2.475 -13.434 0.0 0 +M V30 19 C 3.7839 -11.1732 0.0 0 +M V30 20 O 2.4631 -14.9333 0.0 0 +M V30 21 C 3.772 -12.6725 0.0 0 +M V30 22 C 5.0809 -10.4116 0.0 0 +M V30 23 C 3.7601 -15.6829 0.0 0 +M V30 24 C 5.069 -13.4102 0.0 0 +M V30 25 C 6.3779 -11.1494 0.0 0 +M V30 26 C 6.366 -12.6487 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 5 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 2 8 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 2 15 17 +M V30 17 2 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 1 20 23 +M V30 23 2 21 24 +M V30 24 1 22 25 +M V30 25 1 24 26 +M V30 26 1 9 10 +M V30 27 1 19 21 +M V30 28 2 25 26 +M V30 END BOND +M V30 END CTAB +M END +> +870 + +> +Z850739964 + +> +350.371 + +> +2.759 + +> +3 + +> +106.340 + +> +5 + +> +ATM + +> + + +> + + +$$$$ +Compound 871 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C 0.4491 -1.4183 0.0 0 +M V30 3 N -0.4491 -2.6238 0.0 0 +M V30 4 C 1.8674 -1.8674 0.0 0 +M V30 5 C 0.4254 -3.8294 0.0 0 +M V30 6 C -1.9619 -2.612 0.0 0 +M V30 7 C 1.8556 -3.3566 0.0 0 +M V30 8 C 3.1557 -1.111 0.0 0 +M V30 9 O -0.0472 -5.2477 0.0 0 +M V30 10 C 3.1439 -4.0894 0.0 0 +M V30 11 C 4.444 -1.8438 0.0 0 +M V30 12 C 4.4322 -3.333 0.0 0 +M V30 13 C 5.7323 -1.0873 0.0 0 +M V30 14 O 7.0206 -1.8201 0.0 0 +M V30 15 N 5.7205 0.4254 0.0 0 +M V30 16 C 7.0088 1.1819 0.0 0 +M V30 17 C 4.4085 1.1819 0.0 0 +M V30 18 C 6.9969 2.6947 0.0 0 +M V30 19 C 8.2852 3.4512 0.0 0 +M V30 20 C 5.685 3.4512 0.0 0 +M V30 21 C 8.2734 4.964 0.0 0 +M V30 22 C 5.6732 4.964 0.0 0 +M V30 23 C 6.9615 5.7205 0.0 0 +M V30 24 N 6.9497 7.2333 0.0 0 +M V30 25 C 5.6377 8.0016 0.0 0 +M V30 26 C 8.238 8.0016 0.0 0 +M V30 27 C 5.6259 9.5144 0.0 0 +M V30 28 C 9.5263 7.257 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 1 4 8 +M V30 8 2 5 9 +M V30 9 1 7 10 +M V30 10 2 8 11 +M V30 11 2 10 12 +M V30 12 1 11 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 18 19 +M V30 19 1 18 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 2 21 23 +M V30 23 1 23 24 +M V30 24 1 24 25 +M V30 25 1 24 26 +M V30 26 1 25 27 +M V30 27 1 26 28 +M V30 28 1 5 7 +M V30 29 1 11 12 +M V30 30 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +871 + +> +Z109805680 + +> +379.452 + +> +3.665 + +> +0 + +> +60.930 + +> +6 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL611980 + +> +0.89 + +$$$$ +Compound 872 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 28 31 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -0.0118 -1.4893 0.0 0 +M V30 3 N -1.3238 -2.2221 0.0 0 +M V30 4 C 1.2765 -2.2221 0.0 0 +M V30 5 N -1.3356 -3.7115 0.0 0 +M V30 6 C 1.2647 -3.7115 0.0 0 +M V30 7 C 2.5649 -1.4657 0.0 0 +M V30 8 C -0.0472 -4.4562 0.0 0 +M V30 9 C 2.5531 -4.4562 0.0 0 +M V30 10 C 3.8533 -2.1985 0.0 0 +M V30 11 C -0.0591 -5.9455 0.0 0 +M V30 12 C 3.8415 -3.6997 0.0 0 +M V30 13 C -1.3711 -6.6902 0.0 0 +M V30 14 O -2.6831 -5.9219 0.0 0 +M V30 15 N -1.3829 -8.1795 0.0 0 +M V30 16 C -2.695 -8.9124 0.0 0 +M V30 17 C -2.7068 -10.4017 0.0 0 +M V30 18 C -4.007 -8.1559 0.0 0 +M V30 19 C -4.0188 -11.1464 0.0 0 +M V30 20 C -5.319 -8.8887 0.0 0 +M V30 21 C -5.3309 -10.3781 0.0 0 +M V30 22 N -6.6429 -11.1228 0.0 0 +M V30 23 C -7.9549 -10.3544 0.0 0 +M V30 24 C -6.6547 -12.6121 0.0 0 +M V30 25 C -9.267 -11.0991 0.0 0 +M V30 26 C -7.9668 -13.345 0.0 0 +M V30 27 O -10.579 -10.3308 0.0 0 +M V30 28 N -9.2788 -12.5885 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 2 7 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 11 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 2 19 21 +M V30 21 1 21 22 +M V30 22 1 22 23 +M V30 23 1 22 24 +M V30 24 1 23 25 +M V30 25 1 24 26 +M V30 26 2 25 27 +M V30 27 1 25 28 +M V30 28 1 6 8 +M V30 29 1 10 12 +M V30 30 1 20 21 +M V30 31 1 26 28 +M V30 END BOND +M V30 END CTAB +M END +> +872 + +> +Z381571198 + +> +377.397 + +> +-0.244 + +> +3 + +> +102.900 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3105426 + +> +0.9 + +$$$$ +Compound 873 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 27 30 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 O 1.4802 0.0117 0.0 0 +M V30 3 O 0.1409 -1.4685 0.0 0 +M V30 4 C -1.4685 -0.2937 0.0 0 +M V30 5 C 0.1409 1.492 0.0 0 +M V30 6 C -2.2204 1.0103 0.0 0 CFG=2 +M V30 7 C -1.2335 2.1146 0.0 0 +M V30 8 C -3.7124 1.1748 0.0 0 +M V30 9 C -4.335 2.5493 0.0 0 +M V30 10 O -5.8036 2.8665 0.0 0 +M V30 11 N -3.5949 3.8534 0.0 0 +M V30 12 C -5.968 4.3585 0.0 0 +M V30 13 N -4.6052 4.9694 0.0 0 +M V30 14 S -7.2721 5.1222 0.0 0 +M V30 15 C -7.2838 6.6259 0.0 0 +M V30 16 C -8.5879 7.3896 0.0 0 +M V30 17 N -9.8919 6.6494 0.0 0 +M V30 18 N -8.5996 8.8933 0.0 0 +M V30 19 C -11.196 7.4131 0.0 0 +M V30 20 C -9.9037 9.6452 0.0 0 +M V30 21 C -11.2077 8.9168 0.0 0 +M V30 22 C -12.5 6.6729 0.0 0 +M V30 23 O -9.9154 11.149 0.0 0 +M V30 24 C -12.5118 9.6687 0.0 0 +M V30 25 C -13.8041 7.4366 0.0 0 +M V30 26 C -12.5118 5.1927 0.0 0 +M V30 27 C -13.8158 8.9403 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 2 1 3 +M V30 3 1 1 4 +M V30 4 1 1 5 +M V30 5 1 4 6 +M V30 6 1 5 7 +M V30 7 1 6 8 CFG=1 +M V30 8 1 8 9 +M V30 9 1 9 10 +M V30 10 2 9 11 +M V30 11 1 10 12 +M V30 12 1 11 13 +M V30 13 1 12 14 +M V30 14 1 14 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 18 20 +M V30 20 2 19 21 +M V30 21 1 19 22 +M V30 22 2 20 23 +M V30 23 1 21 24 +M V30 24 2 22 25 +M V30 25 1 22 26 +M V30 26 2 24 27 +M V30 27 1 6 7 +M V30 28 2 12 13 +M V30 29 1 20 21 +M V30 30 1 25 27 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 6) +M V30 END COLLECTION +M V30 END CTAB +M END +> +873 + +> +Z113879598 + +> +406.479 + +> +-1.766 + +> +1 + +> +114.520 + +> +5 + +> +parp2 + +> + + +> + + +$$$$ +Compound 874 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 26 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C -1.3046 -0.7404 0.0 0 +M V30 3 C -1.3163 -2.2214 0.0 0 +M V30 4 C -2.6092 0.0235 0.0 0 +M V30 5 C -2.621 -2.9501 0.0 0 +M V30 6 C -3.9139 -0.7169 0.0 0 +M V30 7 Cl -2.6327 -4.431 0.0 0 +M V30 8 C -3.9256 -2.1978 0.0 0 +M V30 9 O -5.2185 0.047 0.0 0 +M V30 10 C -6.5231 -0.6934 0.0 0 +M V30 11 C -7.8278 0.0705 0.0 0 +M V30 12 O -7.8395 1.5749 0.0 0 +M V30 13 N -9.1324 -0.6699 0.0 0 +M V30 14 C -10.437 0.094 0.0 0 +M V30 15 C -10.4488 1.5984 0.0 0 +M V30 16 C -11.7417 -0.6464 0.0 0 +M V30 17 C -11.7534 2.3506 0.0 0 +M V30 18 C -13.0463 0.1175 0.0 0 +M V30 19 C -11.7652 3.8551 0.0 0 +M V30 20 C -13.058 1.6102 0.0 0 +M V30 21 C -14.3509 -0.6229 0.0 0 +M V30 22 O -10.484 4.6073 0.0 0 +M V30 23 N -13.0698 4.6073 0.0 0 +M V30 24 O -15.6556 0.141 0.0 0 +M V30 25 N -14.3627 -2.1038 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 1 6 9 +M V30 9 1 9 10 +M V30 10 1 10 11 +M V30 11 2 11 12 +M V30 12 1 11 13 +M V30 13 1 13 14 +M V30 14 2 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 2 16 18 +M V30 18 1 17 19 +M V30 19 2 17 20 +M V30 20 1 18 21 +M V30 21 2 19 22 +M V30 22 1 19 23 +M V30 23 2 21 24 +M V30 24 1 21 25 +M V30 25 1 6 8 +M V30 26 1 18 20 +M V30 END BOND +M V30 END CTAB +M END +> +874 + +> +Z17833956 + +> +382.198 + +> +1.831 + +> +3 + +> +124.510 + +> +6 + +> +parp14 + +> + + +> + + +$$$$ +Compound 875 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 25 28 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C -1.3147 0.7699 0.0 0 +M V30 3 C 1.291 0.7699 0.0 0 +M V30 4 N -1.4806 2.2742 0.0 0 +M V30 5 N -2.7006 0.1658 0.0 0 +M V30 6 C 2.5821 0.0236 0.0 0 +M V30 7 N -2.9612 2.594 0.0 0 +M V30 8 C -3.0204 -1.291 0.0 0 +M V30 9 C -3.7192 1.291 0.0 0 +M V30 10 N 3.8732 0.7936 0.0 0 +M V30 11 N 2.5703 -1.4687 0.0 0 +M V30 12 C -1.9188 -2.286 0.0 0 +M V30 13 C -4.4655 -1.7411 0.0 0 +M V30 14 C 5.1643 0.0473 0.0 0 +M V30 15 C 3.8614 -2.2149 0.0 0 +M V30 16 C -2.2386 -3.7429 0.0 0 +M V30 17 C -0.4974 -1.8122 0.0 0 +M V30 18 C -4.7853 -3.1981 0.0 0 +M V30 19 C 5.1525 -1.445 0.0 0 +M V30 20 C 6.4554 0.8172 0.0 0 +M V30 21 O 3.8495 -3.7074 0.0 0 +M V30 22 C -3.6837 -4.193 0.0 0 +M V30 23 C 6.4435 -2.1912 0.0 0 +M V30 24 C 7.7465 0.071 0.0 0 +M V30 25 C 7.7346 -1.4213 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 6 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 1 10 14 +M V30 14 1 11 15 +M V30 15 1 12 16 +M V30 16 1 12 17 +M V30 17 2 13 18 +M V30 18 2 14 19 +M V30 19 1 14 20 +M V30 20 2 15 21 +M V30 21 2 16 22 +M V30 22 1 19 23 +M V30 23 2 20 24 +M V30 24 2 23 25 +M V30 25 2 7 9 +M V30 26 1 15 19 +M V30 27 1 18 22 +M V30 28 1 24 25 +M V30 END BOND +M V30 END CTAB +M END +> +875 + +> +Z17844323 + +> +349.410 + +> +2.065 + +> +1 + +> +72.170 + +> +4 + +> +parp3 + +> + + +> + + +$$$$ +Compound 876 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 26 29 0 0 0 +M V30 BEGIN ATOM +M V30 1 S 0.0 0.0 0.0 0 +M V30 2 C 0.8736 1.2278 0.0 0 +M V30 3 C 0.6021 -1.3576 0.0 0 +M V30 4 N 2.3612 1.2396 0.0 0 +M V30 5 N 0.4014 2.6681 0.0 0 +M V30 6 C -0.2951 -2.5619 0.0 0 +M V30 7 N 2.8098 2.6799 0.0 0 +M V30 8 C -1.0389 3.1404 0.0 0 +M V30 9 C 1.6056 3.5654 0.0 0 +M V30 10 O -1.8063 -2.5501 0.0 0 +M V30 11 N 0.1534 -3.9786 0.0 0 +M V30 12 C -1.3576 4.6161 0.0 0 +M V30 13 C -2.1605 2.1487 0.0 0 +M V30 14 C 1.5938 5.0766 0.0 0 +M V30 15 N -2.2785 -3.9668 0.0 0 +M V30 16 C -1.0743 -4.8522 0.0 0 +M V30 17 C -2.798 5.0884 0.0 0 +M V30 18 C -0.2597 5.6314 0.0 0 +M V30 19 C -3.6008 2.6209 0.0 0 +M V30 20 C -1.0861 -6.3398 0.0 0 +M V30 21 C -3.9196 4.0967 0.0 0 +M V30 22 C 0.2007 -7.0718 0.0 0 +M V30 23 C -2.3966 -7.0718 0.0 0 +M V30 24 C 0.1888 -8.5593 0.0 0 +M V30 25 C -2.4084 -8.5593 0.0 0 +M V30 26 C -1.1215 -9.3031 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 1 3 +M V30 3 2 2 4 +M V30 4 1 2 5 +M V30 5 1 3 6 +M V30 6 1 4 7 +M V30 7 1 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 2 6 11 +M V30 11 2 8 12 +M V30 12 1 8 13 +M V30 13 1 9 14 +M V30 14 1 10 15 +M V30 15 1 11 16 +M V30 16 1 12 17 +M V30 17 1 12 18 +M V30 18 2 13 19 +M V30 19 1 16 20 +M V30 20 2 17 21 +M V30 21 2 20 22 +M V30 22 1 20 23 +M V30 23 1 22 24 +M V30 24 2 23 25 +M V30 25 2 24 26 +M V30 26 2 7 9 +M V30 27 2 15 16 +M V30 28 1 19 21 +M V30 29 1 25 26 +M V30 END BOND +M V30 END CTAB +M END +> +876 + +> +Z17854721 + +> +363.436 + +> +3.621 + +> +0 + +> +69.630 + +> +4 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1898239 + +> +0.88 + +$$$$ +Compound 877 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 24 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -1.302 0.7507 0.0 0 +M V30 3 F -2.0528 -0.5278 0.0 0 +M V30 4 F -0.563 2.0528 0.0 0 +M V30 5 C -2.6041 1.5014 0.0 0 +M V30 6 C -2.6158 3.0029 0.0 0 +M V30 7 C -3.9062 0.7742 0.0 0 +M V30 8 C -3.9179 3.7654 0.0 0 +M V30 9 C -5.2083 1.5249 0.0 0 +M V30 10 C -5.22 3.0264 0.0 0 +M V30 11 O -6.5221 3.7889 0.0 0 +M V30 12 C -7.8242 3.0499 0.0 0 +M V30 13 C -9.1263 3.8124 0.0 0 +M V30 14 O -9.138 5.3139 0.0 0 +M V30 15 N -10.4283 3.0733 0.0 0 +M V30 16 C -11.7304 3.8358 0.0 0 +M V30 17 C -13.0325 3.0968 0.0 0 +M V30 18 C -11.7421 5.3373 0.0 0 +M V30 19 C -14.3346 3.8593 0.0 0 +M V30 20 C -13.0442 6.0881 0.0 0 +M V30 21 C -15.6367 3.1203 0.0 0 +M V30 22 C -14.3463 5.3608 0.0 0 +M V30 23 O -15.6484 1.6422 0.0 0 +M V30 24 C -16.9387 3.8827 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 2 5 6 +M V30 6 1 5 7 +M V30 7 1 6 8 +M V30 8 2 7 9 +M V30 9 2 8 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 2 13 14 +M V30 14 1 13 15 +M V30 15 1 15 16 +M V30 16 2 16 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 2 18 20 +M V30 20 1 19 21 +M V30 21 2 19 22 +M V30 22 2 21 23 +M V30 23 1 21 24 +M V30 24 1 9 10 +M V30 25 1 20 22 +M V30 END BOND +M V30 END CTAB +M END +> +877 + +> +Z17856233 + +> +337.293 + +> +3.714 + +> +1 + +> +55.400 + +> +6 + +> +parp14 + +> + + +> + + +$$$$ +Compound 878 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 23 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -1.305 0.7524 0.0 0 +M V30 3 F -2.0575 -0.529 0.0 0 +M V30 4 F -0.5761 2.0575 0.0 0 +M V30 5 C -2.6101 1.5049 0.0 0 CFG=1 +M V30 6 O -3.9151 0.7759 0.0 0 +M V30 7 C -2.6218 3.0098 0.0 0 +M V30 8 N -3.9269 3.7623 0.0 0 +M V30 9 C -3.9386 5.2672 0.0 0 +M V30 10 O -2.6571 6.0197 0.0 0 +M V30 11 N -5.2437 6.0197 0.0 0 +M V30 12 C -5.2554 7.5246 0.0 0 +M V30 13 C -6.5605 8.277 0.0 0 +M V30 14 C -3.9739 8.277 0.0 0 +M V30 15 O -7.8655 7.5481 0.0 0 +M V30 16 C -6.5722 9.782 0.0 0 +M V30 17 C -3.9857 9.782 0.0 0 +M V30 18 C -9.1706 8.3006 0.0 0 +M V30 19 C -5.2907 10.5344 0.0 0 +M V30 20 C -10.4756 7.5716 0.0 0 +M V30 21 C -11.7807 8.3241 0.0 0 +M V30 22 O -11.7925 9.829 0.0 0 +M V30 23 N -13.0858 7.5951 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 2 5 +M V30 5 1 5 6 CFG=1 +M V30 6 1 5 7 +M V30 7 1 7 8 +M V30 8 1 8 9 +M V30 9 2 9 10 +M V30 10 1 9 11 +M V30 11 1 11 12 +M V30 12 2 12 13 +M V30 13 1 12 14 +M V30 14 1 13 15 +M V30 15 1 13 16 +M V30 16 2 14 17 +M V30 17 1 15 18 +M V30 18 2 16 19 +M V30 19 1 18 20 +M V30 20 1 20 21 +M V30 21 2 21 22 +M V30 22 1 21 23 +M V30 23 1 17 19 +M V30 END BOND +M V30 BEGIN COLLECTION +M V30 MDLV30/STERAC1 ATOMS=(1 5) +M V30 END COLLECTION +M V30 END CTAB +M END +> +878 + +> +Z596236262 + +> +335.279 + +> +0.371 + +> +4 + +> +113.680 + +> +8 + +> +ATM + +> + + +> + + +$$$$ +Compound 879 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 32 36 0 0 0 +M V30 BEGIN ATOM +M V30 1 O 0.0 0.0 0.0 0 +M V30 2 C -1.0163 1.1226 0.0 0 +M V30 3 N -2.4935 0.8272 0.0 0 +M V30 4 C -0.5672 2.5644 0.0 0 +M V30 5 C -2.9662 -0.5908 0.0 0 +M V30 6 C 0.8508 3.0371 0.0 0 +M V30 7 C -1.4654 3.7935 0.0 0 +M V30 8 C -4.4434 -0.8863 0.0 0 +M V30 9 C -1.9735 -1.6899 0.0 0 +M V30 10 N 2.139 2.2926 0.0 0 +M V30 11 N 0.839 4.5498 0.0 0 +M V30 12 N -0.5908 5.0225 0.0 0 +M V30 13 C -4.9161 -2.3044 0.0 0 +M V30 14 C -2.4462 -3.108 0.0 0 +M V30 15 C 3.4271 3.0489 0.0 0 +M V30 16 C 2.1271 5.3061 0.0 0 +M V30 17 C -3.9234 -3.4035 0.0 0 +M V30 18 C 3.4153 4.5616 0.0 0 +M V30 19 C 2.1153 6.8188 0.0 0 +M V30 20 N -4.3962 -4.8216 0.0 0 +M V30 21 C 0.8036 7.5751 0.0 0 +M V30 22 C 3.4035 7.5751 0.0 0 +M V30 23 C -5.8734 -5.117 0.0 0 +M V30 24 C -3.4035 -5.9207 0.0 0 +M V30 25 C 0.7917 9.0878 0.0 0 +M V30 26 C 3.3916 9.0878 0.0 0 +M V30 27 C -6.3461 -6.5352 0.0 0 +M V30 28 C -3.8762 -7.3388 0.0 0 +M V30 29 C 2.0799 9.8441 0.0 0 +M V30 30 N -5.3534 -7.6342 0.0 0 +M V30 31 C -5.8261 -9.0524 0.0 0 +M V30 32 C -4.8334 -10.1514 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 2 1 2 +M V30 2 1 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 1 6 10 +M V30 10 1 6 11 +M V30 11 2 7 12 +M V30 12 1 8 13 +M V30 13 2 9 14 +M V30 14 2 10 15 +M V30 15 1 11 16 +M V30 16 2 13 17 +M V30 17 1 15 18 +M V30 18 1 16 19 +M V30 19 1 17 20 +M V30 20 2 19 21 +M V30 21 1 19 22 +M V30 22 1 20 23 +M V30 23 1 20 24 +M V30 24 1 21 25 +M V30 25 2 22 26 +M V30 26 1 23 27 +M V30 27 1 24 28 +M V30 28 2 25 29 +M V30 29 1 27 30 +M V30 30 1 30 31 +M V30 31 1 31 32 +M V30 32 1 11 12 +M V30 33 1 14 17 +M V30 34 2 16 18 +M V30 35 1 26 29 +M V30 36 1 28 30 +M V30 END BOND +M V30 END CTAB +M END +> +879 + +> +Z466174702 + +> +426.514 + +> +4.110 + +> +1 + +> +65.770 + +> +5 + +> +Serine/threonine-protein kinase Chk1 + +> +CHEMBL1997759 + +> +0.86 + +$$$$ +Compound 880 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 32 36 0 0 0 +M V30 BEGIN ATOM +M V30 1 Cl 0.0 0.0 0.0 0 +M V30 2 C 1.4725 -0.1413 0.0 0 +M V30 3 C 2.3442 1.0837 0.0 0 +M V30 4 C 2.0732 -1.496 0.0 0 +M V30 5 C 1.7198 2.462 0.0 0 +M V30 6 C 3.8167 0.9424 0.0 0 +M V30 7 C 3.5458 -1.6374 0.0 0 +M V30 8 N 0.2473 2.78 0.0 0 +M V30 9 N 2.4502 3.7696 0.0 0 +M V30 10 C 4.4175 -0.4123 0.0 0 +M V30 11 O 0.0824 4.2761 0.0 0 +M V30 12 C 1.4371 4.8887 0.0 0 +M V30 13 C 1.7316 6.3612 0.0 0 +M V30 14 S 0.6125 7.3743 0.0 0 +M V30 15 C -0.8246 6.9266 0.0 0 +M V30 16 N -1.2958 5.513 0.0 0 +M V30 17 N -2.0497 7.8219 0.0 0 +M V30 18 N -2.8036 5.5248 0.0 0 +M V30 19 C -2.0615 9.3298 0.0 0 +M V30 20 C -3.2748 6.9502 0.0 0 +M V30 21 C -0.7774 10.0837 0.0 0 +M V30 22 C -3.3691 10.0837 0.0 0 +M V30 23 C -4.712 7.4214 0.0 0 +M V30 24 C -0.7892 11.5915 0.0 0 +M V30 25 C 0.5065 9.3533 0.0 0 +M V30 26 C -3.3808 11.5915 0.0 0 +M V30 27 C -5.8311 6.4319 0.0 0 +M V30 28 C -5.03 8.8939 0.0 0 +M V30 29 C -2.0968 12.3455 0.0 0 +M V30 30 C -7.2683 6.9031 0.0 0 +M V30 31 C -6.4672 9.3651 0.0 0 +M V30 32 N -7.5863 8.3756 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 1 3 6 +M V30 6 2 4 7 +M V30 7 2 5 8 +M V30 8 1 5 9 +M V30 9 2 6 10 +M V30 10 1 8 11 +M V30 11 2 9 12 +M V30 12 1 12 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 2 15 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 1 17 19 +M V30 19 1 17 20 +M V30 20 2 19 21 +M V30 21 1 19 22 +M V30 22 1 20 23 +M V30 23 1 21 24 +M V30 24 1 21 25 +M V30 25 2 22 26 +M V30 26 2 23 27 +M V30 27 1 23 28 +M V30 28 2 24 29 +M V30 29 1 27 30 +M V30 30 2 28 31 +M V30 31 2 30 32 +M V30 32 1 7 10 +M V30 33 1 11 12 +M V30 34 2 18 20 +M V30 35 1 26 29 +M V30 36 1 31 32 +M V30 END BOND +M V30 END CTAB +M END +> +880 + +> +Z92073297 + +> +460.939 + +> +4.517 + +> +0 + +> +82.520 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL1552719 + +> +0.86 + +$$$$ +Compound 881 +Actelion Java MolfileCreator 2.0 + + 0 0 0 0 0 0 0 V3000 +M V30 BEGIN CTAB +M V30 COUNTS 23 25 0 0 0 +M V30 BEGIN ATOM +M V30 1 F 0.0 0.0 0.0 0 +M V30 2 C -0.0118 1.5178 0.0 0 +M V30 3 C 1.2807 2.2768 0.0 0 +M V30 4 C -1.3281 2.2768 0.0 0 +M V30 5 C 1.2688 3.7947 0.0 0 +M V30 6 C -1.34 3.7947 0.0 0 +M V30 7 C 2.5614 4.5536 0.0 0 +M V30 8 C -0.0474 4.5536 0.0 0 +M V30 9 O 2.5495 6.0715 0.0 0 +M V30 10 N 3.854 3.8065 0.0 0 +M V30 11 C 5.1466 4.5655 0.0 0 +M V30 12 C 6.4391 3.8184 0.0 0 +M V30 13 C 7.7317 4.5773 0.0 0 +M V30 14 N 9.0243 3.8303 0.0 0 +M V30 15 C 9.0124 2.3361 0.0 0 +M V30 16 C 10.3169 4.5892 0.0 0 +M V30 17 C 10.305 1.6009 0.0 0 +M V30 18 C 11.6095 3.8421 0.0 0 +M V30 19 C 11.5976 2.3598 0.0 0 +M V30 20 C 10.2932 0.1067 0.0 0 +M V30 21 C 12.8902 1.6246 0.0 0 +M V30 22 C 11.5858 -0.6285 0.0 0 +M V30 23 C 12.8783 0.1304 0.0 0 +M V30 END ATOM +M V30 BEGIN BOND +M V30 1 1 1 2 +M V30 2 2 2 3 +M V30 3 1 2 4 +M V30 4 1 3 5 +M V30 5 2 4 6 +M V30 6 1 5 7 +M V30 7 2 5 8 +M V30 8 2 7 9 +M V30 9 1 7 10 +M V30 10 1 10 11 +M V30 11 1 11 12 +M V30 12 1 12 13 +M V30 13 1 13 14 +M V30 14 1 14 15 +M V30 15 1 14 16 +M V30 16 1 15 17 +M V30 17 1 16 18 +M V30 18 2 17 19 +M V30 19 1 17 20 +M V30 20 1 19 21 +M V30 21 2 20 22 +M V30 22 2 21 23 +M V30 23 1 6 8 +M V30 24 1 18 19 +M V30 25 1 22 23 +M V30 END BOND +M V30 END CTAB +M END +> +881 + +> +Z364384872 + +> +312.381 + +> +3.683 + +> +1 + +> +32.340 + +> +5 + +> +Poly [ADP-ribose] polymerase-1 + +> +CHEMBL3629672 + +> +0.89 + +$$$$ diff --git a/docs/tutorials/images/Conformers_1.png b/docs/tutorials/images/Conformers_1.png new file mode 100644 index 0000000000000000000000000000000000000000..20bbfc3656de8738663059e875e158d62bf87343 GIT binary patch literal 310889 zcmY&;Qq}`H{I2u_9v52zgmC zSSU;=0001332|XX007`K006)S2%vv@-lROB0RUiZEQEyQC4_|VlJ5)Y3Z5Buy)x&pH$X`=a#VjyWZ2Q2ww90*VdCS|CFv zDiW0NTdR;uMg*t>8=`608YWGcD#yp+Fqso2laU{^2C@s_!@;XQ7=>so)PW@XNc33K z>sd$14@D^K2Z0V0L74c|DRJNMf6=2|HSkYK;vW4O8xe*-89)mTrgR$3fgT!;*xPWN@Gld3MJqj#EI1M6?1NWN*SB(hkD)uLnC;A9;qs8V%x5LGbeD|xx zTk>73%Olk+j)(U&Hiy$h6pA1if-pp%$7LG;w&&x<)D7CRA53qX)yLhvf|P^24i*c( z42B!HjwtY0z$?Ip<;3?(t3XZPT?J|l$jWi2zky#TV5*750yNtK{xH}Nm}?|kYEW^Y 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defined as mathematical representations of molecules’ properties that are generated by algorithms. The numerical values of molecular descriptors are used to quantitatively describe the physical and chemical information of the molecules. An example of molecular descriptors is the LogP which is a quantitative representation of the [lipophilicity](https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/lipophilicity) of the molecules, it is obtained by measuring the partitioning of the molecule between an aqueous phase and a lipophilic phase which consists usually of water/*n*-octanol. - [source](https://www.sciencedirect.com/topics/medicine-and-dentistry/molecular-descriptor)\n", - "> \n", - "\n", - "Molecular descriptors can generally classified in four ways:\n", - "\n", - "![image.png](attachment:c13f670f-e6ec-4364-adc7-044cea35600c.png)\n", - "\n", - "([source](https://chem.libretexts.org/Courses/Intercollegiate_Courses/Cheminformatics_OLCC_(2019)/05%3A_5._Quantitative_Structure_Property_Relationships/5.03%3A_Molecular_Descriptors))\n", - "\n", - "## Tutorial\n", - "\n", - "In this tutorial, we’ll show how descriptors can be useful as filters in the drug discovery process. This tutorial was inspired from the [TeachOpenCADD talktorial](https://projects.volkamerlab.org/teachopencadd/talktorials/T002_compound_adme.html?highlight=descriptors), we highly encourage you to read through the theory, understand ADME and why we care about it in the drug discovery process from the talktorial before diving into this tutorial. It provides the necessary background information to fully understand the purpose of this tutorial. \n", - "\n", - "The set of descriptors that will be focused on today are: \n", - "\n", - "- Molecular weight ≤ 500 Da\n", - "- Number of hydrogen bond acceptors (HBAs) ≤ 10\n", - "- Number of hydrogen bond donors (HBD) ≤ 5\n", - "- Calculated LogP (octanol-water coefficient) ≤ 5\n", - "\n", - "These descriptors and their limits are collectively known as **[Lipinkski’s rule of five (Ro5)](https://www.sciencedirect.com/science/article/abs/pii/S0169409X96004231)**, this is a method used to estimate a compounds bioavailability based solely on its chemical structure. If a molecule violates any of the rules listed above (i.e. a molecular weight of 700 Da), it’s probable that the compound will **exhibit poor absorption or permeation** and subsequently be removed from your list.\n", - "\n", - "## Tutorial\n", - "\n", - "This tutorial will show you a real-world scenario of \n", - "\n", - "- **Part 1:** Obtaining a virtual screening library from **[Enamine](https://enamine.net/compound-libraries/targeted-libraries/dna-library)**\n", - " - The DNA library is designed to identify novel active compounds against proteins which are essential for DNA stability. At 5530 compounds, this is one of Enamine’s smaller libraries. The same functions could easily be applied to some of the larger libraries using Datamol’s parallelize functions.\n", - "- **Part 2:** Then calculate the relevant molecular properties for the Ro5 for the list\n", - "- **Part 3:** Investigate compliance with Ro5\n", - "- **Part 4:** And finally, revealing the statistics for the dataset of compounds using Ro5 as a filter. With this, we will be able to find the answer to our question; how many fulfill vs. violate Ro5?\n", - " - Subsequently, we can show different ways of displaying the data to make it more visually appealing using Matplotlib" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c2136858", - "metadata": {}, - "outputs": [], - "source": [ - "import datamol as dm\n", - "\n", - "# Part 1: Obtain a list of molecules and visualize\n", - "# Load sdf downloaded from Enamine with the flag as_df set to True\n", - "# This will automatically create a 'smiles' column from the sdf file\n", - "data = dm.read_sdf('/home/data/Enamine_DNA_Libary_5530cmpds_20200831.sdf', as_df=True)\n", - "smiles = data[\"smiles\"].iloc[:].tolist()\n", - "mols = [dm.to_mol(s) for s in smiles]\n", - "dm.to_image(mols[900:909], n_cols = 3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "396835e5", - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate a specific descriptor for a compound\n", - "n_aromatic_atoms = dm.descriptors.n_aromatic_atoms(mols[0])\n", - "print(\"Number of aromatic atoms in the compound is\", n_aromatic_atoms)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "41388855", - "metadata": {}, - "outputs": [], - "source": [ - "# Part 2: Calculate the relevant molecular properties for the Ro5 for the list\n", - "\n", - "# Calculate many descriptors for a compound\n", - "dm.descriptors.compute_many_descriptors(mols[900])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "108c4837", - "metadata": {}, - "outputs": [], - "source": [ - "# Batch compute many descriptors for a list of compounds\n", - "df = dm.descriptors.batch_compute_many_descriptors(mols)\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6733ca23", - "metadata": {}, - "outputs": [], - "source": [ - "# Part 3: Investigate compliance with Ro5\n", - "\n", - "df = df[df['mw'] <= 500]\n", - "df = df[df['n_lipinski_hba'] <= 10]\n", - "df = df[df['n_lipinski_hbd'] <= 5]\n", - "df = df[df['clogp'] <= 5]\n", - "df\n", - "\n", - "# 5350 of the 5530 compounds in the dataset satisfy all criteria in the rule of 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dc9f95ed", - "metadata": {}, - "outputs": [], - "source": [ - "# Part 4: Reveal the statistics for the dataset of compounds using Ro5 as a filter. How many fulfill vs. violate Ro5? \n", - "# Plotting the RO5 descriptors\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "fig, axs = plt.subplots(ncols=4, figsize=(25, 6))\n", - "plt.rcParams['font.size'] = 12\n", - "sns.histplot(df, x='mw', ax=axs[0])\n", - "sns.histplot(df, x='n_lipinski_hba', ax=axs[1])\n", - "sns.histplot(df, x='n_lipinski_hbd', ax=axs[2])\n", - "sns.histplot(df, x='clogp', ax=axs[3])" - ] - }, - { - "cell_type": "markdown", - "id": "415ac881", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "If you’re curious to learn more about some of the other established rules in the drug discovery industry, feel free to run this list through a Google search: \n", - "\n", - "- Rules of CNS\n", - "- BBB score\n", - "- Rule of Egan\n", - "- Rule-of-5\n", - "- Beyond Rule-of-5\n", - "- Rule-of-4\n", - "- Ghose Filter\n", - "- Zinc Rule\n", - "- Rule of GSK (4/400)\n", - "- Lead-Like Soft Rule\n", - "- Oprea’s Rule\n", - "- Pfizer Rule (3/75)\n", - "- REOS Filter\n", - "- Rule-of-3\n", - "- Extended Rule-of-3\n", - "- Veber Filter\n", - "\n", - "## References:\n", - "\n", - "- TeachOpenCADD - [https://projects.volkamerlab.org/teachopencadd/talktorials/T002_compound_adme.html?highlight=descriptors](https://projects.volkamerlab.org/teachopencadd/talktorials/T002_compound_adme.html?highlight=descriptors)\n", - "- ADME criteria ([Wikipedia](https://en.wikipedia.org/wiki/ADME) and [Mol Pharm. (2010), 7(5), 1388-1405](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3025274/))\n", - "- What are lead compounds? ([Wikipedia](https://en.wikipedia.org/wiki/Lead_compound))\n", - "- What is the LogP value? ([Wikipedia](https://en.wikipedia.org/wiki/Partition_coefficient))\n", - "- Lipinski et al. “Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings.” ([Adv. Drug Deliv. Rev. (1997), 23, 3-25](https://www.sciencedirect.com/science/article/pii/S0169409X96004231))" - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "main_language": "python", - "notebook_metadata_filter": "-all" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/tutorials/new/Fuzzy_Scaffolds.ipynb b/docs/tutorials/new/Fuzzy_Scaffolds.ipynb deleted file mode 100644 index e21e9ad8..00000000 --- a/docs/tutorials/new/Fuzzy_Scaffolds.ipynb +++ /dev/null @@ -1,3451 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fa974713", - "metadata": {}, - "source": [ - "# Fuzzy Scaffolds\n", - "\n", - "Fuzzy scaffolding is a concept useful for scaffold decoration and constrained scaffolding. If you want finer control over the generation of your scaffolds, you can use the fuzzy scaffold function to **enforce specific groups** that need to appear in the core as a sort of pharmacophore requirement.\n", - "\n", - "**Note:** A pharmacophore is essentially “[a part of a molecular structure that is responsible for a particular biological or pharmacological interaction that it undergoes](https://link.springer.com/referenceworkentry/10.1007/978-3-642-16483-5_4502)”. \n", - "\n", - "You can also force R groups to be included as well, allowing for flexibility to reconstruct specified positions (attachment points) in the scaffold. Overall, it allows you to build a highly specific [molecular series to be used for MMPA](https://pubs.acs.org/doi/10.1021/jm500022q#:~:text=A%20matched%20molecular%20series%20is,groups%20at%20the%20same%20position.). \n", - "\n", - "## Understanding Key Parameters\n", - "\n", - "- **enforce_subs -** this lets you specify what substructure(s) you want to enforce on the scaffold\n", - "- **n_atom_cuttoff** - the minimum number of atoms a core should have. The smaller the number, the smaller the new scaffolds will be or the lesser number of new scaffolds will be generated, vice versa is true.\n", - "- **ignore_non_ring -** Some scaffolds might be a simple aliphatic chain, in other words, a molecule that only contains straight/branched chains with no rings. Most of the time, you want to ***ignore these scaffolds*** as they typically don’t translate well in a drug like context.\n", - "- **mcs_params -** This is quite a niche parameter. If two molecules in your dataset have a different Murcko scaffold but the same Minimum Common scaffold, toggling this argument will categorize these molecules into the same bucket using a [maximum common substructure algorithm](https://www.rdkit.org/docs/GettingStartedInPython.html#maximum-common-substructure).\n", - "\n", - "## Datamol Example" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "de75702a", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:39] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:39] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:39] WARNING: not 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atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neiRDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "ghbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom witRDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "hout neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not remoRDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "ving hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: nRDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "ot removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighborRDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "s\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:4RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "0] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen aRDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "tom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removRDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "ing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummRDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "y atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:40] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom witRDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "h dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neigRDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "hbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removRDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "ing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removinRDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "g hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogenRDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - " atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING:RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - " not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighboRDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "rs\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighborRDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "s\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom with dummy atom neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "RDKit WARNING: [12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n", - "[12:45:41] WARNING: not removing hydrogen atom without neighbors\n" - ] - }, - { - "data": { - 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"file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/tutorials/new/Generate_Scaffold.ipynb b/docs/tutorials/new/Generate_Scaffold.ipynb deleted file mode 100644 index f90159cf..00000000 --- a/docs/tutorials/new/Generate_Scaffold.ipynb +++ /dev/null @@ -1,598 +0,0 @@ -{ - "cells": [ - { - "attachments": { - "81a46677-723c-42db-b6a3-281143cf3ae0.png": { - "image/png": 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- } - }, - "cell_type": "markdown", - "id": "2fc73a86", - "metadata": {}, - "source": [ - "# Generate Scaffold\n", - "\n", - "## Introduction to Scaffolds\n", - "\n", - "A scaffold is best defined as a **molecular core** of a compound where R groups are attached via attachment points. Scaffolds are important in drug discovery as they help us uncover structure-activity relationships ([SAR](https://info.collaborativedrug.com/tofu-content-what-is-sar)) and often are found to be essential for the bioactivity of a given class of compounds. The general idea behind this approach is finding relationships between the structure of a compound and its properties such as biological activity and/or physicochemical properties. \n", - "\n", - "There are multiple ways to define a scaffold and this can make it hard to compare the results of different, independent studies that involve scaffolds. If you’re interested, you can read more about scaffolding [here](https://datagrok.ai/help/domains/chem/functions/murcko-scaffolds) and [here](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3328312/). Most of the time, we rely on the Murcko scaffold which is also known as the [Bemis-Murcko](https://pubs.acs.org/doi/10.1021/jm9602928) framework. \n", - "\n", - "From [Chemaxon](https://docs.chemaxon.com/display/docs/bemis-murcko-clustering.md): “Bemis and Murcko outlined a popular method for deriving scaffolds from molecules by removing side chain atoms. A molecular framework can be interpreted as a graph containing nodes and edges representing atom and bond types, respectively. Removing atom and bond labels or agglomerating nodes by chemotype yields a hierarchy of reduced graphs, or molecular equivalence classes, that represent sets of related molecules. Likewise, a framework can be further decomposed into individual rings (or the core ring assembly) using chemically intuitive rules: the rings can individually or jointly be considered as scaffolds derived from the original compound.” \n", - "\n", - "Scaffolds are generally useful in two main ways: \n", - "\n", - "1. Identifying core structures that have preferential activity against some specific target classes. This can then serve as a “building block” to further optimize active compounds on certain properties through the modification of R groups that are attached to the scaffold (sometimes referred to as scaffold decorations).\n", - "2. Scaffold hopping - finding structurally distinct compounds that have the same activity\n", - "\n", - "Scaffold hopping is particularly useful in [ligand](https://en.wikipedia.org/wiki/Ligand_(biochemistry))-based virtual screening methods where the information of known active compounds is used for hit identification and optimization rather than the available structural data for the target protein. In this approach, you start with a search template (i.e. the scaffold of the known active compound with all the decorations), keep the decorations the same and replace the scaffold itself with a similar molecular structure\n", - "\n", - "Below is an image showing a network of possible scaffolds for a given molecule A: \n", - "\n", - "![image.png](attachment:81a46677-723c-42db-b6a3-281143cf3ae0.png)\n", - "\n", - "If you’re interested in learning more about scaffolds and how we explore scaffolds computationally, read this [paper](https://pubs.acs.org/doi/10.1021/acs.jmedchem.5b01746). \n", - "\n", - "## Tutorial\n", - "\n", - "So, what does this look like in practice? This tutorial will show you some of the basics of using scaffolds in drug discovery. \n", - "\n", - "1. Load an example dataset/list of molecules\n", - "2. Identify the scaffolds\n", - "3. This will then enable you to create a chemical series which can be used in a MMPA (see the [fragmentation](https://www.notion.so/Fragmenting-Compounds-8c861697ae6c44f3991cb215fd93e393) tutorial)\n", - " 1. A molecular series refers to a set of two or more molecules with the same scaffold but different R groups at the same position, read more [here](https://pubs.acs.org/doi/10.1021/jm500022q#:~:text=A%20matched%20molecular%20series%20is,groups%20at%20the%20same%20position.). Once a molecular series is generated, it enables scientists to focus on studying molecular properties and how changes in the structure are associated in the changes of these values (e.g. SAR studies).\n", - "\n", - "## RDKit Example" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "4d9006c8", - "metadata": {}, - "outputs": [], - "source": [ - "from rdkit import Chem\n", - "from rdkit.Chem.Scaffolds import MurckoScaffold\n", - "from rdkit.Chem.Draw import IPythonConsole, MolsToGridImage\n", - "\n", - "# Load a list of molecules\n", - "smiles_list = [\"CCOC1=CC=CC=C1C(=O)OCC(=O)NC1=CC=CC=C1\",\n", - " \"NC(=O)C1=C(NC(=O)COC2=CC=CC=C2C(F)(F)F)SC=C1\",\n", - " \"CC(C)NC(=O)CSCC1=CC=CC=C1Br\",\n", - " \"CC1=CC=C(C(=O)NC(C)C)C=C1NC(=O)C1=CC=CO1\",\n", - " \"O=C(CN1CCCCCC1=O)NCC1=CC=C(N2C=CN=C2)C(F)=C1\"\n", - " ]\n", - "mol_list = [Chem.MolFromSmiles(smi) for smi in smiles_list]\n", - "scaffolds = [MurckoScaffold.GetScaffoldForMol(mol) for mol in mol_list]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d5b3010f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - 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"\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Extracting Murcko scaffolds from list of compounds\n", - "scaffolds = [dm.to_scaffold_murcko(mol) for mol in mol_list]\n", - "dm.to_image(scaffolds, n_cols=3)" - ] - }, - { - "cell_type": "markdown", - "id": "1e167bb7", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "- What is SAR? [https://info.collaborativedrug.com/tofu-content-what-is-sar](https://info.collaborativedrug.com/tofu-content-what-is-sar)\n", - "- [https://datagrok.ai/help/domains/chem/functions/murcko-scaffolds](https://datagrok.ai/help/domains/chem/functions/murcko-scaffolds)\n", - "- [http://practicalcheminformatics.blogspot.com/2021/10/exploratory-data-analysis-with.html](http://practicalcheminformatics.blogspot.com/2021/10/exploratory-data-analysis-with.html)" - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "main_language": "python", - "notebook_metadata_filter": "-all" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/tutorials/new/Generating_Conformers.ipynb b/docs/tutorials/new/Generating_Conformers.ipynb deleted file mode 100644 index c2c06af4..00000000 --- a/docs/tutorials/new/Generating_Conformers.ipynb +++ /dev/null @@ -1,612 +0,0 @@ -{ - "cells": [ - { - "attachments": { - "3411d6e3-7efa-43fe-8b8a-669d68efdf0b.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "id": "ad8338f0", - "metadata": {}, - "source": [ - "# Generating Conformers\n", - "\n", - "A drug-like molecule can exist in a variety of diverse 3D shapes depending on the number of rotatable bonds, bond order, torsion, and in general, its degree of freedom. Each individual 3D spatial arrangement of a molecule is defined as ***conformer*** and each conformer may have different properties (e.g. relative energy). This is why the [sampling of the conformational space](https://pubs.acs.org/doi/full/10.1021/acs.jcim.7b00221), often referred to as conformational search, is a key step to understand the 3D properties of a given compound. You must factor in all the possible conformers and their respective properties in order to achieve the best representation of a molecule. It is a necessary step in any [virtual screening](https://en.wikipedia.org/wiki/Virtual_screening#:~:text=Virtual%20screening%20(VS)%20is%20a,a%20protein%20receptor%20or%20enzyme.) campaign.\n", - "\n", - "**Note:** You can see a good visualization on how relative energy of conformers changes based on manual manipulation of bond angles [here](https://www.sas.upenn.edu/~kimg/mcephome/chem502/ethbutconform/ethbutmm2.html#:~:text=The%20highest%20energy%20conformer%2C%20the,energy%20of%203.5803%20kcal%2F%20mol.). \n", - "\n", - "A common term that you will see throughout this example is ***RMSD*** which stands for root-mean-square deviation. RMSD is widely used as a similarity measure when analyzing conformations: the smaller the RMSD between two conformers, the more similar in 3D spatial arrangement they are. Once conformers are generated, they are usually pruned on RMSD, meaning, structures that are redundant and essentially correspond to the same conformation are removed from the list. \n", - "\n", - "**Note:** RMSD is not the only measure of conformer similarity, and it does have its limitations. If you’re interested in learning more about all the various ways in which chemical structural similarity can be measured, read more [here.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6068280/) \n", - "\n", - "### How are conformers generated?\n", - "\n", - "The current default RDKit method used to generate conformers leverages various versions of experimental-torsion distance geometry with additional basic knowledge ([ETKDG](https://pubs.acs.org/doi/full/10.1021/acs.jcim.5b00654)) created by Riniker and Landrum. From the RDKit book the default algorithm followed is:\n", - "\n", - "1. The molecule’s distance bounds matrix is calculated based on the connection table and a set of rules.\n", - "2. The bounds matrix is smoothed using a triangle-bounds smoothing algorithm.\n", - "3. A random distance matrix that satisfies the bound's matrix is generated.\n", - "4. This distance matrix is embedded in 3D dimensions (producing coordinates for each atom).\n", - "5. The resulting coordinates are cleaned up somewhat using the “distance geometry force field”, based on distance constraints from the bounds matrix. \n", - "\n", - "The first 5 steps describe the “ETDG” approach. The additional “K” in ETKDG just defines further constraints from chemical knowledge such as “aromatic rings are to be flat or bonds connected to triple bonds are to be collinear”. These additional constraints introduce a certain level of “chemical awareness” that helps generate correct conformers which are chemically and physically valid. Read more [here](https://www.blopig.com/blog/2016/06/advances-in-conformer-generation-etkdg-and-etdg/), [here](https://greglandrum.github.io/rdkit-blog/conformers/exploration/2021/01/31/looking-at-random-coordinate-embedding.html) and [here](https://greglandrum.github.io/rdkit-blog/conformers/exploration/2021/02/22/etkdg-and-distance-constraints.html). \n", - "\n", - "![image.png](attachment:3411d6e3-7efa-43fe-8b8a-669d68efdf0b.png)\n", - "\n", - "***[Source](https://pubs.acs.org/doi/10.1021/acs.jcim.5b00654)***\n", - "\n", - "## Tutorial\n", - "\n", - "Now let’s start with a tutorial on how you would go about generating conformers via RDKit.\n", - "\n", - "## RDKit Example\n", - "\n", - "Below is an example of how you would go about generating conformers in RDKit." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "4e45a284", - "metadata": {}, - "outputs": [], - "source": [ - "from rdkit import Chem\n", - "from rdkit.Chem import AllChem\n", - "from rdkit.Chem import rdDistGeom\n", - "from rdkit.Chem import rdMolAlign\n", - "from rdkit.Chem import rdMolDescriptors\n", - "from rdkit.Chem import rdMolTransforms\n", - "from rdkit.Chem import rdForceFieldHelpers\n", - "\n", - "from rdkit.Chem import PyMol\n", - "import copy\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "4b9cefae", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = Chem.MolFromSmiles('O=C(C)Oc1ccccc1C(=O)O')\n", - "m" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "e7b4d603", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m2" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "1209acc4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "3" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rotatable_bonds = Chem.rdMolDescriptors.CalcNumRotatableBonds(m2)\n", - "rotatable_bonds" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "07d0df61", - "metadata": {}, - "outputs": [], - "source": [ - "# Setup the parameters for the embedding\n", - "params = getattr(rdDistGeom, \"ETDG\")()\n", - "params.randomSeed = 0\n", - "params.enforceChirality = True\n", - "params.useRandomCoords = True\n", - "params.numThreads = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "c7c224c9", - "metadata": {}, - "outputs": [], - "source": [ - "# EMbed conformers\n", - "confs = rdDistGeom.EmbedMultipleConfs(m2, numConfs=50, params=params)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "8453b480", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "50" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(confs)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "642925b0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0,\n", - " 1,\n", - " 2,\n", - " 3,\n", - " 4,\n", - " 5,\n", - " 6,\n", - " 7,\n", - " 8,\n", - " 9,\n", - " 10,\n", - " 11,\n", - " 12,\n", - " 13,\n", - " 14,\n", - " 15,\n", - " 16,\n", - " 17,\n", - " 18,\n", - " 19,\n", - " 20,\n", - " 21,\n", - " 22,\n", - " 23,\n", - " 24,\n", - " 25,\n", - " 26,\n", - " 27,\n", - " 28,\n", - " 29,\n", - " 30,\n", - " 31,\n", - " 32,\n", - " 33,\n", - " 34,\n", - " 35,\n", - " 36,\n", - " 37,\n", - " 38,\n", - " 39,\n", - " 40,\n", - " 41,\n", - " 42,\n", - " 43,\n", - " 44,\n", - " 45,\n", - " 46,\n", - " 47,\n", - " 48,\n", - " 49]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Minimize energy\n", - "energy_iterations = 200\n", - "results = rdForceFieldHelpers.UFFOptimizeMoleculeConfs(m2, maxIters=energy_iterations)\n", - "energies = [energy for _, energy in results]\n", - "energies = []\n", - "for conf in m2.GetConformers():\n", - " ff = rdForceFieldHelpers.UFFGetMoleculeForceField(m2, confId=conf.GetId())\n", - " energies.append(ff.CalcEnergy())\n", - "energies = np.array(energies)\n", - "# Add the energy as a property to each conformers\n", - "[\n", - " conf.SetDoubleProp(\"rdkit_uff_energy\", energy)\n", - " for energy, conf in zip(energies, m2.GetConformers())\n", - "]\n", - "\n", - "# Now we reorder conformers according to their energies,\n", - "# so the lowest energies conformers are first.\n", - "mol_clone = copy.deepcopy(m2)\n", - "ordered_conformers = [\n", - " conf for _, conf in sorted(zip(energies, mol_clone.GetConformers()), key=lambda x: x[0])\n", - "]\n", - "m2.RemoveAllConformers()\n", - "[m2.AddConformer(conf, assignId=True) for conf in ordered_conformers]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "74756772", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Align conformers to each others\n", - "rdMolAlign.AlignMolConformers(m2)\n", - "m2" - ] - }, - { - "cell_type": "markdown", - "id": "4a5e643a", - "metadata": {}, - "source": [ - "As a beginner, this can be a bit overwhelming and complicated to get the hang of. Let’s see how this would look in Datamol\n", - "\n", - "## Datamol Example" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "aa5b82da", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "import datamol as dm" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "9f55f956", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "smiles = \"O=C(C)Oc1ccccc1C(=O)O\"\n", - "mol = dm.to_mol(smiles)\n", - "mol" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "c03e90f5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# generate conformers\n", - "mol = dm.conformers.generate(mol, align_conformers=True)\n", - "mol" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "912fdfe2", - "metadata": {}, - "outputs": [], - "source": [ - "# Get all conformers as a list\n", - "conformers = mol.GetConformers()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "a1b296b0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "50" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(conformers)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "81fb899c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 2.03942419, -1.45922972, -0.5317635 ],\n", - " [ 1.99263631, -1.56446468, 0.71918407],\n", - " [ 3.17475723, -2.13427287, 1.41002923],\n", - " [ 0.83776239, -1.13909896, 1.35783965],\n", - " [-0.24741278, -0.60913374, 0.65486606],\n", - " [-0.31728327, 0.74698269, 0.41866521],\n", - " [-1.37604602, 1.30106187, -0.27254643],\n", - " [-2.40163014, 0.50559754, -0.74968685],\n", - " [-2.35444025, -0.85475256, -0.52596736],\n", - " [-1.27294042, -1.39641191, 0.17657748],\n", - " [-1.21427441, -2.8252044 , 0.4168274 ],\n", - " [-0.25085537, -3.31954322, 1.04442977],\n", - " [-2.22026164, -3.68264236, -0.04010149]])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Get the 3D atom positions of the first conformer\n", - "positions = mol.GetConformer(0).GetPositions()\n", - "positions" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "9864a4c2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'rdkit_uff_energy': 35.402383024447225}\n" - ] - } - ], - "source": [ - "# If minimization has been enabled (default to True)\n", - "# you can access the computed energy.\n", - "conf = mol.GetConformer(0)\n", - "props = conf.GetPropsAsDict()\n", - "print(props)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "056e862c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.00000000e+00, 1.01201288e+00, 3.80585511e-02, ...,\n", - " 1.00248204e+00, 6.38152829e-02, 9.30034182e-01],\n", - " [1.01201288e+00, 4.67577303e-08, 1.01828888e+00, ...,\n", - " 7.17687634e-02, 1.01744973e+00, 4.35281059e-01],\n", - " [3.80585511e-02, 1.01828888e+00, 4.67577303e-08, ...,\n", - " 1.01013936e+00, 7.97864852e-02, 9.43285047e-01],\n", - " ...,\n", - " [1.00248204e+00, 7.17687634e-02, 1.01013936e+00, ...,\n", - " 0.00000000e+00, 1.00439835e+00, 4.19279897e-01],\n", - " [6.38152831e-02, 1.01744973e+00, 7.97864852e-02, ...,\n", - " 1.00439835e+00, 4.67577303e-08, 9.36046765e-01],\n", - " [9.30034182e-01, 4.35281059e-01, 9.43285047e-01, ...,\n", - " 4.19279897e-01, 9.36046765e-01, 0.00000000e+00]])" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Compute RMSD\n", - "rmsd = dm.conformers.rmsd(mol)\n", - "rmsd" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "5b1878d2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(50, 50)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rmsd.shape" - ] - }, - { - "cell_type": "markdown", - "id": "1a2d80ef", - "metadata": {}, - "source": [ - "**In essentially one line of code, you can generate a list of conformers.** What’s important to understand are some of the key parameters that are factored into this process. In general, sticking with the defaults in Datamol will suffice in most cases, but if you want to make specific modifications, you can. If you’re interested in learning more about all the algorithms underlying conformer generation, read [this](https://pubs.acs.org/doi/10.1021/acs.jcim.7b00221). \n", - "\n", - " A few parameters to highlight: \n", - "\n", - "- **n_confs** - Specifying the number of conformers to generate. This is based on the number of rotatable bonds and, by default, this is set to 200 if there are more than 8 rotatable bonds and 50 if there are less than 8. Theoretically, there are an unlimited number of conformers that can be derived from a single rotatable bond, however, in practice, not all the conformer structures make sense since only “stable” conformers are relevant in this context. This is why the defaults are set in place. Hypothetically, if you only have 2 rotatable bonds and you set n_confs to 2,000,000, not only will this be computationally expensive but a lot of the conformers generated will start to have non-relevant structures that are not useful.\n", - "- **add_hs** - By default, hydrogen atoms are added before embedding because it is critical to generating high quality 3D conformations.\n", - "- **minimize_energy** - Minimizing energy releases the strain of the generated conformation to the closest local minima enabling you to find a more relevant conformation. In other words, ***finding the conformer that is most likely to exist***. There are multiple force fields that you can apply.\n", - "- **method -** Within the ETKDG method, there are various versions that can be selected to generate conformers.\n", - "- **energy_iterations -** This options allows you to specify how many iterations of conformer generation you want to go through if you have enabled energy minimization. In general, the more iterations you specify, the more accurate the conformers. However, there is a trade off between the number of iterations and computation speed. Running through 1000 iterations will be significantly more expensive computationally as opposed to 100 iterations.\n", - "- **rms_cutoff** is the max RMSD value for which two conformers are considered to be the same.\n", - "\n", - "The full table of parameters along with their definitions is shown below:" - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "main_language": "python", - "notebook_metadata_filter": "-all" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/tutorials/new/Preprocessing.ipynb b/docs/tutorials/new/Preprocessing.ipynb deleted file mode 100644 index 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" - } - }, - "cell_type": "markdown", - "id": "0a462b7a", - "metadata": {}, - "source": [ - "# Pre-processing Molecules\n", - "\n", - "You’ve probably heard the adage “garbage in, garbage out” before in reference to the importance of data quality when it comes to AI/ML. The same holds true in the field of drug discovery. Given the scarcity and often non-consistent quality of available data for drug discovery, an initial clean up is almost always required to ensure the use of high quality data in the generation of your models. If you don’t do this, the use of lesser quality data would definitely impact the accuracy of your models in any downstream task. Pre-processing of data and molecules is extremely important, let’s dive in!\n", - "\n", - "## Representing Molecules\n", - "\n", - "There are many ways in which molecules can be represented. In other words, how can we effectively express the complexity of a molecule in a way that machines can understand? Here are some existing methods: \n", - "\n", - "- [Molfile](https://en.wikipedia.org/wiki/Chemical_table_file) - A table that holds information about the atoms, bonds, connectivity and coordinates of a molecule\n", - "- **SMILES** - stands for **S**implified **M**olecular **I**nput **L**ine-**E**ntry **S**ystem and the name essentially describes it. It’s a line notation for encoding molecular structures where atoms are represented by their standard abbreviation as a chemical element (i.e. C for carbon, N for nitrogen etc.). Multiple symbols are then used to define elements with charges, bonds, rings, aromaticity, stereochemistry, and much more. For more detail, read [here](https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system#Terminology).\n", - " - As an example, CCO, OCC and C(O)C all refer to ethanol. Having a number of equally valid SMILES strings for a given molecule can be an issue, therefore, canonicalization algorithms can be used to generate canonical SMILES to produce unique and consistent SMILES strings.\n", - " - Although SMILES are commonly used, they are not perfect. In a generative model using SMILES as inputs and outputs, there are often invalid SMILES strings that are produced (i.e. the SMILE string corresponds to an invalid molecule that violates basic chemical rules).\n", - "- **SELFIES** - stands for **SELF**-referenc**I**ng **E**mbedded **S**trings, it is another string-based representation for molecules that is generally more suitable for ML models and exhibits more robustness (i.e. more SELFIE strings corresponds to a valid molecule). Read more [here](https://aspuru.substack.com/p/molecular-graph-representations-and).\n", - "- **InChi** - another string-based method of representing chemical structures developed by IUPAC. Read more [here](https://iupac.org/100/stories/what-on-earth-is-inchi/). On the other hand, [InChi key](https://dev.drugbank.com/guides/terms/inchi-key) is a newer version of InChi that is only useful to identify molecules, however, it is impossible to reconstruct a molecule from an InChi key.\n", - "\n", - "See below for a graphic that summarizes some of the methods discussed in the section above: \n", - "\n", - "![image.png](attachment:8e26de78-ec19-4302-8b3e-1cb0e36c99b5.png)\n", - "\n", - "***[Source](https://www.researchgate.net/publication/344906202_Chemoinformatics-based_enumeration_of_chemical_libraries_a_tutorial)***\n", - "\n", - "**Note:** it’s important to understand that all forms of molecular representation have their pro’s and con’s. It’s less of a “one-size-fits-all” and more about picking and choosing specific methods to represent a molecule given your specific use case. \n", - "\n", - "## Sanitize and Standardize\n", - "\n", - "***Molecular sanitization*** is the process of ensuring that the molecules in your dataset ***are realistic***. You can read more about the sanitization procedure as applied in the RDKit [here](https://www.rdkit.org/docs/RDKit_Book.html#molecular-sanitization). In Datamol, there are a few extra steps as well, sanitization is done under the following procedure: \n", - "\n", - "1. Adjusting for nitrogen aromaticity since faulty valence for nitrogen in aromatic rings is [currently](https://github.com/rdkit/rdkit/issues/2011) an issue in RDKit through the Sanifix algorithm. \n", - "2. An extra conversion is done from mol → smiles → mol to ensure that the molecules are valid SMILES.\n", - "3. Charge neutralization - this is NOT charge removal, it attempts to correct valence issues arising from incorrect charges being placed on atoms.\n", - "\n", - "Users can control the application of the sanifix algorithim or charge neutralization, users can toggle the respective parameters ***sanifix*** and ***charge_neutral*** to be TRUE/FALSE. \n", - "\n", - "The process of **standardization** is used to generate ***canonical SMILES.*** It is currently done using the following procedure which can be controlled by the user through the described parameters below:\n", - "\n", - "- ***disconnect_metals -*** metal disconnection\n", - " - Depending on the source of the database, some compounds may be reported in salt form or associated to metallic ions (e.g. the sodium salt of a carboxylic compound). In most cases, these counter-ions are not relevant so the use of this function is required before further utilization of the dataset.)\n", - " - More details [here](https://molvs.readthedocs.io/en/latest/guide/standardize.html#disconnect-metals)\n", - "- ***normalize -*** ion (charge) and functional groups normalization\n", - " - It corrects drawing errors and standardizes functional groups in the molecule as well as ensuring the overall proper charge of the compound\n", - " - More details [here](https://molvs.readthedocs.io/en/latest/guide/standardize.html#apply-normalization-rules)\n", - "- ***reionize -*** reionization of the molecule (protonation following the acidity order)\n", - " - If one or more acidic functionalities are present in the molecule, this option ensures the correct neutral/ionized state for such functional groups. Molecules are uncharged by adding and/or removing hydrogens. For zwitterions, hydrogens are moved to eliminate charges where possible. However, in cases where there is a positive charge that is not neutralizable, an attempt is made to also preserve the corresponding negative charge\n", - " - Read more [here](https://molvs.readthedocs.io/en/latest/guide/standardize.html#reionize-acids)\n", - "- ***uncharge* -** charge removal\n", - " - This option neutralize the molecule by reversing the protonation state of protonated and deprotonated groups, if present (e.g. a carboxylate is re-protonated to the corresponding carboxylic acid).\n", - " - In cases where there is a positive charge that is not neutralizable, an attempt is made to also preserve the corresponding negative charge to ensure a net zero charge.\n", - "- ***stereo -*** stereochemistry proper reassignment if missing.\n", - " - Stereochemical information is corrected and/or added if missing using built-in RDKit functionality to force a clean recalculation of stereochemistry\n", - "\n", - "The actual processes for sanitization and standardization described can get a bit too detailed, with lots of chemistry terminology. We recommend just sticking with the defaults already set in Datamol. It’s enough just to understand the importance of why we sanitize and standardize our datasets as a key step in the pre-processing, as you continue spending time in the AI/ML for drug discovery field, you will get more familiar with the details. \n", - "\n", - "## Tutorial\n", - "\n", - "In this tutorial, let’s walk through how to load a dataset and then apply the described pre-processing pipeline which will take a list of molecules and: \n", - "\n", - "- Convert to a mol.\n", - "- Fix common errors in the mol.\n", - "- Sanitize the mol.\n", - "- Standardize the mol.\n", - "- Generate a standardized SMILES.\n", - "- Generate SELFIES.\n", - "- Generate InChi and InChi key.\n", - "- Save the results as CSV or SDF file formats.\n", - "\n", - "From here, we will generate a table where it can more easily visualized. The option of parallelization will also be shown: \n", - "\n", - "**Note:** parallelizing the preprocessing will only be faster if your dataset is very large. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "2a3c8bf5", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import datamol as dm\n", - "\n", - "dm.disable_rdkit_log()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "fc621492", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(642, 4)\n" - ] - }, - { - "data": { - "text/html": [ - "

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Huge thanks to @Valence-jonnyhsu for leading the refactoring of the datamol tutorials. * Improve documentation for `dm.standardize_mol()` * Multiple various docstring improvments. diff --git a/setup.py b/setup.py index 4aee8199..b730e802 100644 --- a/setup.py +++ b/setup.py @@ -16,6 +16,7 @@ "appdirs", "scikit-learn", "packaging", + "rdkit", ] setup( From 63647e2c095088940584857af7e0ee9c193ba08f Mon Sep 17 00:00:00 2001 From: Hadrien Mary Date: Sun, 4 Sep 2022 14:17:19 -0400 Subject: [PATCH 04/15] typing and docstring fixes --- datamol/align.py | 5 +- datamol/cluster.py | 7 +- datamol/conformers/_conformers.py | 10 +- datamol/descriptors/descriptors.py | 10 +- datamol/fp.py | 7 +- datamol/fragment/_assemble.py | 5 +- datamol/fragment/_fragment.py | 14 +- datamol/graph.py | 5 +- datamol/io.py | 11 +- datamol/mcs.py | 5 +- datamol/mol.py | 12 +- datamol/scaffold/_fuzzy.py | 43 +- datamol/similarity.py | 7 +- datamol/utils/fs.py | 2 +- datamol/utils/jobs.py | 7 +- datamol/viz/_circle_grid.py | 14 +- datamol/viz/_substructure.py | 5 +- datamol/viz/_viz.py | 3 +- docs/index.md | 8 +- docs/tutorials/Fuzzy_Scaffolds.ipynb | 1535 +++++++++++++------------- mkdocs.yml | 6 +- news/tutos.rst | 2 +- 22 files changed, 843 insertions(+), 880 deletions(-) diff --git a/datamol/align.py b/datamol/align.py index 2e8199f3..c810da4f 100644 --- a/datamol/align.py +++ b/datamol/align.py @@ -1,6 +1,7 @@ from typing import Optional from typing import Union from typing import Sequence +from typing import Any from packaging import version from collections import defaultdict as ddict @@ -132,7 +133,7 @@ def auto_align_many( copy: bool = True, cluster_cutoff: float = 0.7, allow_r_groups: bool = True, - **kwargs, + **kwargs: Any, ): """Partition a list of molecules into clusters sharing common scaffold of common core, then align the molecules to that common core. This function will compute the list of @@ -162,7 +163,7 @@ def auto_align_many( allow_r_groups: Optional, if True, terminal dummy atoms in the reference are ignored if they match an implicit hydrogen in the molecule, and a constrained depiction is still attempted - kwargs: Additional arguments to pass to clustering method + **kwargs: Additional arguments to pass to clustering method """ if copy: diff --git a/datamol/cluster.py b/datamol/cluster.py index c2b7349a..182c2e22 100644 --- a/datamol/cluster.py +++ b/datamol/cluster.py @@ -3,6 +3,7 @@ from typing import Optional from typing import Sequence from typing import Union +from typing import Tuple import operator import functools @@ -77,7 +78,7 @@ def pick_diverse( dist_fn: Optional[Callable] = None, seed: int = 42, n_jobs: Optional[int] = 1, -): +) -> Tuple[int, list]: r"""Pick a set of diverse molecules based on they fingerprint. Args: @@ -130,7 +131,7 @@ def pick_centroids( seed: int = 42, method: str = "sphere", n_jobs: Optional[int] = 1, -): +) -> Tuple[int, list]: r"""Pick a set of `npick` centroids from a list of molecules. Args: @@ -213,7 +214,7 @@ def assign_to_centroids( feature_fn: Optional[Callable] = None, dist_fn: Optional[Callable] = None, n_jobs: Optional[int] = 1, -): +) -> Tuple[dict, list]: r"""Assign molecules to centroids. Each molecule will be assigned to the closest centroid. Args: diff --git a/datamol/conformers/_conformers.py b/datamol/conformers/_conformers.py index ac557dd5..e4f3f059 100644 --- a/datamol/conformers/_conformers.py +++ b/datamol/conformers/_conformers.py @@ -2,6 +2,7 @@ from typing import List from typing import Sequence from typing import Optional +from typing import Tuple import copy @@ -9,7 +10,6 @@ import numpy as np -from rdkit import Chem from rdkit.Chem import AllChem from rdkit.Chem import rdDistGeom from rdkit.Chem import rdMolAlign @@ -86,10 +86,10 @@ def generate( align_conformers: Whether to align the conformers. minimize_energy: Whether to minimize conformer's energies using MMFF94s. Disable to generate conformers much faster. - sort_by_energies: Sort conformers by energy when minimizing is turned to False. + sort_by_energy: Sort conformers by energy when minimizing is turned to False. method: RDKit method to use for embedding. Choose among ["ETDG", "ETKDG", "ETKDGv2", "ETKDGv3"]. If None, "ETKDGv3" is used. - forcfield: molecular forcefield to use, one of ['UFF','MMFF94S','MMFF94s_noEstat'] + forcefield: molecular forcefield to use, one of ['UFF','MMFF94S','MMFF94s_noEstat'] ewindow: maximum energy above minimum energy conformer to output eratio: max delta-energy divided by rotatable bonds for conformers energy_iterations: Maximum number of iterations during the energy minimization procedure. @@ -102,8 +102,6 @@ def generate( are removed in the returned molecule. Warning: explicit hydrogens won't be conserved. It is strongly recommended to let the default value to True. The RDKit documentation says: "To get good 3D conformations, it's almost always a good idea to add hydrogens to the molecule first." - fallback_to_random_coords: Whether to use random coordinate initializations as a fallback if the initial - embedding fails. ignore_failure: It set to True, this will avoid raising an error when the embedding fails and return None instead. embed_params: Allows the user to specify arbitrary embedding parameters for the conformers. This will override any other default settings. See https://www.rdkit.org/docs/source/rdkit.Chem.rdDistGeom.html#rdkit.Chem.rdDistGeom.EmbedParameters @@ -384,7 +382,7 @@ def align_conformers( copy: bool = True, conformer_id: int = -1, backend: str = "crippenO3A", -): +) -> Tuple[list, list]: """Align a list of molecules to a reference molecule. Note that using the `O3A` backend, hydrogens will be added at the beginning of the procedure diff --git a/datamol/descriptors/descriptors.py b/datamol/descriptors/descriptors.py index b6d7ffbc..004cb377 100644 --- a/datamol/descriptors/descriptors.py +++ b/datamol/descriptors/descriptors.py @@ -62,7 +62,7 @@ def _sasscorer(mol: Mol): n_saturated_rings = Lipinski.NumSaturatedRings # type: ignore -def n_rigid_bonds(mol: Mol): +def n_rigid_bonds(mol: Mol) -> int: """Compute the number of rigid bonds in a molecule. Rigid bonds are bonds that are not single and not in rings. @@ -78,13 +78,13 @@ def n_rigid_bonds(mol: Mol): return n_rigid_bonds -def n_aromatic_atoms(mol: Mol): +def n_aromatic_atoms(mol: Mol) -> int: """Calculate the number of aromatic atoms.""" matches = mol.GetSubstructMatches(_AROMATIC_QUERY) return len(matches) -def n_aromatic_atoms_proportion(mol: Mol): +def n_aromatic_atoms_proportion(mol: Mol) -> int: """Calculate the aromatic proportion: # aromatic atoms/#atoms total. Args: @@ -95,7 +95,7 @@ def n_aromatic_atoms_proportion(mol: Mol): return n_aromatic_atoms(mol) / mol.GetNumHeavyAtoms() -def n_stereo_centers(mol: Mol): +def n_stereo_centers(mol: Mol) -> int: """Compute the number of stereocenters in a molecule. Args: @@ -113,7 +113,7 @@ def n_stereo_centers(mol: Mol): return n -def n_charged_atoms(mol: Mol): +def n_charged_atoms(mol: Mol) -> int: """Compute the number of charged atoms in a molecule. Args: diff --git a/datamol/fp.py b/datamol/fp.py index 44759938..7e6435af 100644 --- a/datamol/fp.py +++ b/datamol/fp.py @@ -1,7 +1,6 @@ from typing import Union from typing import Optional - -import warnings +from typing import Any import numpy as np @@ -237,7 +236,7 @@ def to_fp( as_array: bool = True, fp_type: str = "ecfp", fold_size: Optional[int] = None, - **fp_args, + **fp_args: Any, ) -> Optional[Union[np.ndarray, SparseBitVect, ExplicitBitVect]]: """Compute the molecular fingerprint given a molecule or a SMILES. @@ -302,7 +301,7 @@ def fold_count_fp( fp: Union[np.ndarray, SparseBitVect, ExplicitBitVect], dim: int = 1024, binary: bool = False, -): +) -> np.ndarray: """Fast folding of a count fingerprint to the specified dimension. Args: diff --git a/datamol/fragment/_assemble.py b/datamol/fragment/_assemble.py index f479967e..47d4aebe 100644 --- a/datamol/fragment/_assemble.py +++ b/datamol/fragment/_assemble.py @@ -434,8 +434,8 @@ def assemble_fragment_order( ): """Assemble a list of fragment into a set of possible molecules under rules defined by the brics algorithm - ..note :: - We are of course assuming: + We are of course assuming: + 1. that the order in the fragmentlist matter :D ! 2. that none of the fragment has explicitly defined hydrogen atoms. 3. only a list of unique molecule is internally maintained @@ -444,7 +444,6 @@ def assemble_fragment_order( fragmentlist: list of original fragments to grow seen: original molecules used as base. If none, the first element of fragment list will be poped out allow_incomplete: Whether to accept assembled molecules with missing fragment - """ if RXNS is None: diff --git a/datamol/fragment/_fragment.py b/datamol/fragment/_fragment.py index 680ceee6..5a77be8a 100644 --- a/datamol/fragment/_fragment.py +++ b/datamol/fragment/_fragment.py @@ -134,12 +134,12 @@ def anybreak( def mmpa_frag( - mol, - pattern: str = None, + mol: dm.Mol, + pattern: Optional[str] = None, max_cut: int = 1, max_bond_cut: int = 20, h_split: bool = False, -) -> Optional[Set[Chem.Mol]]: +) -> Optional[Set[dm.Mol]]: """Fragment molecule on specific bonds suitable for a MMPA analysis. Args: @@ -152,7 +152,7 @@ def mmpa_frag( This is equivalent to enabling the addition of new fragments. Returns: - List of fragments + List of fragments. """ frags = [] @@ -173,7 +173,7 @@ def mmpa_frag( ) if h_split: - mol = Chem.AddHs(mol) + mol = dm.add_hs(mol) frags += rdMMPA.FragmentMol( mol, pattern="[#1]!@!=!#[!#1]", @@ -184,7 +184,7 @@ def mmpa_frag( return set(frags) -def mmpa_cut(mol: Chem.rdchem.Mol, rdkit_pattern: bool = False) -> Optional[Set[Any]]: +def mmpa_cut(mol: dm.Mol, rdkit_pattern: bool = False) -> Optional[Set[Any]]: """Cut molecules to perform mmpa analysis later Args: @@ -216,7 +216,7 @@ def mmpa_cut(mol: Chem.rdchem.Mol, rdkit_pattern: bool = False) -> Optional[Set[ outlines.add(output) # hydrogen splitting - mol = Chem.AddHs(mol) + mol = dm.add_hs(mol) smiles = dm.to_smiles(mol) n = mol.GetNumHeavyAtoms() diff --git a/datamol/graph.py b/datamol/graph.py index 401aec6f..c0416ab1 100644 --- a/datamol/graph.py +++ b/datamol/graph.py @@ -61,7 +61,7 @@ def get_all_path_between( atom_idx_1: int, atom_idx_2: int, ignore_cycle_basis: bool = False, -): +) -> list: """Get all simple path between two atoms of a molecule Args: @@ -116,7 +116,8 @@ def match_molecular_graphs( hydrogens, they can be re-ordered in any way. Args: - mol1, mol2: The molecules to match their indices. + mol1: A molecule. + mol2: A molecule. match_atoms_on: Properties on which to match the atom types. By default, it matches on the `'atomic_num'` property. Empty list means that it does not consider atom features during matching. diff --git a/datamol/io.py b/datamol/io.py index 2f04dcf7..da048597 100644 --- a/datamol/io.py +++ b/datamol/io.py @@ -3,6 +3,7 @@ from typing import List from typing import Sequence from typing import TextIO +from typing import Any import os import io @@ -26,7 +27,7 @@ def read_csv( urlpath: Union[str, os.PathLike, TextIO], smiles_column: Optional[str] = None, mol_column: str = "mol", - **kwargs, + **kwargs: Any, ) -> pd.DataFrame: """Read a CSV file. @@ -35,7 +36,7 @@ def read_csv( smiles_column: Use this column to build a mol column. mol_column: Name to give to the mol column. If not None a mol column will be build. Avoid when loading a very large file. - kwargs: Arguments to pass to `pd.read_csv()`. + **kwargs: Arguments to pass to `pd.read_csv()`. Returns: df: a `pandas.DataFrame` @@ -54,7 +55,7 @@ def read_excel( sheet_name: Optional[Union[str, int, list]] = 0, smiles_column: Optional[str] = None, mol_column: str = "mol", - **kwargs, + **kwargs: Any, ) -> pd.DataFrame: """Read an excel file. @@ -64,7 +65,7 @@ def read_excel( mol_column: Name to give to the mol column. If not None a mol column will be build. Avoid when loading a very large file. mol_column: name to give to the mol column. - kwargs: Arguments to pass to `pd.read_excel()`. + **kwargs: Arguments to pass to `pd.read_excel()`. Returns: df: a `pandas.DataFrame` @@ -203,7 +204,7 @@ def read_molblock( Note that potential molecule properties are **not** read. Args: - mol_block: String containing the Mol block. + molblock: String containing the Mol block. sanitize: Whether to sanitize the molecules. strict_parsing: If set to false, the parser is more lax about correctness of the contents. remove_hs: Whether to remove the existing hydrogens in the SDF files. diff --git a/datamol/mcs.py b/datamol/mcs.py index 35a32232..28849b7b 100644 --- a/datamol/mcs.py +++ b/datamol/mcs.py @@ -1,4 +1,5 @@ from typing import List +from typing import Any from rdkit.Chem import rdFMCS @@ -24,7 +25,7 @@ def find_mcs( bond_compare: str = "CompareOrder", ring_compare: str = "IgnoreRingFusion", with_details: bool = False, - **kwargs, + **kwargs: Any, ): """Find the maximum common substructure from a list of molecules. @@ -44,7 +45,7 @@ def find_mcs( bond_compare: One of "CompareAny", "CompareOrder", "CompareOrderExact". ring_compare: One of "IgnoreRingFusion", "PermissiveRingFusion", "StrictRingFusion". with_details: Whether to return the RDKit MCS object or just the SMARTS string. - kwargs: Additional arguments for the MCS. + **kwargs: Additional arguments for the MCS. """ if atom_compare not in ALLOWED_ATOM_COMPARE: diff --git a/datamol/mol.py b/datamol/mol.py index 57e06352..99ba5821 100644 --- a/datamol/mol.py +++ b/datamol/mol.py @@ -320,7 +320,7 @@ def sanitize_smiles(smiles: Optional[str], isomeric: bool = True) -> Optional[st return smiles -def sanitize_first(mols: List[Mol], charge_neutral: bool = False, sanifix: bool = True): +def sanitize_first(mols: List[Mol], charge_neutral: bool = False, sanifix: bool = True) -> Mol: """Sanitize a list of molecules and return the first valid molecule seen in the list. Args: @@ -339,7 +339,7 @@ def sanitize_first(mols: List[Mol], charge_neutral: bool = False, sanifix: bool return None -def standardize_smiles(smiles: str, tautomer: bool = False): +def standardize_smiles(smiles: str, tautomer: bool = False) -> str: r""" Apply smile standardization procedure. This is a convenient function wrapped arrounf RDKit smiles standardizer and tautomeric canonicalization. @@ -365,7 +365,7 @@ def standardize_mol( reionize: bool = True, uncharge: bool = False, stereo: bool = True, -): +) -> Mol: r""" This function returns a standardized version the given molecule. It relies on the RDKit [`rdMolStandardize` module](https://www.rdkit.org/docs/source/rdkit.Chem.MolStandardize.rdMolStandardize.html) @@ -508,7 +508,7 @@ def decrease_bond(bond: Chem.rdchem.Bond) -> Optional[Union[list, Chem.rdchem.Bo return bond -def fix_valence(mol, inplace: bool = False, allow_ring_break: bool = False) -> Optional[Mol]: +def fix_valence(mol: Mol, inplace: bool = False, allow_ring_break: bool = False) -> Optional[Mol]: """Identify and try to fix valence issues by removing any supplemental bond that should not be in the graph. @@ -851,14 +851,14 @@ def atom_list_to_bond( return bonds -def substructure_matching_bonds(mol: Mol, query: Mol, **kwargs): +def substructure_matching_bonds(mol: Mol, query: Mol, **kwargs: Any) -> Tuple[list, list]: """Perform a substructure match using `GetSubstructMatches` but instead of returning only the atom indices also return the bond indices. Args: mol: A molecule. query: A molecule used as a query to match against. - kwargs: Any other arguments to pass to `mol.GetSubstructMatches()`. + **kwargs: Any other arguments to pass to `mol.GetSubstructMatches()`. Returns: atom_matches: A list of lists of atom indices. diff --git a/datamol/scaffold/_fuzzy.py b/datamol/scaffold/_fuzzy.py index d52ab1c2..a3c9ba4e 100644 --- a/datamol/scaffold/_fuzzy.py +++ b/datamol/scaffold/_fuzzy.py @@ -1,6 +1,8 @@ from typing import Dict from typing import List from typing import Any +from typing import Tuple +from typing import Optional import collections import itertools @@ -13,16 +15,23 @@ from rdkit.Chem.rdmolops import AdjustQueryParameters from rdkit.Chem.rdmolops import AdjustQueryProperties -from rdkit.Chem.Fraggle import FraggleSim from rdkit.Chem.Scaffolds import MurckoScaffold -import datamol as dm +from ..types import Mol +from ..mol import keep_largest_fragment +from ..mol import fix_mol +from ..mol import to_mol +from ..mol import add_hs +from ..mol import remove_hs +from ..convert import to_smiles +from ..convert import to_smarts +from ..convert import from_smarts def trim_side_chain(mol: Chem.rdchem.Mol, core, unwanted_side_chains): """Trim list of side chain from a molecule.""" - mol = Chem.AddHs(mol) + mol = add_hs(mol) match = mol.GetSubstructMatch(core) map2idx = {} @@ -52,26 +61,26 @@ def trim_side_chain(mol: Chem.rdchem.Mol, core, unwanted_side_chains): emol.AddBond(nei_idx, new_ind, bond.GetBondType()) mol = emol.GetMol() - mol = Chem.RemoveHs(mol) + mol = remove_hs(mol) query_param = AdjustQueryParameters() query_param.makeDummiesQueries = False query_param.adjustDegree = False query_param.aromatizeIfPossible = True for patt, _ in unwanted2map.items(): - cur_frag = dm.fix_mol(patt) + cur_frag = fix_mol(patt) mol = Chem.DeleteSubstructs(mol, cur_frag, onlyFrags=True) - return dm.keep_largest_fragment(mol) + return keep_largest_fragment(mol) def fuzzy_scaffolding( mols: List[Chem.rdchem.Mol], - enforce_subs: List[str] = None, + enforce_subs: Optional[List[str]] = None, n_atom_cuttoff: int = 8, - additional_templates: List[Chem.rdchem.Mol] = None, + additional_templates: Optional[List[Mol]] = None, ignore_non_ring: bool = False, - mcs_params: Dict[Any, Any] = None, -): + mcs_params: Optional[Dict[Any, Any]] = None, +) -> Tuple[set, Dict[str, dict], Dict[str, list]]: """Generate fuzzy scaffold with enforceable group that needs to appear in the core, forcing to keep the full side chain if required @@ -139,14 +148,14 @@ def fuzzy_scaffolding( rw_scf.RemoveAtom(a) scfs = list(rdmolops.GetMolFrags(rw_scf, asMols=False)) else: - scfs = [dm.to_smiles(scf)] + scfs = [to_smiles(scf)] # add templates mols if exists: for tmp in additional_templates: - tmp = dm.to_mol(tmp) + tmp = to_mol(tmp) tmp_scf = MurckoScaffold.MakeScaffoldGeneric(tmp) if generic_m.HasSubstructMatch(tmp_scf): - scfs.append(dm.to_smiles(tmp_scf)) + scfs.append(to_smiles(tmp_scf)) for scf in scfs: if scf2infos[scf].get("mols"): @@ -171,14 +180,14 @@ def fuzzy_scaffolding( **mcs_params, ) - mcsM = Chem.MolFromSmarts(mcs.smartsString) + mcsM = from_smarts(mcs.smartsString) mcsM.UpdatePropertyCache(False) Chem.SetHybridization(mcsM) if mcsM.GetNumAtoms() < n_atom_cuttoff: continue - scf2infos[scf]["smarts"] = dm.to_smarts(mcsM) + scf2infos[scf]["smarts"] = to_smarts(mcsM) if popout: mols = mols[:-1] @@ -208,7 +217,7 @@ def fuzzy_scaffolding( ] rgroups = [gp[f"R{k}"] for k in acceptable_groups if f"R{k}" in gp.keys()] - if enforce_subs: + if enforce_subs is not None: rgroups = [ rgp for rgp in rgroups @@ -218,6 +227,6 @@ def fuzzy_scaffolding( scaff = trim_side_chain(mol, AdjustQueryProperties(core, core_query_param), rgroups) except: continue - all_scaffolds.add(dm.to_smiles(scaff)) + all_scaffolds.add(to_smiles(scaff)) return all_scaffolds, scf2infos, scf2groups diff --git a/datamol/similarity.py b/datamol/similarity.py index 19891641..9f9c853e 100644 --- a/datamol/similarity.py +++ b/datamol/similarity.py @@ -1,11 +1,10 @@ from typing import List from typing import Optional from typing import Union +from typing import Any import functools -from rdkit import Chem - import numpy as np from sklearn.metrics import pairwise_distances_chunked from scipy.spatial import distance @@ -17,7 +16,7 @@ def pdist( mols: List[Union[str, dm.Mol]], n_jobs: Optional[int] = 1, squareform: bool = True, - **fp_args, + **fp_args: Any, ) -> np.ndarray: """Compute the pairwise tanimoto distance between the fingerprints of all the molecules in the input set. @@ -57,7 +56,7 @@ def cdist( distances_chunk: bool = False, distances_chunk_memory: int = 1024, distances_n_jobs: int = -1, - **fp_args, + **fp_args: Any, ) -> np.ndarray: """Compute the tanimoto distance between the fingerprints of each pair of molecules of the two collections of inputs. diff --git a/datamol/utils/fs.py b/datamol/utils/fs.py index 3bbab081..e77c1ee1 100644 --- a/datamol/utils/fs.py +++ b/datamol/utils/fs.py @@ -173,7 +173,7 @@ def join(*paths): filesystem to use (and so the separator. Args: - paths: a list of paths supported by `fsspec` such as local, s3, gcs, etc. + *paths: a list of paths supported by `fsspec` such as local, s3, gcs, etc. """ paths = [str(path).rstrip("/") for path in paths] source_path = paths[0] diff --git a/datamol/utils/jobs.py b/datamol/utils/jobs.py index ceb0193b..a10e1bcb 100644 --- a/datamol/utils/jobs.py +++ b/datamol/utils/jobs.py @@ -24,7 +24,7 @@ def __init__( progress: bool = False, total: Optional[int] = None, tqdm_kwargs: Optional[dict] = None, - **job_kwargs, + **job_kwargs: Any, ): """ JobRunner with sequential/parallel regimes. The multiprocessing backend use joblib which @@ -216,7 +216,7 @@ def parallelized( arg_type: str = "arg", total: Optional[int] = None, tqdm_kwargs: Optional[dict] = None, - **job_kwargs, + **job_kwargs: Any, ) -> Sequence[Optional[Any]]: """Run a function in parallel. @@ -268,7 +268,7 @@ def parallelized_with_batches( tqdm_kwargs: Optional[dict] = None, flatten_results: bool = True, joblib_batch_size: Union[int, str] = "auto", - **job_kwargs, + **job_kwargs: Any, ) -> Sequence[Optional[Any]]: """Run a function in parallel using batches. @@ -288,7 +288,6 @@ def parallelized_with_batches( - "kwargs": the input is passed as a map: `fn(**kwargs)`. total: The number of elements in the iterator. Only used when `progress` is True. tqdm_kwargs: Any additional arguments supported by the `tqdm` progress bar. - job_kwargs: Any additional arguments supported by `joblib.Parallel`. flatten_results: Whether to flatten the results. joblib_batch_size: It corresponds to the `batch_size` argument of `dm.parallelized` that is forwarded to `joblib.Parallel` under the hood. diff --git a/datamol/viz/_circle_grid.py b/datamol/viz/_circle_grid.py index a7195c43..99745eee 100644 --- a/datamol/viz/_circle_grid.py +++ b/datamol/viz/_circle_grid.py @@ -1,5 +1,7 @@ from typing import List from typing import Tuple +from typing import Any +from typing import Optional import io import math @@ -15,10 +17,10 @@ def circle_grid( center_mol: Chem.rdchem.Mol, circle_mols: List[List[Chem.rdchem.Mol]], - legend: str = None, + legend: Optional[str] = None, mol_size: Tuple[int, int] = (200, 200), circle_margin: int = 50, - act_mapper: dict = None, + act_mapper: Optional[dict] = None, ): """Show molecules in concentric rings, with one molecule at the center @@ -37,10 +39,10 @@ def __init__( self, center_mol: Chem.rdchem.Mol, circle_mols: List[List[Chem.rdchem.Mol]], - legend: str = None, + legend: Optional[str] = None, mol_size: Tuple[int, int] = (200, 200), circle_margin: int = 50, - act_mapper: dict = None, + act_mapper: Optional[dict] = None, ): """Show molecules in concentric rings, with one molecule at the center @@ -132,7 +134,7 @@ def _draw_mol_at( mol_size=None, act_dict={}, center=False, - **kwargs, + **kwargs: Any, ): img = mol if mol_size is None: @@ -158,7 +160,7 @@ def _draw_mol_at( font = ImageFont.truetype(fontpath, 18 + center * 8) w, h = self.draw.multiline_textsize(txt, font=font) except: - passcircle_mols + pass def _draw_center_mol(self): self._draw_mol_at( diff --git a/datamol/viz/_substructure.py b/datamol/viz/_substructure.py index a11f869c..035ca689 100644 --- a/datamol/viz/_substructure.py +++ b/datamol/viz/_substructure.py @@ -1,5 +1,4 @@ -import itertools - +from typing import Any from typing import Union from typing import List @@ -13,7 +12,7 @@ def match_substructure( queries: Union[List[dm.Mol], dm.Mol], highlight_bonds: bool = True, copy: bool = True, - **kwargs, + **kwargs: Any, ): """Generate an image of molecule(s) with substructure matches for a given pattern or substructure. diff --git a/datamol/viz/_viz.py b/datamol/viz/_viz.py index 87fa2492..68c2655a 100644 --- a/datamol/viz/_viz.py +++ b/datamol/viz/_viz.py @@ -2,6 +2,7 @@ from typing import List from typing import Tuple from typing import Optional +from typing import Any import fsspec @@ -33,7 +34,7 @@ def to_image( legend_fontsize: int = 16, kekulize: bool = True, align: Union[dm.Mol, str, bool] = False, - **kwargs, + **kwargs: Any, ): """Generate an image out of a molecule or a list of molecules. diff --git a/docs/index.md b/docs/index.md index 8ae16a26..9a3b24fa 100644 --- a/docs/index.md +++ b/docs/index.md @@ -18,13 +18,9 @@ Use conda: mamba install -c conda-forge datamol ``` -!!! tips +_**Tips:** You can replace `mamba` by `conda`._ - You can replace `mamba` by `conda`. - -!!! note - - We highly recommend using a [Conda Python distribution](https://github.com/conda-forge/miniforge) to install Datamol. The package is also pip installable if you need it: `pip install datamol`. +_**Note:** We highly recommend using a [Conda Python distribution](https://github.com/conda-forge/miniforge) to install Datamol. The package is also pip installable if you need it: `pip install datamol`._ ## Quick API Tour diff --git a/docs/tutorials/Fuzzy_Scaffolds.ipynb b/docs/tutorials/Fuzzy_Scaffolds.ipynb index 4d9db7fc..cf62856e 100644 --- a/docs/tutorials/Fuzzy_Scaffolds.ipynb +++ b/docs/tutorials/Fuzzy_Scaffolds.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "id": "da29c9a0-c136-48f8-9be8-63c50eca0652", "metadata": {}, "outputs": [ @@ -698,7 +698,7 @@ "" ] }, - "execution_count": 3, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -716,7 +716,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "id": "54bc03cc-95d9-49b8-a071-b38837a57bd6", "metadata": {}, "outputs": [ @@ -726,798 +726,747 @@ "\n", "\n", " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - 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"execution_count": 8, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -1529,6 +1478,14 @@ "sfs = [dm.to_mol(s) for s in list(scaffolds)]\n", "dm.to_image(sfs, mol_size=(200, 150), max_mols=12)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "afdafbf7-d873-402f-af3c-d02194e98e7d", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/mkdocs.yml b/mkdocs.yml index 749374e6..27632413 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -88,9 +88,9 @@ plugins: heading_level: 3 show_root_full_path: false - - mkdocs-jupyter: - execute: false - # kernel_name: python3 + # - mkdocs-jupyter: + # execute: false + # # kernel_name: python3 - mike: version_selector: true diff --git a/news/tutos.rst b/news/tutos.rst index 06fca86c..a6a3bbc8 100644 --- a/news/tutos.rst +++ b/news/tutos.rst @@ -6,7 +6,7 @@ * Revamped all the datamol tutorials and add new tutorials. Huge thanks to @Valence-jonnyhsu for leading the refactoring of the datamol tutorials. * Improve documentation for `dm.standardize_mol()` -* Multiple various docstring improvments. +* Multiple various docstring and typing improvments. **Deprecated:** From cc3159e629d7afded400f64234f27d50f0b4c355 Mon Sep 17 00:00:00 2001 From: Hadrien Mary Date: Sun, 4 Sep 2022 14:38:37 -0400 Subject: [PATCH 05/15] relocate rdkit data files into datamol --- .github/workflows/doc.yml | 3 +- .github/workflows/test.yml | 5 +- README.md | 1 + datamol/data.py | 37 +- datamol/data/cdk2.sdf | 5373 +++ datamol/data/solubility.test.sdf | 12195 +++++++ datamol/data/solubility.train.sdf | 48585 ++++++++++++++++++++++++++++ datamol/io.py | 16 +- docs/index.md | 1 + news/tutos.rst | 1 + 10 files changed, 66182 insertions(+), 35 deletions(-) create mode 100644 datamol/data/cdk2.sdf create mode 100644 datamol/data/solubility.test.sdf create mode 100644 datamol/data/solubility.train.sdf diff --git a/.github/workflows/doc.yml b/.github/workflows/doc.yml index 6974efe5..5e590eb0 100644 --- a/.github/workflows/doc.yml +++ b/.github/workflows/doc.yml @@ -17,8 +17,7 @@ jobs: uses: actions/checkout@v2 - name: Setup conda - # see https://github.com/mamba-org/provision-with-micromamba/issues/54 - uses: mamba-org/provision-with-micromamba@755a9542150cc9dedd1c1dd95b963460ec320939 + uses: mamba-org/provision-with-micromamba with: environment-file: false cache-downloads: true diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index cc043fdd..dddae587 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -14,7 +14,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: ["3.8", "3.9"] + python-version: ["3.9", "3.10"] os: ["ubuntu-latest", "macos-latest", "windows-latest"] rdkit-version: ["2021.09", "2022.03"] @@ -30,8 +30,7 @@ jobs: uses: actions/checkout@v2 - name: Setup conda - # see https://github.com/mamba-org/provision-with-micromamba/issues/54 - uses: mamba-org/provision-with-micromamba@755a9542150cc9dedd1c1dd95b963460ec320939 + uses: mamba-org/provision-with-micromamba with: environment-file: false cache-downloads: true diff --git a/README.md b/README.md index 186c75fa..001f6773 100644 --- a/README.md +++ b/README.md @@ -94,6 +94,7 @@ See below the associated versions of Python and RDKit, for which a minor version | `datamol` | `python` | `rdkit` | | --------- | ------------ | -------------------- | +| `0.8` | `[3.9, 3.10]` | `[2021.09, 2022.03]` | | `0.7` | `[3.8, 3.9]` | `[2021.09, 2022.03]` | | `0.6` | `[3.8, 3.9]` | `[2021.09]` | | `0.5` | `[3.8, 3.9]` | `[2021.03, 2021.09]` | diff --git a/datamol/data.py b/datamol/data.py index f84e79b9..66b23549 100644 --- a/datamol/data.py +++ b/datamol/data.py @@ -1,13 +1,10 @@ from typing import Optional - -import os +from typing import cast import pkg_resources import pandas as pd -from rdkit.Chem import RDConfig - from .io import read_sdf from .convert import from_df from .convert import render_mol_df @@ -26,7 +23,7 @@ def freesolv(): """ with pkg_resources.resource_stream("datamol", "data/freesolv.csv") as f: - data = pd.read_csv(f) # type: ignore + data = pd.read_csv(f) return data @@ -38,11 +35,9 @@ def cdk2(as_df: bool = True, mol_column: Optional[str] = "mol"): mol_column: Name of the mol column. Only relevant if `as_df` is True. """ - return read_sdf( - os.path.join(RDConfig.RDDocsDir, "Book/data/cdk2.sdf"), - as_df=as_df, - mol_column=mol_column, - ) + with pkg_resources.resource_stream("datamol", "data/cdk2.sdf") as f: + data = read_sdf(f, as_df=as_df, mol_column=mol_column) + return data def solubility(as_df: bool = True, mol_column: Optional[str] = "mol"): @@ -55,20 +50,16 @@ def solubility(as_df: bool = True, mol_column: Optional[str] = "mol"): mol_column: Name of the mol column. Only relevant if `as_df` is True. """ - train: pd.DataFrame = read_sdf( - os.path.join(RDConfig.RDDocsDir, "Book/data/solubility.train.sdf"), - as_df=True, - mol_column="mol", - smiles_column=None, - ) # type: ignore - train["split"] = "train" + with pkg_resources.resource_stream("datamol", "data/solubility.train.sdf") as f: + train = read_sdf(f, as_df=True, mol_column="mol", smiles_column=None) - test: pd.DataFrame = read_sdf( - os.path.join(RDConfig.RDDocsDir, "Book/data/solubility.test.sdf"), - as_df=True, - mol_column="mol", - smiles_column=None, - ) # type: ignore + with pkg_resources.resource_stream("datamol", "data/solubility.test.sdf") as f: + test = read_sdf(f, as_df=True, mol_column="mol", smiles_column=None) + + train = cast(pd.DataFrame, train) + test = cast(pd.DataFrame, test) + + train["split"] = "train" test["split"] = "test" # NOTE(hadim): LMAO RDkit consistency xD diff --git a/datamol/data/cdk2.sdf b/datamol/data/cdk2.sdf new file mode 100644 index 00000000..ecb58c32 --- /dev/null +++ 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0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0031 -3.0008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.0432 -3.5993 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 1 0 + 5 7 2 0 + 7 1 1 0 + 7 8 1 0 + 8 9 1 0 +M END +> (38) +47 + +> (38) +1-ethyl-2-methylbenzene + +> (38) +-3.21 + +> (38) +(A) low + +> (38) +c1cccc(C)c1CC + +$$$$ +1-ethyl-4-methylbenzene + RDKit 2D + + 9 9 0 0 0 0 0 0 0 0999 V2000 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 2.7000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0031 -3.0008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.0432 -3.5993 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 3 5 2 0 + 5 6 1 0 + 6 7 2 0 + 7 1 1 0 + 7 8 1 0 + 8 9 1 0 +M END 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0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0031 -3.0008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3039 -3.7494 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3070 -5.2502 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.3471 -5.8487 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 1 1 0 + 6 7 1 0 + 7 8 1 0 + 8 9 1 0 + 9 10 1 0 +M END +> (41) +51 + +> (41) +n-butylbenzene + +> (41) +-4.06 + +> (41) +(A) low + +> (41) +c1ccccc1CCCC + +$$$$ +1,4-diethylbenzene + RDKit 2D + + 10 10 0 0 0 0 0 0 0 0999 V2000 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.0031 3.0008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0432 3.5993 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0031 -3.0008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.0432 -3.5993 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 4 5 1 0 + 3 6 2 0 + 6 7 1 0 + 7 8 2 0 + 8 1 1 0 + 8 9 1 0 + 9 10 1 0 +M END +> (42) +52 + +> (42) +1,4-diethylbenzene + +> (42) +-3.75 + +> (42) +(A) low + +> (42) +c1cc(CC)ccc1CC + +$$$$ +p-isopropyltoluene + RDKit 2D + + 10 10 0 0 0 0 0 0 0 0999 V2000 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 2.7000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0031 -3.0008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.0432 -3.5993 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0351 -3.6026 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 3 5 2 0 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0 0 0 0 0 0 0999 V2000 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0031 -3.0008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0351 -3.6026 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3039 -3.7494 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3064 -4.9494 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 1 1 0 + 6 7 1 0 + 7 8 1 0 + 7 9 1 0 + 9 10 1 0 +M END +> (45) +56 + +> (45) +2-butylbenzene + +> (45) +-3.89 + +> (45) +(A) low + +> (45) +c1ccccc1C(C)CC + +$$$$ +pentamethylbenzene + RDKit 2D + + 11 11 0 0 0 0 0 0 0 0999 V2000 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.3383 1.3500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 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-0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5929 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2964 1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2964 1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 3 4 1 0 + 4 5 2 0 + 5 6 1 0 + 6 7 2 0 + 7 8 1 0 + 8 9 2 0 + 9 10 1 0 + 10 5 1 0 + 10 11 2 0 + 11 2 1 0 +M END +> (55) +68 + +> (55) +2-methylnaphthalene + +> (55) +-3.77 + +> (55) +(A) low + +> (55) +Cc1ccc2ccccc2c1 + +$$$$ +1-methylnaphthalene + RDKit 2D + + 11 12 0 0 0 0 0 0 0 0999 V2000 + -2.6111 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6111 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2964 -1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2964 -1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5929 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5929 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2964 1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2964 1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2928 2.6973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 7 1 0 + 7 8 2 0 + 8 9 1 0 + 9 4 1 0 + 9 10 2 0 + 10 1 1 0 + 10 11 1 0 +M END +> (56) +69 + +> (56) +1-methylnaphthalene + +> (56) +-3.7 + +> (56) +(A) low + +> (56) +c1ccc2ccccc2c1C + +$$$$ +2-ethylnaphthalene + RDKit 2D + + 12 13 0 0 0 0 0 0 0 0999 V2000 + -3.9072 2.7019 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.9091 1.5019 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6111 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6111 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2964 -1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2964 -1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5929 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5929 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2964 1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2964 1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 7 1 0 + 7 8 2 0 + 8 9 1 0 + 9 10 2 0 + 10 11 1 0 + 11 6 1 0 + 11 12 2 0 + 12 3 1 0 +M END +> (57) +71 + +> (57) +2-ethylnaphthalene + +> (57) +-4.29 + +> (57) +(A) low + +> (57) +CCc1ccc2ccccc2c1 + +$$$$ +1,5-dimethylnaphthalene + RDKit 2D + + 12 13 0 0 0 0 0 0 0 0999 V2000 + -2.6111 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6111 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2964 -1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2964 -1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2964 -2.6973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5929 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5929 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2964 1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2964 1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2928 2.6973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 1 0 + 5 7 2 0 + 7 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12 2 1 0 +M END +> (59) +73 + +> (59) +2,3-dimethylnaphthalene + +> (59) +-4.72 + +> (59) +(A) low + +> (59) +Cc1c(C)cc2ccccc2c1 + +$$$$ +acenaphthylene + RDKit 2D + + 12 14 0 0 0 0 0 0 0 0999 V2000 + -2.5371 0.2373 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5371 -1.2229 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2594 -1.9530 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.2229 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2777 -1.9530 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5553 -1.2229 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5553 0.2373 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2777 0.9674 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 0.2373 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2594 0.9674 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.9674 2.4458 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8944 2.4276 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 7 1 0 + 7 8 2 0 + 8 9 1 0 + 9 4 1 0 + 9 10 2 0 + 10 1 1 0 + 10 11 1 0 + 11 12 2 3 + 12 8 1 0 +M END +> (60) +74 + +> (60) +acenaphthylene + +> (60) +-3.96 + +> (60) +(A) low + +> (60) +c1ccc2cccc3c2c1C=C3 + +$$$$ +1,4-dimethylnaphthalene + RDKit 2D + + 12 13 0 0 0 0 0 0 0 0999 V2000 + -2.6111 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6111 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2964 -1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2928 -2.6973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2964 -1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5929 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5929 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2964 1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2964 1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2928 2.6973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 3 5 2 0 + 5 6 1 0 + 6 7 2 0 + 7 8 1 0 + 8 9 2 0 + 9 10 1 0 + 10 5 1 0 + 10 11 2 0 + 11 1 1 0 + 11 12 1 0 +M END +> (61) +76 + +> (61) +1,4-dimethylnaphthalene + +> (61) +-4.14 + +> (61) +(A) low + +> (61) +c1cc(C)c2ccccc2c1C + +$$$$ +2,6-dimethylnaphthalene + RDKit 2D + + 12 13 0 0 0 0 0 0 0 0999 V2000 + -3.6486 1.3517 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6111 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6111 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2964 -1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2964 -1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5929 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.6321 -1.3486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5929 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2964 1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2964 1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 3 4 1 0 + 4 5 2 0 + 5 6 1 0 + 6 7 2 0 + 7 8 1 0 + 7 9 1 0 + 9 10 2 0 + 10 11 1 0 + 11 5 1 0 + 11 12 2 0 + 12 2 1 0 +M END +> (62) +77 + +> (62) +2,6-dimethylnaphthalene + +> (62) +-4.89 + +> (62) +(A) low + +> (62) +Cc1ccc2cc(C)ccc2c1 + +$$$$ +acenaphthene + RDKit 2D + + 12 14 0 0 0 0 0 0 0 0999 V2000 + -2.5371 0.2373 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5371 -1.2229 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2594 -1.9530 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.2229 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2777 -1.9530 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5553 -1.2229 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5553 0.2373 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2777 0.9674 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 0.2373 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2594 0.9674 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.9674 2.4458 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8944 2.4276 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 7 1 0 + 7 8 2 0 + 8 9 1 0 + 9 4 1 0 + 9 10 2 0 + 10 1 1 0 + 10 11 1 0 + 11 12 1 0 + 12 8 1 0 +M END +> (63) +78 + +> (63) +acenaphthene + +> (63) +-4.63 + +> (63) +(A) low + +> (63) +c1ccc2cccc3c2c1CC3 + +$$$$ +1,4,5-trimethylnaphthalene + RDKit 2D + + 13 14 0 0 0 0 0 0 0 0999 V2000 + -2.6111 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6111 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2964 -1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2928 -2.6973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2964 -1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2964 -2.6973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5929 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5929 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2964 1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2964 1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2928 2.6973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 3 5 2 0 + 5 6 1 0 + 6 7 1 0 + 6 8 2 0 + 8 9 1 0 + 9 10 2 0 + 10 11 1 0 + 11 5 1 0 + 11 12 2 0 + 12 1 1 0 + 12 13 1 0 +M END +> (64) +79 + +> (64) +1,4,5-trimethylnaphthalene + +> (64) +-4.92 + +> (64) +(A) low + +> (64) +c1cc(C)c2c(C)cccc2c1C + +$$$$ +phenantherene + RDKit 2D + + 14 16 0 0 0 0 0 0 0 0999 V2000 + 1.7503 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.4558 0.6928 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.8205 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.8205 -1.5498 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.4558 -2.2973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.1332 -2.2973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.4277 -1.5498 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.4277 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.1332 0.6928 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.4558 2.1879 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7503 2.9537 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.0631 2.1879 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7503 -1.5498 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.0631 0.6928 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 3 4 1 0 + 4 5 2 0 + 4 6 1 0 + 6 7 2 0 + 7 8 1 0 + 3 9 1 0 + 9 8 2 0 + 2 10 1 0 + 10 11 2 0 + 11 12 1 0 + 1 13 2 0 + 13 5 1 0 + 1 14 1 0 + 14 12 2 0 +M END +> (65) +81 + +> (65) +phenantherene + +> (65) +-5.26 + +> (65) +(A) low + +> (65) +c(c(c(c(c1)ccc2)c2)ccc3)(c1)c3 + +$$$$ +9-methylanthracene + RDKit 2D + + 15 17 0 0 0 0 0 0 0 0999 V2000 + 1.2806 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2806 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2989 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2989 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5978 1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8968 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8968 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5978 -1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0037 -2.7002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5978 -1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8968 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8968 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5978 1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 3 4 1 0 + 4 5 2 0 + 5 6 1 0 + 6 7 2 0 + 7 8 1 0 + 4 9 1 0 + 9 8 2 0 + 3 10 1 0 + 2 11 1 0 + 11 12 2 0 + 12 13 1 0 + 1 14 1 0 + 14 13 2 0 + 1 15 2 0 + 15 5 1 0 +M END +> (66) +82 + +> (66) +9-methylanthracene + +> (66) +-5.89 + +> 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0.0000 -2.3956 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2104 -2.0174 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.6643 -0.5800 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.1270 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.5809 1.2104 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.6052 0.5296 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.1096 1.9670 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5722 2.3452 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0504 3.1017 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 2 5 1 0 + 5 6 2 0 + 6 7 1 0 + 7 4 1 0 + 6 8 1 0 + 8 9 2 0 + 9 10 1 0 + 10 11 1 0 + 11 12 1 0 + 10 13 2 0 + 13 5 1 0 + 13 14 1 0 + 14 15 1 0 + 15 12 1 0 + 14 16 2 0 + 16 1 1 0 +M END +> (70) +87 + +> (70) +1,2,3,6,7,8-hexahydropyrene + +> (70) +-5.96 + +> (70) +(A) low + +> (70) +c1c(CC3)c2c(C3)ccc(CC4)c2c(C4)c1 + +$$$$ +benzo(a)fluorene + RDKit 2D + + 17 20 0 0 0 0 0 0 0 0999 V2000 + -1.0690 -1.3585 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.2938 -0.4677 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.7636 -0.8240 0.0000 C 0 0 0 0 0 0 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0 0 0 0 0 0 0 0 0 0 0 + -1.1200 3.5400 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.1800 2.7600 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.5100 3.5400 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.8400 2.7600 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.8400 1.2100 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.1800 0.4400 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.1800 -1.0700 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.8400 -1.8400 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.5100 -1.0700 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.5100 0.4400 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.1800 1.2100 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.1200 0.4400 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.1200 -1.0700 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 7 1 0 + 7 8 2 0 + 8 9 1 0 + 9 10 2 0 + 10 11 1 0 + 11 12 1 0 + 12 13 2 0 + 13 14 1 0 + 14 15 1 0 + 15 16 2 0 + 16 17 1 0 + 17 18 2 0 + 18 1 1 0 + 18 19 1 0 + 19 14 2 0 + 19 20 1 0 + 20 11 2 0 + 20 21 1 0 + 21 8 1 0 + 21 22 2 0 + 22 1 1 0 + 22 5 1 0 +M END +> (78) +97 + +> (78) 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0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.7500 0.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2400 0.1800 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.0900 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2400 0.1800 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 1 1 0 + 5 7 1 0 + 7 8 2 0 + 8 9 1 0 + 9 10 1 0 + 10 11 2 0 + 11 12 1 0 + 12 13 2 0 + 13 14 1 0 + 14 15 2 0 + 15 16 1 0 + 16 17 2 0 + 17 18 1 0 + 18 13 1 0 + 18 19 2 0 + 19 10 1 0 + 19 20 1 0 + 20 21 1 0 + 21 4 1 0 + 21 9 2 0 +M END +> (81) +101 + +> (81) +13H-dibenzo(a,i)carbazole + +> (81) +-7.42 + +> (81) +(A) low + +> (81) +c1ccc4c(c1)ccc5c3ccc2ccccc2c3n(H)c45 + +$$$$ +2-aminoanthracene + RDKit 2D + + 15 17 0 0 0 0 0 0 0 0999 V2000 + -4.9360 1.3500 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8968 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8968 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5978 -1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2989 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2806 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5978 -1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8968 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8968 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5978 1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2806 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2989 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5978 1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 3 4 1 0 + 4 5 2 0 + 5 6 1 0 + 6 7 2 0 + 7 8 1 0 + 8 9 2 0 + 9 10 1 0 + 10 11 2 0 + 11 12 1 0 + 12 7 1 0 + 12 13 2 0 + 13 14 1 0 + 14 5 1 0 + 14 15 2 0 + 15 2 1 0 +M END +> (82) +104 + +> (82) +2-aminoanthracene + +> (82) +-5.17 + +> (82) +(A) low + +> (82) +Nc3ccc2cc1ccccc1cc2c3 + +$$$$ +2-ethylanthracene + RDKit 2D + + 16 18 0 0 0 0 0 0 0 0999 V2000 + -3.8968 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5978 1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2989 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2806 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5978 1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8968 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1981 1.4978 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.2364 0.8963 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8968 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5978 -1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2806 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2989 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5978 -1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8968 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 7 1 0 + 7 8 1 0 + 8 9 1 0 + 7 10 2 0 + 10 11 1 0 + 11 12 2 0 + 12 5 1 0 + 12 13 1 0 + 13 14 2 0 + 14 3 1 0 + 14 15 1 0 + 15 16 2 0 + 16 1 1 0 +M END +> (83) +106 + +> (83) +2-ethylanthracene + +> (83) +-6.89 + +> (83) +(A) low + +> (83) +c1cc2cc3cc(CC)ccc3cc2cc1 + +$$$$ +benzo(b)fluoranthene + RDKit 2D + + 20 24 0 0 0 0 0 0 0 0999 V2000 + 2.3700 0.1300 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.7600 0.6200 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.8800 -0.2800 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.6300 -1.7400 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.2500 -2.2500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.1200 -1.3000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.7400 -1.8100 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.3600 -0.8700 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.0200 -1.0200 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.1100 -2.3100 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.7600 -2.0200 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -5.3500 -0.4500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.2800 0.8100 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6100 0.5500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2400 1.4800 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.1300 0.5600 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2500 1.0500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.4900 2.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.3600 3.4500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.9900 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0 0 0 0 0 0 0 0 + 3.1700 -2.5400 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.4400 -1.8300 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.4400 -0.3800 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.1700 0.3300 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.1700 1.7800 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.9000 2.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.6300 1.7800 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.6300 0.3300 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.9000 -0.3800 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.6300 -0.3800 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 1 1 0 + 5 7 1 0 + 7 8 2 0 + 8 9 1 0 + 9 10 1 0 + 10 11 1 0 + 11 12 2 0 + 12 13 1 0 + 13 14 2 0 + 14 15 1 0 + 15 16 2 0 + 16 17 1 0 + 17 18 2 0 + 18 19 1 0 + 19 10 2 0 + 19 14 1 0 + 18 20 1 0 + 20 4 1 0 + 20 9 2 0 +M END +> (85) +108 + +> (85) +benzo(j)fluoranthene + +> (85) +-8 + +> (85) +(A) low + +> (85) +c1ccc4c(c1)ccc5c2cccc3cccc(c23)c45 + +$$$$ +benzo(k)fluoranthene + RDKit 2D + + 20 24 0 0 0 0 0 0 0 0999 V2000 + -5.2400 0.8500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -5.2400 -0.9600 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.0100 -1.6600 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.8200 -0.9500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6200 -1.6600 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4200 -0.9500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4400 0.8600 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6400 1.5400 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.8200 0.8500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.0400 1.5300 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.9300 1.1200 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.6300 2.3600 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.0300 2.3300 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.7200 1.1200 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.0200 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.7100 -1.2800 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.9900 -2.4800 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.6100 -2.4500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.9200 -1.2600 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.6200 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 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0.0000 Br 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -2.7000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 4 6 1 0 + 6 7 2 0 + 7 1 1 0 + 7 8 1 0 +M END +> (171) +217 + +> (171) +m-bromotoluene + +> (171) +-3.52 + +> (171) +(A) low + +> (171) +c1ccc(Br)cc1C + +$$$$ +o-chlorotoluene + RDKit 2D + + 8 8 0 0 0 0 0 0 0 0999 V2000 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3383 -1.3500 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -2.7000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 1 0 + 5 7 2 0 + 7 1 1 0 + 7 8 1 0 +M END +> (172) +218 + +> (172) +o-chlorotoluene + +> (172) +-3.52 + +> 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C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -2.7000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 3 5 2 0 + 5 6 1 0 + 6 7 2 0 + 7 1 1 0 + 7 8 1 0 +M END +> (174) +220 + +> (174) +p-chlorotoluene + +> (174) +-3.08 + +> (174) +(A) low + +> (174) +c1cc(Cl)ccc1C + +$$$$ +2-chlorobiphenyl + RDKit 2D + + 13 14 0 0 0 0 0 0 0 0999 V2000 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -3.0008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2978 -3.7529 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3380 -3.1546 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2955 -5.2529 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0048 -6.0009 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 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0 0 0 0 0 0 0 0 0 0 0 0 + 1.3026 -5.2488 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3002 -3.7488 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 3 5 2 0 + 5 6 1 0 + 6 7 2 0 + 7 1 1 0 + 7 8 1 0 + 8 9 2 0 + 9 10 1 0 + 10 11 2 0 + 11 12 1 0 + 11 13 1 0 + 13 14 2 0 + 14 8 1 0 +M END +> (177) +224 + +> (177) +4,4ᄡ-PCB + +> (177) +-6.56 + +> (177) +(A) low + +> (177) +c1cc(Cl)ccc1c2ccc(Cl)cc2 + +$$$$ +2,2ᄡ-PCB + RDKit 2D + + 14 15 0 0 0 0 0 0 0 0999 V2000 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3383 -1.3500 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -3.0008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2978 -3.7529 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3380 -3.1546 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2955 -5.2529 0.0000 C 0 0 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0 0 + -2.3337 -5.8546 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0048 -6.0009 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0067 -7.2009 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3026 -5.2488 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3002 -3.7488 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 1 1 0 + 6 7 1 0 + 7 8 2 0 + 8 9 1 0 + 9 10 1 0 + 9 11 2 0 + 11 12 1 0 + 11 13 1 0 + 13 14 2 0 + 14 7 1 0 +M END +> (180) +228 + +> (180) +3,4-PCB + +> (180) +-6.39 + +> (180) +(A) low + +> (180) +c1ccccc1c2cc(Cl)c(Cl)cc2 + +$$$$ +2,4-PCB + RDKit 2D + + 14 15 0 0 0 0 0 0 0 0999 V2000 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -3.0008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2978 -3.7529 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 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0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2978 -3.7529 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3380 -3.1546 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2955 -5.2529 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0048 -6.0009 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0067 -7.2009 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3026 -5.2488 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.3428 -5.8471 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3002 -3.7488 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 1 1 0 + 6 7 1 0 + 7 8 2 0 + 8 9 1 0 + 8 10 1 0 + 10 11 2 0 + 11 12 1 0 + 11 13 1 0 + 13 14 1 0 + 13 15 2 0 + 15 7 1 0 +M END +> (183) +232 + +> (183) +2,4,5-PCB + +> (183) +-6.27 + +> (183) +(A) low + +> (183) +c1ccccc1c2c(Cl)cc(Cl)c(Cl)c2 + +$$$$ +2,4ᄡ,5-PCB + RDKit 2D + + 15 16 0 0 0 0 0 0 0 0999 V2000 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 2.7000 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 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0 0 0 0 0 0 0 0999 V2000 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3383 1.3500 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -3.0008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2978 -3.7529 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3380 -3.1546 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2955 -5.2529 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0048 -6.0009 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3026 -5.2488 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.3428 -5.8471 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3002 -3.7488 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 4 6 1 0 + 6 7 2 0 + 7 1 1 0 + 7 8 1 0 + 8 9 2 0 + 9 10 1 0 + 9 11 1 0 + 11 12 2 0 + 12 13 1 0 + 13 14 1 0 + 13 15 2 0 + 15 8 1 0 +M END +> (186) +235 + +> (186) +2,3ᄡ,5-PCB + +> 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0 0 0 0 0 0 0 0 0 0 0 + 1.3002 -3.7488 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.3385 -3.1472 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 1 1 0 + 6 7 1 0 + 7 8 2 0 + 8 9 1 0 + 8 10 1 0 + 10 11 1 0 + 10 12 2 0 + 12 13 1 0 + 13 14 2 0 + 14 7 1 0 + 14 15 1 0 +M END +> (189) +239 + +> (189) +2,3,6-PCB + +> (189) +-6.29 + +> (189) +(A) low + +> (189) +c1ccccc1c2c(Cl)c(Cl)ccc2Cl + +$$$$ +2,2ᄡ,3,3ᄡ-PCB + RDKit 2D + + 16 17 0 0 0 0 0 0 0 0999 V2000 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3383 1.3500 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3383 -1.3500 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -3.0008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2978 -3.7529 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3380 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C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -3.0008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2978 -3.7529 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3380 -3.1546 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2955 -5.2529 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3337 -5.8546 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0048 -6.0009 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3026 -5.2488 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3002 -3.7488 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.3385 -3.1472 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 3 4 1 0 + 4 5 1 0 + 4 6 2 0 + 6 7 1 0 + 6 8 1 0 + 8 9 2 0 + 9 2 1 0 + 9 10 1 0 + 10 11 2 0 + 11 12 1 0 + 11 13 1 0 + 13 14 1 0 + 13 15 2 0 + 15 16 1 0 + 16 17 2 0 + 17 10 1 0 + 17 18 1 0 +M END +> (204) +258 + +> (204) +2,2ᄡ,3,3ᄡ,6,6ᄡ-PCB + +> (204) +-8.65 + +> (204) +(A) low + +> (204) +Clc1cc(Cl)c(Cl)cc1c2c(Cl)c(Cl)ccc2Cl + +$$$$ +2,2ᄡ,3,3ᄡ,5ᄡ,6-PCB + RDKit 2D + + 18 19 0 0 0 0 0 0 0 0999 V2000 + 2.3383 -1.3500 0.0000 Cl 0 0 0 0 0 0 0 0 0 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C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0067 -7.2009 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3026 -5.2488 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.3428 -5.8471 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3002 -3.7488 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 3 4 1 0 + 4 5 1 0 + 4 6 2 0 + 6 7 1 0 + 6 8 1 0 + 8 9 1 0 + 8 10 2 0 + 10 2 1 0 + 10 11 1 0 + 11 12 2 0 + 12 13 1 0 + 12 14 1 0 + 14 15 2 0 + 15 16 1 0 + 15 17 1 0 + 17 18 1 0 + 17 19 2 0 + 19 11 1 0 +M END +> (212) +268 + +> (212) +2,2ᄡ,3,4,4ᄡ,5ᄡ,6-PCB + +> (212) +-7.92 + +> (212) +(A) low + +> (212) +Clc1cc(Cl)c(Cl)c(Cl)c1c2c(Cl)cc(Cl)c(Cl)c2 + +$$$$ +2,2ᄡ,3,3ᄡ,4,4ᄡ,6-PCB + RDKit 2D + + 19 20 0 0 0 0 0 0 0 0999 V2000 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 2.7000 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3383 1.3500 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 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3.7496 1.2177 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2756 4.0395 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.7059 4.2135 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 4 5 1 0 + 5 6 1 0 + 6 7 1 0 + 7 8 1 0 + 8 9 1 0 + 9 10 1 0 + 10 11 2 3 + 11 12 1 0 + 12 13 2 0 + 12 14 1 0 + 10 15 1 0 + 9 16 1 0 + 16 14 1 0 + 9 17 1 0 + 8 18 1 0 + 7 19 1 0 + 19 15 1 0 + 19 20 1 0 + 20 21 1 0 + 21 22 2 0 + 21 23 1 0 + 6 24 1 0 + 5 25 1 0 + 25 18 1 0 + 5 26 1 0 + 4 27 1 0 + 27 24 1 0 + 4 28 1 0 + 2 29 1 0 + 29 28 1 0 +M END +> (406) +510 + +> (406) +spironolactone + +> (406) +-4.28 + +> (406) +(A) low + +> (406) +O=C(OC(C(C(C(C(C(C(=CC(=O)C1)C2)(C1)C)C3)C2SC(=O)C)C4)(C3)C)(C4)C5)C5 + +$$$$ +estrone + RDKit 2D + + 20 23 0 0 0 0 0 0 0 0999 V2000 + 3.2968 3.7367 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.3032 2.5367 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0440 1.7885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0440 0.3467 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8030 -0.3650 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4380 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+> (407) +512 + +> (407) +estrone + +> (407) +-3.96 + +> (407) +(A) low + +> (407) +O=C(C(C(C(C(c(c(cc(O)c1)C2)c1)C3)C2)C4)(C3)C)C4 + +$$$$ +deoxycorticosterone_acetate + RDKit 2D + + 27 30 0 0 0 0 0 0 0 0999 V2000 + -4.1792 -0.4015 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.1792 -1.8432 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -5.2209 -2.4390 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -2.9382 -2.5732 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6790 -1.8250 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4197 -2.5550 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8212 -1.8067 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8030 -0.3650 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0440 0.3467 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5625 0.3285 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5625 1.8067 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.3032 2.5367 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.3007 4.0378 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.2604 4.6358 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5988 4.7911 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5964 6.2919 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 5.8945 7.0453 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.8925 8.2453 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 6.9348 6.4473 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0440 1.7885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0413 2.9885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8030 2.5185 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4562 1.7885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4380 0.3285 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6790 -0.3832 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6930 0.8167 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.9565 0.3102 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 2 4 1 0 + 4 5 2 3 + 5 6 1 0 + 6 7 1 0 + 7 8 1 0 + 8 9 1 0 + 9 10 1 0 + 10 11 1 0 + 11 12 1 0 + 12 13 1 0 + 13 14 2 0 + 13 15 1 0 + 15 16 1 0 + 16 17 1 0 + 17 18 2 0 + 17 19 1 0 + 12 20 1 0 + 20 9 1 0 + 20 21 1 0 + 20 22 1 0 + 22 23 1 0 + 23 24 1 0 + 24 8 1 0 + 24 25 1 0 + 25 5 1 0 + 25 26 1 0 + 25 27 1 0 + 27 1 1 0 +M END +> (408) +513 + +> (408) +deoxycorticosterone_acetate + +> (408) +-4.63 + +> (408) +(A) low + +> (408) +C1C(=O)C=C2CCC3C4CCC(C(=O)COC(=O)C)C4(C)CCC3C2(C)C1 + +$$$$ +17-methyltestosterone + RDKit 2D + + 22 25 0 0 0 0 0 0 0 0999 V2000 + -5.2209 -2.4390 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -4.1792 -1.8432 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.9382 -2.5732 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6790 -1.8250 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6790 -0.3832 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4380 0.3285 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8030 -0.3650 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0440 0.3467 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0440 1.7885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.3032 2.5367 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.2474 3.1070 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 4.3528 3.1185 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5625 1.8067 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8030 2.5185 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.0061 1.1862 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5625 0.3285 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8212 -1.8067 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4562 1.7885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.9565 0.3102 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6930 0.8167 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4197 -2.5550 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.1792 -0.4015 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 3 + 4 5 1 0 + 5 6 1 0 + 6 7 1 0 + 7 8 1 0 + 8 9 1 0 + 9 10 1 0 + 10 11 1 0 + 10 12 1 0 + 10 13 1 0 + 9 14 1 0 + 9 15 1 0 + 8 16 1 0 + 16 13 1 0 + 7 17 1 0 + 6 18 1 0 + 18 14 1 0 + 5 19 1 0 + 5 20 1 0 + 4 21 1 0 + 21 17 1 0 + 2 22 1 0 + 22 19 1 0 +M END +> (409) +514 + +> (409) +17-methyltestosterone + +> (409) +-3.99 + +> (409) +(A) low + +> (409) +O=C(C=C(C(C(C(C(C(C(O)(C)C1)(C2)C)C1)C3)C2)(C4)C)C3)C4 + +$$$$ +androstenedione + RDKit 2D + + 21 24 0 0 0 0 0 0 0 0999 V2000 + -4.1792 -0.4015 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.1792 -1.8432 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -5.2209 -2.4390 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -2.9382 -2.5732 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6790 -1.8250 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 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0 0 0 0 0 0 0 0 0 0 0 0 + 2.4229 3.0112 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5993 3.2532 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.6120 2.6093 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 4.6639 4.7526 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.9943 5.4472 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 6.0590 6.9466 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 7.1227 7.5020 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 5.0463 7.5905 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0440 1.7885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.1791 1.2866 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8030 2.5185 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4562 1.7885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.4997 2.3811 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4380 0.3285 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.3064 0.8243 0.0000 F 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6790 -0.3832 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5391 -0.8933 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.9565 0.3102 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.1792 -0.4015 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 3 + 4 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3.9000 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.9000 1.9500 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 4.9394 0.1503 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 2 4 1 0 + 2 5 1 0 + 5 6 1 0 + 5 7 1 0 + 7 8 1 0 + 7 9 2 0 +M END +> (496) +624 + +> (496) +pencillamine + +> (496) +-0.13 + +> (496) +(C) high + +> (496) +CC(C)(S)C(N)C(O)=O + +$$$$ +haloperidol + RDKit 2D + + 26 28 0 0 0 0 0 0 0 0999 V2000 + 2.3238 -0.1256 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6003 1.4977 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8990 0.7455 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -5.2003 1.4932 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -6.4990 0.7409 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -6.4969 -0.4591 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -7.8003 1.4887 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -9.0994 0.7387 0.0000 C 0 0 0 0 0 0 0 0 0 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C 0 0 0 0 0 0 0 0 0 0 0 0 + -8.8378 3.5932 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -6.4995 3.7432 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -5.2004 2.9933 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8933 -0.7554 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.8522 -1.3521 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6061 2.9986 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.6472 3.5953 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 3 4 1 0 + 4 5 2 0 + 5 6 1 0 + 6 7 2 3 + 7 8 1 0 + 8 9 2 0 + 9 10 1 0 + 10 11 2 0 + 11 12 1 0 + 11 13 1 0 + 8 14 1 0 + 14 13 2 0 + 7 15 1 0 + 15 16 1 0 + 6 17 1 0 + 17 18 1 0 + 5 19 1 0 + 2 20 1 0 + 20 19 2 0 +M END +> (499) +628 + +> (499) +DES + +> (499) +-4.35 + +> (499) +(A) low + +> (499) +Oc(ccc(C(=C(c(ccc(O)c1)c1)CC)CC)c2)c2 + +$$$$ +chloramphenicol + RDKit 2D + + 20 20 0 0 0 0 0 0 0 0999 V2000 + 2.8509 -5.8560 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8912 -5.2578 0.0000 C 0 0 0 0 0 0 0 0 0 0 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-1.2989 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5002 0.0000 S 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2806 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5978 -1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8968 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8968 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5978 1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2806 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5002 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0058 3.0010 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2905 3.7573 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2846 5.2581 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5809 6.0145 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5762 7.2145 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.6227 5.4189 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2989 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5978 1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 1 0 + 6 7 2 0 + 7 8 1 0 + 8 9 2 0 + 9 10 1 0 + 10 11 2 0 + 11 6 1 0 + 11 12 1 0 + 12 13 1 0 + 13 14 1 0 + 14 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0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.6486 -1.3517 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2964 1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 2 0 + 2 4 1 0 + 2 5 1 0 + 5 6 2 0 + 6 7 1 0 + 7 8 2 0 + 8 9 1 0 + 9 10 1 0 + 10 11 1 0 + 11 12 1 0 + 12 13 2 0 + 12 14 2 0 + 8 15 1 0 + 15 12 1 0 + 6 16 1 0 + 5 17 1 0 + 17 15 2 0 +M END +> (507) +638 + +> (507) +hydrochlorthiazide + +> (507) +-2.62 + +> (507) +(B) medium + +> (507) +O=S(=O)(N)c(c(cc(NCNS1(=O)=O)c12)Cl)c2 + +$$$$ +chlorothiazide + RDKit 2D + + 17 18 0 0 0 0 0 0 0 0999 V2000 + -3.9101 2.6992 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -3.9106 1.4992 0.0000 S 0 0 0 0 0 0 0 0 0 0 0 0 + -4.9503 0.9000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -4.9495 2.0999 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6111 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6111 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.6486 -1.3517 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2964 -1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 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-5.2578 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1894 -6.0109 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1873 -7.5117 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.2253 -8.1139 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.4920 -5.2654 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 7.5293 -5.8687 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3383 1.3500 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 4 5 1 0 + 5 6 1 0 + 6 7 1 0 + 7 8 1 0 + 6 9 1 0 + 9 10 1 0 + 2 11 1 0 + 11 12 2 0 + 12 13 1 0 + 13 14 2 0 + 14 15 1 0 + 14 16 1 0 + 11 17 1 0 + 17 16 2 0 +M END +> (509) +640 + +> (509) +procaine + +> (509) +-1.4 + +> (509) +(B) medium + +> (509) +O=C(OCCN(CC)CC)c(ccc(N)c1)c1 + +$$$$ +promethazine + RDKit 2D + + 20 22 0 0 0 0 0 0 0 0999 V2000 + -3.8968 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8968 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5978 -1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2989 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5002 0.0000 S 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2806 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5978 -1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8968 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8968 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5978 1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2806 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5002 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0058 3.0010 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2905 3.7573 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3323 3.1617 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2846 5.2581 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3211 5.8629 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.2428 5.8537 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2989 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5978 1.5002 0.0000 C 0 0 0 0 0 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0 0 0 0 0 0 0 0 0 0 0 0 + 3.7308 4.4645 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.0039 5.2543 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.0620 4.6881 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.3406 5.0280 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0517 6.1927 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3751 3.8801 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.1776 3.9578 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 1 0 + 5 7 2 0 + 7 1 1 0 + 7 8 1 0 + 8 9 2 0 + 9 10 1 0 + 10 1 1 0 + 10 11 1 0 + 11 12 1 0 + 12 13 1 0 + 13 14 1 0 + 14 15 1 0 + 13 16 1 0 + 16 17 1 0 + 16 18 1 0 + 18 11 1 0 + 18 19 1 0 +M END +> (515) +648 + +> (515) +tubercidin + +> (515) +-1.95 + +> (515) +(B) medium + +> (515) +c12ncnc(N)c1ccn2C3OC(CO)C(O)C3(O) + +$$$$ +sulfathiozole + RDKit 2D + + 16 17 0 0 0 0 0 0 0 0999 V2000 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 2.7000 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 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-5.6333 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3198 -4.7137 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8068 -3.3033 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7710 -2.1532 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.9528 -2.3614 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5629 0.0216 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.6026 -0.5776 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.3383 1.3500 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3383 1.3500 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -2.7000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 1 0 + 4 5 1 0 + 4 6 1 0 + 6 7 1 0 + 7 8 1 0 + 6 9 1 0 + 9 10 2 0 + 9 11 1 0 + 11 12 1 0 + 12 13 2 0 + 12 14 1 0 + 14 15 1 0 + 15 6 1 0 + 15 16 2 0 +M END +> (517) +650 + +> (517) 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N 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8912 -5.2578 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.9292 -5.8600 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8889 -6.4578 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.8509 -5.8560 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 2 4 1 0 + 4 5 2 0 + 4 6 1 0 + 6 7 1 0 + 7 8 2 0 + 8 9 1 0 + 9 10 2 0 + 10 11 1 0 + 11 12 1 0 + 12 13 1 0 + 13 14 2 0 + 13 15 1 0 + 15 16 1 0 + 16 17 1 0 + 16 18 1 0 + 16 19 1 0 + 11 20 2 0 + 20 7 1 0 +M END +> (550) +693 + +> (550) +kasugamycin + +> (550) +-2.93 + +> (550) +(B) medium + +> (550) +CN(C)C(=O)Nc1cccc(OC(=O)NC(C)(C)C)c1 + +$$$$ +flurbiprofen + RDKit 2D + + 18 19 0 0 0 0 0 0 0 0999 V2000 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -3.0008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2978 -3.7529 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3380 -3.1546 0.0000 F 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2955 -5.2529 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0048 -6.0009 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0102 -7.5017 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0270 -8.1051 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3123 -8.2482 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3166 -9.4482 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.3495 -7.6447 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3026 -5.2488 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3002 -3.7488 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 1 1 0 + 6 7 1 0 + 7 8 2 0 + 8 9 1 0 + 8 10 1 0 + 10 11 2 0 + 11 12 1 0 + 12 13 1 0 + 12 14 1 0 + 14 15 2 0 + 14 16 1 0 + 11 17 1 0 + 17 18 2 0 + 18 7 1 0 +M END +> (551) +694 + +> (551) +flurbiprofen + +> (551) +-4.49 + +> (551) +(A) low + +> (551) +c1ccccc1c2c(F)cc(C(C)C(=O)O)cc2 + +$$$$ +ditalimfos + RDKit 2D + + 19 20 0 0 0 0 0 0 0 0999 V2000 + 6.9131 2.4056 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.3243 1.3600 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.8236 1.3429 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 4.0872 0.0351 0.0000 P 0 0 0 0 0 0 0 0 0 0 0 0 + 4.6987 -0.9974 0.0000 S 0 0 0 0 0 0 0 0 0 0 0 0 + 5.6171 0.0499 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 6.3808 -1.2421 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 7.5807 -1.2305 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5889 0.0182 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7138 1.2033 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0825 2.3453 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2917 0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0028 1.5132 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3155 0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3155 -0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0028 -1.5132 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2917 -0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7138 -1.2033 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0907 -2.3426 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 1 0 + 4 5 2 0 + 4 6 1 0 + 6 7 1 0 + 7 8 1 0 + 4 9 1 0 + 9 10 1 0 + 10 11 2 0 + 10 12 1 0 + 12 13 2 0 + 13 14 1 0 + 14 15 2 0 + 15 16 1 0 + 16 17 2 0 + 17 12 1 0 + 17 18 1 0 + 18 9 1 0 + 18 19 2 0 +M END +> (552) +695 + +> (552) +ditalimfos + +> (552) +-3.35 + +> (552) +(A) low + +> (552) +CCOP(=S)(OCC)N2C(=O)c1ccccc1C2=O + +$$$$ +carboxin + RDKit 2D + + 16 17 0 0 0 0 0 0 0 0999 V2000 + 2.3383 -1.3500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 S 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5972 1.5031 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.6375 0.9049 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5951 3.0039 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8933 3.7570 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8933 5.2571 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1924 6.0071 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.4914 5.2571 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.4915 3.7571 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1925 3.0071 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 3 + 3 4 1 0 + 4 5 1 0 + 5 6 1 0 + 6 7 1 0 + 7 2 1 0 + 3 8 1 0 + 8 9 2 0 + 8 10 1 0 + 10 11 1 0 + 11 12 2 0 + 12 13 1 0 + 13 14 2 0 + 14 15 1 0 + 15 16 2 0 + 16 11 1 0 +M END +> (553) +696 + +> (553) +carboxin + +> (553) +-3.14 + +> (553) +(A) low + +> (553) +CC1=C(SCCO1)C(=O)Nc2ccccc2 + +$$$$ +chlordimenform + RDKit 2D + + 13 13 0 0 0 0 0 0 0 0999 V2000 + 6.2387 -0.8917 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.2003 -1.4932 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 5.2024 -2.6932 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8990 -0.7455 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.6003 -1.4977 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3383 1.3500 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -2.7000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 2 4 1 0 + 4 5 2 3 + 5 6 1 0 + 6 7 2 0 + 7 8 1 0 + 8 9 2 0 + 9 10 1 0 + 9 11 1 0 + 11 12 2 0 + 12 6 1 0 + 12 13 1 0 +M END +> (554) +698 + +> (554) +chlordimenform + +> (554) +-2.86 + +> (554) +(B) medium + +> (554) +CN(C)C=Nc1ccc(Cl)cc1C + +$$$$ +nifuroxime + RDKit 2D + + 11 11 0 0 0 0 0 0 0 0999 V2000 + 0.7500 -1.0323 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2135 0.3943 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.6375 0.8603 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 2.8853 2.0344 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.5307 0.0589 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.2760 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2135 0.3943 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.7500 -1.0323 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6281 -2.2462 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0157 -3.6164 0.0000 N 0 0 0 0 0 0 0 0 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0 0 0 0 0 0 0 0 0 0 0 0 + -0.7464 -5.2884 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.7536 -5.2860 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2149 -3.8587 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 2 0 + 3 5 1 0 + 5 6 1 0 + 6 7 2 0 + 7 8 1 0 + 8 9 2 0 + 9 10 1 0 + 10 11 2 0 + 11 6 1 0 + 11 12 1 0 + 12 13 1 0 + 13 14 1 0 + 14 15 1 0 + 15 16 1 0 + 16 12 1 0 +M END +> (556) +700 + +> (556) +dioxacarb + +> (556) +-1.57 + +> (556) +(B) medium + +> (556) +CNC(=O)Oc1ccccc1C2OCCO2 + +$$$$ +ibuprofen + RDKit 2D + + 15 15 0 0 0 0 0 0 0 0999 V2000 + 3.6331 -3.6060 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5951 -3.0039 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.5548 -3.6021 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5973 -1.5031 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6003 1.4977 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8990 0.7455 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.9395 1.3433 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8969 -0.4545 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.6375 -0.9049 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 2 4 1 0 + 4 5 1 0 + 5 6 2 0 + 6 7 1 0 + 7 8 2 0 + 8 9 1 0 + 8 10 1 0 + 10 11 1 0 + 11 12 1 0 + 11 13 1 0 + 5 14 1 0 + 14 9 2 0 + 4 15 1 0 +M END +> (557) +701 + +> (557) +ibuprofen + +> (557) +-3.99 + +> (557) +(A) low + +> (557) +O=C(O)C(c(ccc(c1)CC(C)C)c1)C + +$$$$ +naprosyn + RDKit 2D + + 17 18 0 0 0 0 0 0 0 0999 V2000 + -3.9072 2.7019 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.9091 1.5019 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6111 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6111 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2964 -1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2964 -1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5929 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5929 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2964 1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2964 1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8942 -1.4964 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8963 -2.6964 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1929 -0.7441 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1908 0.4559 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 6.2333 -1.3420 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 7 1 0 + 7 8 2 0 + 8 9 1 0 + 9 10 2 0 + 10 11 1 0 + 11 6 1 0 + 11 12 2 0 + 12 3 1 0 + 8 13 1 0 + 13 14 1 0 + 13 15 1 0 + 15 16 1 0 + 15 17 2 0 +M END +> (558) +703 + +> (558) +naprosyn + +> (558) +-4.16 + +> (558) +(A) low + +> (558) +COc2ccc1cc(ccc1c2)C(C)C(O)=O + +$$$$ +benznidazole + RDKit 2D + + 19 20 0 0 0 0 0 0 0 0999 V2000 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0031 -3.0008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3039 -3.7494 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3070 -5.2502 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2688 -5.8520 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.6078 -5.9988 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.6109 -7.4996 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8244 -8.3594 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.2467 -7.8885 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 6.1435 -8.6858 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 5.4892 -6.7132 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.3609 -9.7860 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 1.8609 -9.7860 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3974 -8.3594 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 1 1 0 + 6 7 1 0 + 7 8 1 0 + 8 9 1 0 + 9 10 2 0 + 9 11 1 0 + 11 12 1 0 + 12 13 1 0 + 13 14 1 0 + 14 15 1 0 + 14 16 2 0 + 13 17 2 3 + 17 18 1 0 + 18 19 2 3 + 19 12 1 0 +M CHG 2 14 1 15 -1 +M END +> (559) +704 + +> (559) +benznidazole + +> (559) +-2.81 + +> (559) +(B) medium + +> (559) +c1ccccc1CNC(=O)CN2C(N(=O)=O)=NC=C2 + +$$$$ +fenbufen + RDKit 2D + + 19 20 0 0 0 0 0 0 0 0999 V2000 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -3.0008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2978 -3.7529 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2955 -5.2529 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0048 -6.0009 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0102 -7.5017 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0270 -8.1051 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3123 -8.2482 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3177 -9.7490 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.6197 -10.4955 0.0000 C 0 0 0 0 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N 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2955 -5.2529 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3337 -5.8546 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0048 -6.0009 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0067 -7.2009 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3026 -5.2488 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.3428 -5.8471 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3002 -3.7488 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 1 0 + 4 5 1 0 + 5 6 1 0 + 6 1 1 0 + 6 7 1 0 + 7 8 2 0 + 8 9 1 0 + 9 10 1 0 + 9 11 2 0 + 11 12 1 0 + 11 13 1 0 + 13 14 1 0 + 13 15 2 0 + 15 7 1 0 +M CHG 2 11 1 12 -1 +M END +> (561) +706 + +> (561) +minoxidil + +> (561) +-1.98 + +> (561) +(B) medium + +> (561) +C1CCCCN1c2nc(N)n(=O)c(N)c2 + +$$$$ +tetroxoprim + RDKit 2D + + 24 25 0 0 0 0 0 0 0 0999 V2000 + 1.2990 -0.7500 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.3383 1.3500 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 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0 0 + 2.3383 1.3500 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3383 1.3500 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3383 -1.3500 0.0000 F 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 2 4 1 0 + 4 5 1 0 + 5 6 2 0 + 5 7 1 0 + 7 8 1 0 + 7 9 2 3 + 9 1 1 0 +M END +> (565) +711 + +> (565) +5-fluorouracil + +> (565) +-1.07 + +> (565) +(B) medium + +> (565) +N1C(=O)NC(=O)C(F)=C1 + +$$$$ +2-(1H)quinolinone + RDKit 2D + + 11 12 0 0 0 0 0 0 0 0999 V2000 + -2.6111 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6111 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2964 -1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2964 -1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5929 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5929 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.6321 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C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3383 1.3500 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 2 4 1 0 + 4 5 2 0 + 5 6 1 0 + 6 7 2 0 + 7 8 1 0 + 7 9 1 0 + 4 10 1 0 + 10 9 2 0 +M CHG 2 1 -1 2 1 +M END +> (622) +783 + +> (622) +4-nitroaniline + +> (622) +-2.37 + +> (622) +(B) medium + +> (622) +O=N(=O)c(ccc(N)c1)c1 + +$$$$ +quanidinoacetic_acid + RDKit 2D + + 8 7 0 0 0 0 0 0 0 0999 V2000 + 1.3000 1.9500 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3000 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2606 0.1503 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 2.6000 0.0000 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 3.9000 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.2000 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.2394 0.5997 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 5.2000 -1.2000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 2 4 1 0 + 4 5 1 0 + 5 6 1 0 + 6 7 2 0 + 6 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C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3383 1.3500 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 4 5 1 0 + 3 6 1 0 + 2 7 1 0 + 7 8 1 0 + 8 9 2 0 + 9 10 1 0 + 10 11 2 0 + 11 12 1 0 + 11 13 1 0 + 8 14 1 0 + 14 12 2 0 +M END +> (635) +799 + +> (635) +monolinuron + +> (635) +-2.57 + +> (635) +(B) medium + +> (635) +O=C(N(OC)C)Nc(ccc(c1)Cl)c1 + +$$$$ +monuron + RDKit 2D + + 13 13 0 0 0 0 0 0 0 0999 V2000 + 1.5548 -3.6021 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5951 -3.0039 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8933 -3.7570 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8916 -4.9570 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.9336 -3.1588 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5973 -1.5031 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 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C 0 0 0 0 0 0 0 0 0 0 0 0 + -5.2007 1.4909 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -6.4972 0.7364 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -6.4920 -0.7636 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -5.1903 -1.5091 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -7.5291 -1.3672 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -5.2049 2.6909 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8939 -0.7546 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 2 4 1 0 + 4 5 2 0 + 5 6 1 0 + 6 7 2 0 + 7 8 1 0 + 8 9 1 0 + 9 10 2 0 + 10 11 1 0 + 11 12 2 0 + 12 13 1 0 + 12 14 1 0 + 10 15 1 0 + 9 16 1 0 + 16 13 2 0 + 7 17 1 0 + 4 18 1 0 + 18 17 2 0 +M CHG 2 1 -1 2 1 +M END +> (643) +809 + +> (643) +nitrofen + +> (643) +-5.46 + +> (643) +(A) low + +> (643) +O=N(=O)c(ccc(Oc(c(cc(c1)Cl)Cl)c1)c2)c2 + +$$$$ +trifluralin + RDKit 2D + + 23 23 0 0 0 0 0 0 0 0999 V2000 + 3.8916 -4.9570 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8933 -3.7570 0.0000 C 0 0 0 0 0 0 0 0 0 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C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3000 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.6000 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.9000 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.9394 0.1503 0.0000 S 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 1 0 + 4 5 1 0 +M END +> (646) +813 + +> (646) +butanethiol + +> (646) +-2.18 + +> (646) +(B) medium + +> (646) +CCCCS + +$$$$ +thiophenol + RDKit 2D + + 7 7 0 0 0 0 0 0 0 0999 V2000 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -2.7000 0.0000 S 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 1 1 0 + 6 7 1 0 +M END +> (647) +814 + +> (647) +thiophenol + +> (647) +-2.12 + +> (647) +(B) medium + +> (647) +c1ccccc1S + +$$$$ +ametryn + RDKit 2D + + 15 15 0 0 0 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6.9348 6.4473 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5964 6.2919 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5988 4.7911 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.3007 4.0378 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.3032 2.5367 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0440 1.7885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0440 0.3467 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8030 -0.3650 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4380 0.3285 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6790 -0.3832 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6790 -1.8250 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4197 -2.5550 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.9382 -2.5732 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.1792 -1.8432 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -5.2209 -2.4390 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -4.1792 -0.4015 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.9565 0.3102 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6930 0.8167 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4562 1.7885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8212 -1.8067 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5625 0.3285 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8030 2.5185 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8068 3.7185 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0413 2.9885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5625 1.8067 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.2604 4.6358 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 2 4 1 0 + 4 5 1 0 + 5 6 1 0 + 6 7 1 0 + 7 8 1 0 + 8 9 1 0 + 9 10 1 0 + 10 11 1 0 + 11 12 1 0 + 12 13 1 0 + 13 14 1 0 + 13 15 1 0 + 15 16 1 0 + 16 17 1 0 + 16 18 1 0 + 12 19 1 0 + 19 18 1 0 + 12 20 1 0 + 11 21 1 0 + 10 22 1 0 + 22 14 1 0 + 9 23 1 0 + 8 24 1 0 + 24 21 1 0 + 24 25 1 0 + 8 26 1 0 + 7 27 1 0 + 27 23 1 0 + 6 28 1 0 +M END +> (671) +844 + +> (671) +deoxycholic_acid + +> (671) +-3.95 + +> (671) +(A) low + +> (671) +O=C(O)CCC(C(C(C(C(C(C(C(C1)CC(O)C2)(C2)C)C3)C1)C4)(C3O)C)C4)C + +$$$$ +hyodeoxycholic_acid + RDKit 2D + + 28 31 0 0 0 0 0 0 0 0999 V2000 + -4.1792 -0.4015 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.1792 -1.8432 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -5.2209 -2.4390 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -2.9382 -2.5732 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6790 -1.8250 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4197 -2.5550 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8212 -1.8067 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.8698 -2.3902 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8030 -0.3650 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0440 0.3467 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5625 0.3285 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5625 1.8067 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.3032 2.5367 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.3007 4.0378 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.2604 4.6358 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5988 4.7911 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5964 6.2919 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.8945 7.0453 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.8925 8.2453 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 6.9348 6.4473 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0440 1.7885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0413 2.9885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8030 2.5185 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4562 1.7885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4380 0.3285 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6790 -0.3832 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6930 0.8167 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.9565 0.3102 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 2 4 1 0 + 4 5 1 0 + 5 6 1 0 + 6 7 1 0 + 7 8 1 0 + 7 9 1 0 + 9 10 1 0 + 10 11 1 0 + 11 12 1 0 + 12 13 1 0 + 13 14 1 0 + 14 15 1 0 + 14 16 1 0 + 16 17 1 0 + 17 18 1 0 + 18 19 2 0 + 18 20 1 0 + 13 21 1 0 + 21 10 1 0 + 21 22 1 0 + 21 23 1 0 + 23 24 1 0 + 24 25 1 0 + 25 9 1 0 + 25 26 1 0 + 26 5 1 0 + 26 27 1 0 + 26 28 1 0 + 28 1 1 0 +M END +> (672) +845 + +> (672) +hyodeoxycholic_acid + +> (672) +-3.82 + +> (672) +(A) low + +> (672) +C1C(O)CC2CC(O)C3C4CCC(C(C)CCC(=O)O)C4(C)CCC3C2(C)C1 + +$$$$ +chenodeoxycholic_acid + RDKit 2D + + 28 31 0 0 0 0 0 0 0 0999 V2000 + 2.2604 4.6358 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.3007 4.0378 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5988 4.7911 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5964 6.2919 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.8945 7.0453 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.9348 6.4473 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 5.8925 8.2453 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.3032 2.5367 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5625 1.8067 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5625 0.3285 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0440 0.3467 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8030 -0.3650 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8212 -1.8067 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.8698 -2.3902 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4197 -2.5550 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6790 -1.8250 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.9382 -2.5732 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.1792 -1.8432 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -5.2209 -2.4390 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -4.1792 -0.4015 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.9565 0.3102 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6790 -0.3832 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6930 0.8167 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 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0 0 0 0 0 0 0 0 0 0 + 2.5951 -3.0039 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.5548 -3.6021 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5973 -1.5031 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3383 -1.3500 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 3 0 + 4 5 1 0 + 5 6 1 0 + 6 7 1 0 + 7 8 2 0 + 7 9 1 0 + 9 10 1 0 + 10 11 2 0 + 11 12 1 0 + 12 13 2 0 + 13 14 1 0 + 14 15 1 0 + 14 16 2 0 + 16 10 1 0 +M END +> (703) +885 + +> (703) +barbane + +> (703) +-4.37 + +> (703) +(A) low + +> (703) +ClCC#CCOC(=O)Nc1cccc(Cl)c1 + +$$$$ +tetracycline + RDKit 2D + + 32 35 0 0 0 0 0 0 0 0999 V2000 + 4.3122 -4.1184 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.2722 -3.5199 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 2.2340 -4.1217 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.2691 -2.0191 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.9711 -1.2660 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.6891 -2.0191 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.6089 -1.2660 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.6089 0.2243 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.6891 0.9935 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.6923 2.1935 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.9711 0.2243 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.9743 1.4243 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.2691 0.9935 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.2691 2.1935 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5671 0.2243 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5671 -1.2660 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.6070 -1.8648 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 5.8752 0.9603 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.8883 2.1603 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 6.9081 0.3494 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -1.9070 0.9935 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.9070 2.1935 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -3.2050 0.2243 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.5030 0.9935 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.5030 2.1935 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -5.8010 0.2243 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -5.8010 -1.2660 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.5030 -2.0191 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.2050 -1.2660 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.9070 -2.0191 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.8540 -2.5946 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.9599 -2.5948 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 2 4 1 0 + 4 5 1 0 + 5 6 1 0 + 6 7 1 0 + 7 8 1 0 + 8 9 2 3 + 9 10 1 0 + 9 11 1 0 + 11 5 1 0 + 11 12 1 0 + 11 13 1 0 + 13 14 2 0 + 13 15 1 0 + 15 16 2 3 + 16 4 1 0 + 16 17 1 0 + 15 18 1 0 + 18 19 1 0 + 18 20 2 0 + 8 21 1 0 + 21 22 2 0 + 21 23 1 0 + 23 24 2 0 + 24 25 1 0 + 24 26 1 0 + 26 27 2 0 + 27 28 1 0 + 28 29 2 0 + 29 23 1 0 + 29 30 1 0 + 30 7 1 0 + 30 31 1 0 + 30 32 1 0 +M END +> (704) +886 + +> (704) +tetracycline + +> (704) +-3.12 + +> (704) +(A) low + +> (704) 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Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 2.3383 1.3500 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -2.7000 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 2 4 1 0 + 4 5 1 0 + 5 6 2 0 + 6 7 1 0 + 7 8 2 0 + 8 9 1 0 + 9 10 1 0 + 6 11 1 0 + 5 12 1 0 + 12 9 2 0 + 12 13 1 0 +M END +> (806) +1020 + +> (806) +Chlorfenac + +> (806) +-3.08 + +> (806) +(A) low + +> (806) +O=C(O)Cc(c(ccc1Cl)Cl)c1Cl + +$$$$ +2,4,5-T + RDKit 2D + + 14 14 0 0 0 0 0 0 0 0999 V2000 + 3.8916 -4.9570 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8933 -3.7570 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.9336 -3.1588 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5951 -3.0039 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5973 -1.5031 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 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0 0 0 0 0 0 0999 V2000 + -1.8591 2.6716 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.2225 1.5280 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.6810 1.1807 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.1672 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.1485 -1.3659 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6669 -1.0186 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4399 -1.9215 0.0000 S 0 0 0 0 0 0 0 0 0 0 0 0 + 0.7408 -1.0186 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.2688 -1.0186 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 2.7318 0.3704 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 1.5048 1.2733 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.3010 0.3704 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2039 0.3704 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 3 4 1 0 + 4 5 2 0 + 5 6 1 0 + 6 7 1 0 + 7 8 1 0 + 8 9 2 0 + 9 10 1 0 + 10 11 2 0 + 11 12 1 0 + 12 8 1 0 + 12 13 1 0 + 13 2 1 0 + 13 6 2 0 +M END +> (838) +1060 + +> (838) +Tricyclazole + +> (838) +-2.07 + +> (838) +(B) medium + +> (838) +Cc1cccc2sc3nncn3c12 + +$$$$ +Dichlorprop + RDKit 2D + + 14 14 0 0 0 0 0 0 0 0999 V2000 + 3.8916 -4.9570 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8933 -3.7570 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.9336 -3.1588 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5951 -3.0039 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5973 -1.5031 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3383 1.3500 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 2.3383 1.3500 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.5548 -3.6021 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 2 4 1 0 + 4 5 1 0 + 5 6 1 0 + 6 7 2 0 + 7 8 1 0 + 8 9 2 0 + 9 10 1 0 + 9 11 1 0 + 7 12 1 0 + 6 13 1 0 + 13 10 2 0 + 4 14 1 0 +M END +> (839) +1061 + +> (839) +Dichlorprop + +> (839) +-2.45 + +> (839) +(B) medium + +> (839) +O=C(O)C(Oc(c(cc(c1)Cl)Cl)c1)C + +$$$$ +Atropic_Acid + RDKit 2D + + 11 11 0 0 0 0 0 0 0 0999 V2000 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0031 -3.0008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0351 -3.6026 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3039 -3.7494 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3064 -4.9494 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.3421 -3.1476 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 1 1 0 + 6 7 1 0 + 7 8 2 0 + 7 9 1 0 + 9 10 2 0 + 9 11 1 0 +M END +> (840) +1062 + +> (840) +Atropic_Acid + +> (840) +-2.06 + +> (840) +(B) medium + +> (840) +c1ccccc1C(=C)C(=O)O + +$$$$ +(4-Chloro-2-methylphenoxy)acetic_Acid + RDKit 2D + + 13 13 0 0 0 0 0 0 0 0999 V2000 + 3.8916 -4.9570 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8933 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0 0 0 0 0 0 0 0 0 0 0 0 + 2.5973 -1.5031 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5951 -3.0039 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.5548 -3.6021 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.6331 -3.6060 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 1 0 + 4 5 1 0 + 4 6 2 0 + 3 7 1 0 + 7 8 2 0 + 8 9 1 0 + 9 10 2 0 + 10 11 1 0 + 11 12 2 0 + 12 7 1 0 +M END +> (844) +1067 + +> (844) +dl-Tropic_Acid + +> (844) +-0.93 + +> (844) +(C) high + +> (844) +OCC(C(O)=O)c1ccccc1 + +$$$$ +p-Aminopropiophenone + RDKit 2D + + 11 11 0 0 0 0 0 0 0 0999 V2000 + 3.6375 -0.9049 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5973 -1.5031 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 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0 0 0 0 0 0 0 0 0 0 0 0 + 1.5819 2.1379 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.6984 1.6979 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.4051 3.3248 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -0.7689 1.8776 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.2889 1.3322 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 1 0 + 2 5 1 0 + 5 6 1 0 + 6 7 1 0 + 6 8 1 0 + 8 9 1 0 + 9 10 2 0 + 9 11 1 0 + 8 12 1 0 + 12 3 1 0 + 12 13 1 0 + 13 1 1 0 +M END +> (850) +1075 + +> (850) +Ecgonine + +> (850) +-0.02 + +> (850) +(C) high + +> (850) +C1C(N2C)CC(O)C(C(=O)O)C2C1 + +$$$$ +Azelaic_Acid + RDKit 2D + + 13 12 0 0 0 0 0 0 0 0999 V2000 + 0.2606 0.1503 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3000 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3000 1.9500 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.6000 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.9000 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.2000 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.5000 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 7.7999 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 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0 0 0 0 0 0 0 0 0 0 + 11.6999 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 12.7393 0.1503 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 11.6999 1.9500 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 1 0 + 4 5 1 0 + 5 6 1 0 + 6 7 1 0 + 7 8 1 0 + 8 9 1 0 + 9 10 1 0 + 10 11 2 0 + 10 12 1 0 +M END +> (853) +1079 + +> (853) +n-Octyl_Carbamate + +> (853) +-3.3 + +> (853) +(A) low + +> (853) +CCCCCCCCOC(=O)N + +$$$$ +Chlordene + RDKit 2D + + 16 18 0 0 0 0 0 0 0 0999 V2000 + -2.6522 0.1786 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -1.4900 -0.1200 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.0200 -0.9900 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.9501 -1.7482 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -0.2800 -0.3300 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.9384 -1.3333 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -0.1100 1.9100 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.7769 2.7184 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2361 2.3246 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 1.1100 -0.9900 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.9300 -1.8500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.0300 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.8900 -0.3900 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.6000 -0.2800 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.1700 0.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8859 1.4631 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 3 + 3 4 1 0 + 3 5 1 0 + 5 6 1 0 + 5 7 1 0 + 7 8 1 0 + 7 9 1 0 + 5 10 1 0 + 10 11 1 0 + 11 12 1 0 + 12 13 2 3 + 13 14 1 0 + 14 10 1 0 + 14 15 1 0 + 15 2 1 0 + 15 7 1 0 + 15 16 1 0 +M END +> (854) +1080 + +> (854) +Chlordene + +> (854) +-5.64 + +> (854) +(A) low + +> (854) +ClC1=C(Cl)C(Cl)(C2(Cl)Cl)C3CC=CC3C12Cl + +$$$$ +1-Hydroxychlordene + RDKit 2D + + 17 19 0 0 0 0 0 0 0 0999 V2000 + 1.0011 -2.8494 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.6500 -1.8400 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.8100 -1.6100 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.8100 -0.4300 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7100 -0.1900 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3000 -0.9000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.3200 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0900 -0.9204 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -2.2900 -0.8000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.8000 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.9233 0.4222 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 0.1000 0.7200 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.7968 1.6969 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -0.1500 2.2900 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2382 2.7958 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8084 3.0121 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6915 -1.9308 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 2 3 + 4 5 1 0 + 5 6 1 0 + 6 2 1 0 + 6 7 1 0 + 7 8 1 0 + 7 9 1 0 + 9 10 2 3 + 10 11 1 0 + 10 12 1 0 + 12 5 1 0 + 12 13 1 0 + 12 14 1 0 + 14 7 1 0 + 14 15 1 0 + 14 16 1 0 + 9 17 1 0 +M END +> (855) +1081 + +> (855) +1-Hydroxychlordene + +> (855) +-5.46 + +> (855) +(A) low + +> (855) +OC1C=CC2C1C3(Cl)C(=C(Cl)C2(Cl)C3(Cl)Cl)Cl + +$$$$ +Brompyrazone + RDKit 2D + + 15 16 0 0 0 0 0 0 0 0999 V2000 + 2.3383 -1.3500 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.3383 1.3500 0.0000 Br 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 2.7000 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -3.0008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2978 -3.7529 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2955 -5.2529 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0048 -6.0009 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3026 -5.2488 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3002 -3.7488 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 3 5 2 3 + 5 6 1 0 + 5 7 1 0 + 7 8 2 3 + 8 9 1 0 + 9 2 1 0 + 9 10 1 0 + 10 11 2 0 + 11 12 1 0 + 12 13 2 0 + 13 14 1 0 + 14 15 2 0 + 15 10 1 0 +M END +> (856) +1082 + +> (856) +Brompyrazone + +> (856) +-3.12 + +> (856) +(A) low + +> (856) +O=C1C(Br)=C(N)C=NN1c2ccccc2 + +$$$$ +Captafol + RDKit 2D + + 18 19 0 0 0 0 0 0 0 0999 V2000 + 6.9616 -2.2794 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 6.3513 -1.2462 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.9413 -0.2013 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 4.8506 -1.2602 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.6506 -1.2719 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 4.2606 -2.3052 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 4.0872 0.0320 0.0000 S 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5889 0.0182 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7138 1.2033 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0825 2.3453 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2917 0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0028 1.5132 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3155 0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3155 -0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0028 -1.5132 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2917 -0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7138 -1.2033 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0907 -2.3426 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 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0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 1 0 + 5 7 2 0 + 7 8 1 0 + 8 9 1 0 + 8 10 2 0 + 10 11 1 0 + 11 4 1 0 + 11 12 2 0 + 12 1 1 0 +M END +> (858) +1085 + +> (858) +4-Hydroxy-2-methylquinoline + +> (858) +-1.2 + +> (858) +(B) medium + +> (858) +c1ccc2c(O)cc(C)nc2c1 + +$$$$ +Chlorfenprop-methyl + RDKit 2D + + 14 14 0 0 0 0 0 0 0 0999 V2000 + 4.9292 -5.8600 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8912 -5.2578 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8933 -3.7570 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.9336 -3.1588 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5951 -3.0039 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.5548 -3.6021 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5973 -1.5031 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3383 1.3500 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 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-0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7138 -1.2033 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5889 0.0182 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7138 1.2033 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0825 2.3453 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2917 0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0028 1.5132 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.9971 3.0138 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -2.0337 3.6184 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 7 1 0 + 7 8 1 0 + 8 9 1 0 + 9 10 2 0 + 9 11 1 0 + 11 6 1 0 + 11 12 2 0 + 12 3 1 0 + 12 13 1 0 + 13 14 1 0 +M END +> (862) +1090 + +> (862) +Meconin + +> (862) +-1.89 + +> (862) +(B) medium + +> (862) +COc1ccc2COC(=O)c2c1OC + +$$$$ +Opianic_Acid + RDKit 2D + + 15 15 0 0 0 0 0 0 0 0999 V2000 + 3.6375 -0.9049 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5973 -1.5031 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5956 -2.7031 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5972 1.5031 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5955 2.7031 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6003 -1.4978 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -3.6387 -0.8963 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0031 -3.0008 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.0432 -3.5993 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 2 4 1 0 + 4 5 2 0 + 5 6 1 0 + 6 7 2 0 + 5 8 1 0 + 8 9 2 0 + 9 10 1 0 + 10 11 1 0 + 11 12 1 0 + 10 13 2 0 + 13 4 1 0 + 13 14 1 0 + 14 15 1 0 +M END +> (863) +1091 + +> (863) +Opianic_Acid + +> (863) +-1.92 + +> (863) +(B) medium + +> (863) +OC(=O)c1c(C=O)ccc(OC)c1OC + +$$$$ +Mecoprop + RDKit 2D + + 14 14 0 0 0 0 0 0 0 0999 V2000 + 3.8916 -4.9570 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8933 -3.7570 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.9336 -3.1588 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5951 -3.0039 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5973 -1.5031 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3383 1.3500 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 2.3383 1.3500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.5548 -3.6021 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 2 4 1 0 + 4 5 1 0 + 5 6 1 0 + 6 7 2 0 + 7 8 1 0 + 8 9 2 0 + 9 10 1 0 + 9 11 1 0 + 7 12 1 0 + 6 13 1 0 + 13 10 2 0 + 4 14 1 0 +M END +> (864) +1092 + +> (864) +Mecoprop + +> (864) +-2.55 + +> (864) +(B) medium + +> (864) +O=C(O)C(Oc(c(cc(c1)Cl)C)c1)C + +$$$$ +Tranid + RDKit 2D + + 16 17 0 0 0 0 0 0 0 0999 V2000 + 8.6528 1.1753 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 7.4997 1.5074 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 6.4182 0.4668 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.7068 -0.6980 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 4.9760 0.8821 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8946 -0.1585 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 2.4505 0.2546 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.1616 -0.4455 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7822 0.9706 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 0.2546 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.3478 -0.4177 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.4213 -0.9542 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.7663 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.1616 2.5141 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.4505 1.7663 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.6009 2.1076 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 2 0 + 3 5 1 0 + 5 6 1 0 + 6 7 2 3 + 7 8 1 0 + 8 9 1 0 + 8 10 1 0 + 10 11 1 0 + 11 12 3 0 + 10 13 1 0 + 13 14 1 0 + 14 9 1 0 + 14 15 1 0 + 15 7 1 0 + 15 16 1 0 +M END +> (865) +1094 + +> (865) +Tranid + +> (865) +-2.08 + +> (865) +(B) medium + +> (865) +CNC(=O)ON=C1C(C2)C(C#N)CC2C1Cl + +$$$$ +Quinethazone + RDKit 2D + + 18 19 0 0 0 0 0 0 0 0999 V2000 + -3.9072 2.7019 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.9091 1.5019 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6111 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6111 -0.7486 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2964 -1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2928 -2.6973 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2964 -1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5929 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5929 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.6321 1.3486 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2964 1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2964 1.4973 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8926 -1.4991 0.0000 S 0 0 0 0 0 0 0 0 0 0 0 0 + 4.9317 -0.8987 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8934 -2.6991 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 4.9322 -2.0986 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 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0 0 0 0 + -3.9091 1.5019 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.9072 2.7019 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.9494 0.9039 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 4 5 2 0 + 4 6 2 0 + 4 7 1 0 + 7 8 1 0 + 8 9 1 0 + 9 10 2 0 + 10 11 1 0 + 11 12 2 0 + 3 13 1 0 + 13 14 1 0 + 13 15 1 0 + 2 16 1 0 + 16 8 2 0 + 16 12 1 0 +M END +> (867) +1096 + +> (867) +Bentazon + +> (867) +-2.68 + +> (867) +(B) medium + +> (867) +O=C(N(S(=O)(=O)Nc1cccc2)C(C)C)c12 + +$$$$ +Inosine + RDKit 2D + + 19 21 0 0 0 0 0 0 0 0999 V2000 + 6.1003 -4.6309 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 5.0501 -5.2115 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.7662 -4.4393 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.6393 -2.9446 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.1852 -2.6281 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.4028 -3.8873 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2065 -3.9814 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.3840 -5.0218 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.1111 -6.1904 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7138 -1.2033 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5889 0.0182 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7138 1.2033 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2917 0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0028 1.5132 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.9991 2.7132 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3155 0.7475 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3155 -0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0028 -1.5132 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2917 -0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 1 0 + 4 5 1 0 + 5 6 1 0 + 6 7 1 0 + 6 8 1 0 + 8 3 1 0 + 8 9 1 0 + 5 10 1 0 + 10 11 1 0 + 11 12 2 0 + 12 13 1 0 + 13 14 1 0 + 14 15 2 0 + 14 16 1 0 + 16 17 1 0 + 17 18 2 3 + 18 19 1 0 + 19 10 1 0 + 19 13 2 0 +M END +> (868) +1097 + +> (868) +Inosine + +> (868) +-1.23 + +> (868) +(B) medium + +> (868) +OCC1OC(C(O)C1O)n2cnc3C(=O)NC=Nc23 + +$$$$ +Anethole + RDKit 2D + + 11 11 0 0 0 0 0 0 0 0999 V2000 + 2.5973 -1.5031 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6003 1.4977 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8990 0.7455 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.9395 1.3433 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5956 -2.7031 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 3 4 1 0 + 4 5 2 0 + 5 6 1 0 + 5 7 1 0 + 7 8 2 3 + 8 9 1 0 + 2 10 1 0 + 10 6 2 0 + 1 11 1 0 +M END +> (869) +1099 + +> (869) +Anethole + +> (869) +-3.13 + +> (869) +(A) low + +> (869) +O(c(ccc(c1)C=CC)c1)C + +$$$$ +Chlorpropamide + RDKit 2D + + 17 17 0 0 0 0 0 0 0 0999 V2000 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 2.7000 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -3.0008 0.0000 S 0 0 0 0 0 0 0 0 0 0 0 0 + 1.0394 -3.6005 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0006 -4.2008 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2993 -3.7521 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2993 -5.2529 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.2598 -5.8526 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5985 -6.0041 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5985 -7.5050 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8978 -8.2562 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8978 -9.4562 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 3 5 2 0 + 5 6 1 0 + 6 7 2 0 + 7 1 1 0 + 7 8 1 0 + 8 9 2 0 + 8 10 2 0 + 8 11 1 0 + 11 12 1 0 + 12 13 2 0 + 12 14 1 0 + 14 15 1 0 + 15 16 1 0 + 16 17 1 0 +M END +> (870) +1100 + +> (870) +Chlorpropamide + +> (870) +-3.03 + +> (870) +(A) low + +> (870) +c1cc(Cl)ccc1S(=O)(=O)NC(=O)NCCC + +$$$$ +Triforine + RDKit 2D + + 22 22 0 0 0 0 0 0 0 0999 V2000 + 4.9372 -1.3609 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8999 -0.7576 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.9411 -0.1611 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 3.9040 0.4424 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5973 -1.5031 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5951 -3.0039 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8933 -3.7570 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8916 -4.9570 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6003 1.4977 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8990 0.7455 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -5.2003 1.4932 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -6.2387 0.8917 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6061 2.9986 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.6472 3.5953 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6100 4.1985 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -1.5689 3.6022 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 2 4 1 0 + 2 5 1 0 + 5 6 1 0 + 6 7 1 0 + 7 8 2 0 + 5 9 1 0 + 9 10 1 0 + 10 11 1 0 + 11 12 1 0 + 12 13 1 0 + 13 14 1 0 + 14 9 1 0 + 12 15 1 0 + 15 16 1 0 + 16 17 1 0 + 17 18 2 0 + 15 19 1 0 + 19 20 1 0 + 19 21 1 0 + 19 22 1 0 +M END +> (871) +1101 + +> (871) +Triforine + +> (871) +-4.19 + +> (871) +(A) low + +> (871) +ClC(Cl)(Cl)C(NC=O)N1CCN(CC1)C(NC=O)C(Cl)(Cl)Cl + +$$$$ +Dyphylline + RDKit 2D + + 18 19 0 0 0 0 0 0 0 0999 V2000 + -3.3560 1.3452 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3155 0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3155 -0.7475 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0028 -1.5132 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.9991 -2.7132 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2917 -0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7138 -1.2033 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5889 0.0182 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7138 1.2033 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 2.1855 -2.6254 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.6552 -2.9294 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.4530 -2.0330 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 4.1277 -4.3539 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.3028 -4.5970 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2917 0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.3560 -1.3452 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0028 1.5132 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -0.9991 2.7132 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 4 5 2 0 + 4 6 1 0 + 6 7 1 0 + 7 8 1 0 + 8 9 2 3 + 7 10 1 0 + 10 11 1 0 + 11 12 1 0 + 11 13 1 0 + 13 14 1 0 + 6 15 2 3 + 15 9 1 0 + 3 16 1 0 + 2 17 1 0 + 17 15 1 0 + 17 18 1 0 +M END +> (872) +1102 + +> (872) +Dyphylline + +> (872) +-0.17 + +> (872) +(C) high + +> (872) +O=C(N(C(=O)C(N(C=N1)CC(O)CO)=C12)C)N2C + +$$$$ +2,6-Diethylaniline + RDKit 2D + + 11 11 0 0 0 0 0 0 0 0999 V2000 + 2.3383 -1.3500 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5972 1.5031 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5955 2.7031 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.0031 -3.0008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0432 -3.5994 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 3 4 1 0 + 4 5 2 0 + 5 6 1 0 + 3 7 1 0 + 7 8 1 0 + 2 9 1 0 + 9 6 2 0 + 9 10 1 0 + 10 11 1 0 +M END +> (873) +1104 + +> (873) +2,6-Diethylaniline + +> (873) +-2.35 + +> (873) +(B) medium + +> (873) +Nc(c(ccc1)CC)c1CC + +$$$$ +N-Phenyldiethanolamine + RDKit 2D + + 13 13 0 0 0 0 0 0 0 0999 V2000 + 3.8916 -4.9570 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8933 -3.7570 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5951 -3.0039 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5973 -1.5031 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8999 -0.7576 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1972 -1.5121 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.2387 -0.9161 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 1 0 + 4 5 1 0 + 5 6 2 0 + 6 7 1 0 + 7 8 2 0 + 8 9 1 0 + 5 10 1 0 + 10 9 2 0 + 4 11 1 0 + 11 12 1 0 + 12 13 1 0 +M END +> (874) +1105 + +> (874) +N-Phenyldiethanolamine + +> (874) +-0.73 + +> (874) +(C) high + +> (874) +OCCN(c(cccc1)c1)CCO + +$$$$ +Camphor + RDKit 2D + + 11 12 0 0 0 0 0 0 0 0999 V2000 + 0.5952 1.7693 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4408 1.1638 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.8868 1.9750 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.9045 0.3879 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.8868 -1.3225 0.0000 C 0 0 0 0 0 0 0 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0 0 0 0 0 0999 V2000 + 4.9292 -5.8600 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8912 -5.2578 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8933 -3.7570 0.0000 S 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5951 -3.0039 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.5548 -3.6021 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5973 -1.5031 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8999 -0.7576 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.9372 -1.3609 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 1 0 + 4 5 2 0 + 4 6 1 0 + 6 7 1 0 + 7 8 1 0 + 6 9 1 0 + 9 10 1 0 + 10 11 1 0 + 11 12 1 0 + 12 13 1 0 + 13 14 1 0 + 14 9 1 0 +M END +> (901) +1139 + +> (901) +Cycloate + +> (901) +-3.4 + +> (901) +(A) low + +> (901) +CCSC(=O)N(CC)C1CCCCC1 + +$$$$ +Methyl_Caprate + RDKit 2D + + 13 12 0 0 0 0 0 0 0 0999 V2000 + 2.6000 -1.2000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.6000 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3000 0.7500 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2606 0.1503 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.9000 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.2000 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.5000 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 7.7999 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 9.0999 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 10.3999 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 11.6999 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 12.9999 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 14.0393 0.5997 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 2 5 1 0 + 5 6 1 0 + 6 7 1 0 + 7 8 1 0 + 8 9 1 0 + 9 10 1 0 + 10 11 1 0 + 11 12 1 0 + 12 13 1 0 +M END +> (902) +1140 + +> (902) +Methyl_Caprate + +> (902) +-4.63 + +> (902) +(A) low + +> (902) +O=C(OC)CCCCCCCCC + +$$$$ +Butylate + RDKit 2D + + 14 13 0 0 0 0 0 0 0 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14 13 0 0 0 0 0 0 0 0999 V2000 + 0.2606 0.1503 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3000 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3000 1.9500 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.6000 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.9000 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.2000 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.5000 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 7.7999 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 9.0999 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 10.3999 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 11.6999 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 12.9999 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 14.2999 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 15.3393 0.1503 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 2 4 1 0 + 4 5 1 0 + 5 6 1 0 + 6 7 1 0 + 7 8 1 0 + 8 9 1 0 + 9 10 1 0 + 10 11 1 0 + 11 12 1 0 + 12 13 1 0 + 13 14 1 0 +M END +> (904) +1142 + +> (904) +11-Aminoundecanoic_Acid + +> (904) +-2.7 + +> (904) +(B) medium + +> (904) +O=C(O)CCCCCCCCCCN + +$$$$ 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0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1873 -7.5117 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1894 -6.0109 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.2297 -5.4127 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8912 -5.2578 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.8509 -5.8560 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8933 -3.7570 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5951 -3.0039 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.5548 -3.6021 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5973 -1.5031 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 1 0 + 4 5 2 0 + 4 6 1 0 + 6 7 1 0 + 6 8 1 0 + 8 9 1 0 + 9 10 2 0 + 9 11 1 0 + 11 12 1 0 + 12 13 2 0 + 13 14 1 0 + 14 15 2 0 + 15 16 1 0 + 16 17 2 0 + 17 12 1 0 +M END +> (913) +1154 + +> (913) 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0 0 0 0 0 0 0 0 + 1.5317 -8.6198 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.0464 -9.7171 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.0840 -6.1102 0.0000 S 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 3 5 2 0 + 5 6 1 0 + 6 7 2 0 + 7 1 1 0 + 7 8 1 0 + 8 9 2 0 + 8 10 2 0 + 8 11 1 0 + 11 12 1 0 + 12 13 2 0 + 13 14 1 0 + 14 15 2 0 + 15 16 1 0 + 16 17 1 0 + 16 18 1 0 + 16 19 1 0 + 15 20 1 0 + 20 12 1 0 +M END +> (914) +1155 + +> (914) +Glybuthiazole + +> (914) +-3.74 + +> (914) +(A) low + +> (914) +c1cc(N)ccc1S(=O)(=O)Nc2nnc(C(C)(C)C)s2 + +$$$$ +Isoamyl_Salicylate + RDKit 2D + + 15 15 0 0 0 0 0 0 0 0999 V2000 + 3.6375 -0.9049 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5973 -1.5031 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5951 -3.0039 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8933 -3.7570 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8912 -5.2578 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1894 -6.0109 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1877 -7.2109 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.2297 -5.4127 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.3383 1.3500 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 4 5 1 0 + 5 6 1 0 + 6 7 1 0 + 6 8 1 0 + 2 9 1 0 + 9 10 2 0 + 10 11 1 0 + 10 12 1 0 + 12 13 2 0 + 13 14 1 0 + 9 15 1 0 + 15 14 2 0 +M END +> (915) +1156 + +> (915) +Isoamyl_Salicylate + +> (915) +-3.16 + +> (915) +(A) low + +> (915) +O=C(OCCC(C)C)c(c(O)ccc1)c1 + +$$$$ +Tolbutamide + RDKit 2D + + 18 18 0 0 0 0 0 0 0 0999 V2000 + 5.1984 -2.7012 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1988 -1.5012 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.4990 -0.7516 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 7.7988 -1.5020 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 9.0990 -0.7525 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 10.3987 -1.5029 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 11.4383 -0.9035 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8990 -0.7508 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5988 -1.5004 0.0000 S 0 0 0 0 0 0 0 0 0 0 0 0 + 3.6377 -2.1009 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5984 -2.7004 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3383 1.3500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 4 5 1 0 + 5 6 1 0 + 6 7 1 0 + 2 8 1 0 + 8 9 1 0 + 9 10 2 0 + 9 11 2 0 + 9 12 1 0 + 12 13 2 0 + 13 14 1 0 + 14 15 2 0 + 15 16 1 0 + 15 17 1 0 + 12 18 1 0 + 18 16 2 0 +M END +> (916) +1157 + +> (916) +Tolbutamide + +> (916) +-3.39 + +> (916) +(A) low + +> (916) +O=C(NCCCC)NS(=O)(=O)c(ccc(c1)C)c1 + +$$$$ +Tributylamine + RDKit 2D + + 13 12 0 0 0 0 0 0 0 0999 V2000 + 5.2000 0.0000 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 3.9000 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.6000 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3000 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2606 0.1503 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.5000 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 7.7999 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 9.0999 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 10.1394 0.1503 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.2030 -1.5008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.5039 -2.2494 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.5070 -3.7502 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 7.5470 -4.3487 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 1 0 + 4 5 1 0 + 1 6 1 0 + 6 7 1 0 + 7 8 1 0 + 8 9 1 0 + 1 10 1 0 + 10 11 1 0 + 11 12 1 0 + 12 13 1 0 +M END +> (917) +1159 + +> (917) +Tributylamine + +> (917) +-3.12 + +> (917) +(A) low + +> (917) +N(CCCC)(CCCC)CCCC + +$$$$ +Niclosamide + RDKit 2D + + 21 22 0 0 0 0 0 0 0 0999 V2000 + 2.3383 -1.3500 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.0031 3.0008 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0432 3.5993 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.0351 3.6026 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.0031 -3.0008 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -1.3039 -3.7494 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3421 -3.1476 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -1.3070 -5.2502 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.0079 -6.0003 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.0080 -7.5003 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.0312 -8.1004 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + -1.3070 -8.2503 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6060 -7.5003 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6060 -6.0003 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.6452 -5.4003 0.0000 O 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-1.3039 -3.7494 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.3092 -5.2494 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6108 -5.9949 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6160 -7.4957 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.5792 -8.1000 0.0000 F 0 0 0 0 0 0 0 0 0 0 0 0 + -3.6575 -8.0918 0.0000 F 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6207 -8.6957 0.0000 F 0 0 0 0 0 0 0 0 0 0 0 0 + -3.9072 -5.2404 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.9020 -3.7404 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6004 -2.9949 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 2 4 1 0 + 4 5 2 0 + 5 6 1 0 + 6 7 2 0 + 7 8 1 0 + 8 9 2 0 + 9 4 1 0 + 9 10 1 0 + 10 11 1 0 + 11 12 2 0 + 12 13 1 0 + 13 14 1 0 + 14 15 1 0 + 14 16 1 0 + 14 17 1 0 + 13 18 2 0 + 18 19 1 0 + 19 20 2 0 + 20 11 1 0 +M END +> (919) +1161 + +> (919) +Niflumic_Acid + +> (919) +-4.17 + +> (919) +(A) low + +> (919) +OC(=O)c1cccnc1Nc2cc(C(F)(F)F)ccc2 + +$$$$ +Phenanthridine + RDKit 2D + + 14 16 0 0 0 0 0 0 0 0999 V2000 + -3.4277 -1.5498 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.4277 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.1332 0.6928 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.8205 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.8205 -1.5498 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.1332 -2.2973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.4558 -2.2973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7503 -1.5498 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7503 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.0631 0.6928 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.0631 2.1879 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7503 2.9537 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.4558 2.1879 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.4558 0.6928 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 1 1 0 + 5 7 1 0 + 7 8 2 0 + 8 9 1 0 + 9 10 2 0 + 10 11 1 0 + 11 12 2 0 + 12 13 1 0 + 13 14 2 0 + 14 4 1 0 + 14 9 1 0 +M END +> (920) +1162 + +> (920) +Phenanthridine + +> (920) +-2.78 + +> (920) +(B) medium + +> (920) +c1ccc2c(c1)cnc3ccccc23 + +$$$$ +Carbanilide + RDKit 2D + + 16 17 0 0 0 0 0 0 0 0999 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0 + 4 5 1 0 + 5 6 1 0 + 6 7 1 0 + 7 2 1 0 + 3 8 1 0 + 8 9 2 0 + 8 10 1 0 + 10 11 1 0 + 11 12 2 0 + 12 13 1 0 + 13 14 2 0 + 14 15 1 0 + 15 16 2 0 + 16 11 1 0 +M END +> (922) +1165 + +> (922) +Pyracarbolid + +> (922) +-2.56 + +> (922) +(B) medium + +> (922) +CC1=C(CCCO1)C(=O)Nc2ccccc2 + +$$$$ +Pyrolan + RDKit 2D + + 18 19 0 0 0 0 0 0 0 0999 V2000 + 7.1275 2.9011 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.9880 2.5249 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 5.0928 3.3240 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.6819 1.0557 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.5772 0.2566 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 4.2567 0.5852 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.9511 -0.8815 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.9531 -1.9978 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.2010 -3.2956 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.6873 -4.3927 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.7343 -2.9815 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5987 -1.5004 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 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0.0000 -3.0008 0.0000 S 0 0 0 0 0 0 0 0 0 0 0 0 + 1.0394 -3.6005 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0006 -4.2008 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2993 -3.7521 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6008 -3.0047 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.6390 -3.6066 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6033 -1.8047 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2993 -5.2529 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.0839 -6.1102 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -0.5445 -7.5378 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -2.0445 -7.5408 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.7479 -8.5131 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5110 -6.1152 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.6508 -5.7400 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 3 5 2 0 + 5 6 1 0 + 6 7 2 0 + 7 1 1 0 + 7 8 1 0 + 8 9 2 0 + 8 10 2 0 + 8 11 1 0 + 11 12 1 0 + 12 13 2 0 + 12 14 1 0 + 11 15 1 0 + 15 16 1 0 + 16 17 1 0 + 17 18 2 0 + 18 19 1 0 + 18 20 1 0 + 20 15 2 0 + 20 21 1 0 +M END +> (924) +1167 + +> (924) 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0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -3.0008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0388 -3.6015 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.0394 -3.6005 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0006 -4.2008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 2 4 1 0 + 4 5 1 0 + 5 6 2 0 + 6 7 1 0 + 7 8 1 0 + 7 9 2 0 + 6 10 1 0 + 10 11 1 0 + 10 12 2 0 + 12 13 1 0 + 13 14 1 0 + 13 15 2 0 + 12 16 1 0 + 16 17 2 0 + 17 5 1 0 + 17 18 1 0 + 18 19 1 0 + 18 20 1 0 + 18 21 1 0 +M CHG 4 7 1 8 -1 13 1 14 -1 +M END +> (925) +1169 + +> (925) +Medinoterb_Acetate + +> (925) +-4.47 + +> (925) +(A) low + +> (925) +CC(=O)Oc1c(N(=O)(=O))c(C)c(N(=O)(=O))cc1C(C)(C)C + +$$$$ +Ethofumesate + RDKit 2D + + 19 20 0 0 0 0 0 0 0 0999 V2000 + 6.0207 1.3637 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.8208 1.3475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.0872 0.0382 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5889 0.0182 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7138 1.2033 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2917 0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0028 1.5132 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3155 0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3155 -0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.6187 -1.4919 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -3.6267 -2.9927 0.0000 S 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5905 -3.5979 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.6688 -3.5878 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -3.6326 -4.1927 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0028 -1.5132 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2917 -0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7138 -1.2033 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.9024 -2.0874 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.8923 -1.4292 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 1 0 + 4 5 1 0 + 5 6 1 0 + 6 7 2 0 + 7 8 1 0 + 8 9 2 0 + 9 10 1 0 + 10 11 1 0 + 11 12 1 0 + 11 13 2 0 + 11 14 2 0 + 9 15 1 0 + 15 16 2 0 + 16 6 1 0 + 16 17 1 0 + 17 4 1 0 + 17 18 1 0 + 17 19 1 0 +M END +> (926) +1170 + +> (926) +Ethofumesate + +> (926) +-3.42 + +> (926) +(A) low + +> (926) 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0 + 10 11 1 0 + 11 12 1 0 + 11 13 1 0 + 13 14 2 0 + 13 15 1 0 + 15 16 1 0 +M END +> (927) +1171 + +> (927) +Ibuproxam + +> (927) +-3.04 + +> (927) +(A) low + +> (927) +c1cc(CC(C)C)ccc1C(C)C(=O)NO + +$$$$ +Salbutamol + RDKit 2D + + 17 17 0 0 0 0 0 0 0 0999 V2000 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.6003 1.4978 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.6387 0.8963 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 2.7000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0031 -3.0008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0351 -3.6026 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3039 -3.7494 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3070 -5.2502 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 2.6078 -5.9988 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.6103 -7.1988 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.6482 -6.5968 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.6460 -5.3970 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 2 5 1 0 + 5 6 1 0 + 5 7 2 0 + 7 8 1 0 + 8 9 2 0 + 9 1 1 0 + 9 10 1 0 + 10 11 1 0 + 10 12 1 0 + 12 13 1 0 + 13 14 1 0 + 14 15 1 0 + 14 16 1 0 + 14 17 1 0 +M END +> (928) +1172 + +> (928) +Salbutamol + +> (928) +-1.22 + +> (928) +(B) medium + +> (928) +c1c(CO)c(O)ccc1C(O)CNC(C)(C)C + +$$$$ +Methyl_Laurate + RDKit 2D + + 15 14 0 0 0 0 0 0 0 0999 V2000 + 2.6000 -1.2000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.6000 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3000 0.7500 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2606 0.1503 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.9000 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.2000 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.5000 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 7.7999 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 9.0999 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 10.3999 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 11.6999 0.7500 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0 0 0 0 0 + -3.6575 -8.0918 0.0000 F 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6207 -8.6957 0.0000 F 0 0 0 0 0 0 0 0 0 0 0 0 + -3.9072 -5.2404 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.9020 -3.7404 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6004 -2.9949 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 2 4 1 0 + 4 5 2 0 + 5 6 1 0 + 6 7 2 0 + 7 8 1 0 + 8 9 2 0 + 9 4 1 0 + 9 10 1 0 + 10 11 1 0 + 11 12 2 0 + 12 13 1 0 + 13 14 1 0 + 14 15 1 0 + 14 16 1 0 + 14 17 1 0 + 13 18 2 0 + 18 19 1 0 + 19 20 2 0 + 20 11 1 0 +M END +> (935) +1181 + +> (935) +Flufenamic_Acid + +> (935) +-4.36 + +> (935) +(A) low + +> (935) +OC(=O)c1ccccc1Nc2cc(C(F)(F)F)ccc2 + +$$$$ +1-Anthranol + RDKit 2D + + 15 17 0 0 0 0 0 0 0 0999 V2000 + -3.8968 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8968 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5978 -1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2989 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2806 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5978 -1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8968 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8968 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5978 1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2806 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0037 2.7002 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2989 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5978 1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 7 1 0 + 7 8 2 0 + 8 9 1 0 + 9 10 2 0 + 10 11 1 0 + 11 6 1 0 + 11 12 2 0 + 12 13 1 0 + 12 14 1 0 + 14 4 1 0 + 14 15 2 0 + 15 1 1 0 +M END +> (936) +1182 + +> (936) +1-Anthranol + +> (936) +-4.73 + +> (936) +(A) low + +> (936) +c1ccc2cc3ccccc3c(O)c2c1 + +$$$$ +Gentisin + RDKit 2D + + 19 21 0 0 0 0 0 0 0 0999 V2000 + -4.9360 1.3500 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8968 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8968 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5978 -1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2989 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5002 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2806 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5978 -1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8968 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1981 -1.4978 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 6.2364 -0.8963 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8968 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5978 1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.6015 2.7002 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2806 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0037 2.7002 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2989 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5978 1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 3 4 1 0 + 4 5 2 0 + 5 6 1 0 + 6 7 1 0 + 7 8 2 0 + 8 9 1 0 + 9 10 1 0 + 10 11 1 0 + 9 12 2 0 + 12 13 1 0 + 13 14 1 0 + 13 15 2 0 + 15 7 1 0 + 15 16 1 0 + 16 17 2 0 + 16 18 1 0 + 18 5 1 0 + 18 19 2 0 + 19 2 1 0 +M END +> (937) +1184 + +> (937) +Gentisin + +> (937) +-2.93 + +> (937) +(B) medium + +> (937) +Oc1ccc2Oc3cc(OC)cc(O)c3C(=O)c2c1 + +$$$$ +Dibenzamid + RDKit 2D + + 17 18 0 0 0 0 0 0 0 0999 V2000 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0031 -3.0008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0351 -3.6026 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3039 -3.7494 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3070 -5.2502 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2688 -5.8520 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.6078 -5.9988 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.6131 -7.4988 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.9147 -8.2443 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.2112 -7.4898 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.2060 -5.9898 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.9044 -5.2443 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 1 1 0 + 6 7 1 0 + 7 8 2 0 + 7 9 1 0 + 9 10 1 0 + 10 11 2 0 + 10 12 1 0 + 12 13 2 0 + 13 14 1 0 + 14 15 2 0 + 15 16 1 0 + 16 17 2 0 + 17 12 1 0 +M END +> (938) +1185 + +> (938) +Dibenzamid + +> (938) +-2.27 + +> (938) +(B) medium + +> (938) +c1ccccc1C(=O)NC(=O)c2ccccc2 + +$$$$ +Perfluidone + RDKit 2D + + 24 25 0 0 0 0 0 0 0 0999 V2000 + 2.3383 -1.3500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.0031 -3.0008 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -1.3039 -3.7494 0.0000 S 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3421 -3.1476 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -1.3020 -2.5494 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -1.3070 -5.2502 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3471 -5.8487 0.0000 F 0 0 0 0 0 0 0 0 0 0 0 0 + -0.2688 -5.8520 0.0000 F 0 0 0 0 0 0 0 0 0 0 0 0 + -1.3089 -6.4502 0.0000 F 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 3.0008 0.0000 S 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0394 3.6005 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0391 2.4007 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2993 3.7521 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5988 3.0029 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8973 3.7537 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8964 5.2537 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5969 6.0029 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2983 5.2521 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 3 4 1 0 + 4 5 2 0 + 5 6 1 0 + 6 7 2 0 + 7 2 1 0 + 7 8 1 0 + 8 9 1 0 + 9 10 2 0 + 9 11 2 0 + 9 12 1 0 + 12 13 1 0 + 12 14 1 0 + 12 15 1 0 + 4 16 1 0 + 16 17 2 0 + 16 18 2 0 + 16 19 1 0 + 19 20 2 0 + 20 21 1 0 + 21 22 2 0 + 22 23 1 0 + 23 24 2 0 + 24 19 1 0 +M END +> (939) +1186 + +> (939) +Perfluidone + +> (939) +-3.8 + +> (939) +(A) low + 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1 2 1 0 + 2 3 2 0 + 3 4 1 0 + 3 5 1 0 + 5 6 2 0 + 6 7 1 0 + 6 8 1 0 + 2 9 1 0 + 9 7 2 0 + 1 10 2 3 + 10 4 1 0 + 10 11 1 0 + 11 12 2 0 + 12 13 1 0 + 13 14 2 0 + 14 15 1 0 + 14 16 1 0 + 11 17 1 0 + 17 16 2 0 +M END +> (940) +1187 + +> (940) +2-(4-Aminophenyl)-6-methyl-benzothiazole + +> (940) +-3.68 + +> (940) +(A) low + +> (940) +N(c(c(S1)cc(c2)C)c2)=C1c(ccc(N)c3)c3 + +$$$$ +Khellin + RDKit 2D + + 19 21 0 0 0 0 0 0 0 0999 V2000 + 5.6440 -1.2674 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.6043 -0.6682 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.6043 0.8382 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.3044 1.5915 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.3068 2.7915 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.9924 0.8382 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.7168 1.5915 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.7255 3.0922 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -0.3098 3.6990 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.5831 0.8382 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.0045 1.2999 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.8792 0.0850 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.0045 -1.1298 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -0.5831 -0.6682 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.7168 -1.4214 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.7316 -2.9221 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7765 -3.5123 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.9924 -0.6682 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.3044 -1.4214 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 3 + 3 4 1 0 + 4 5 2 0 + 4 6 1 0 + 6 7 2 0 + 7 8 1 0 + 8 9 1 0 + 7 10 1 0 + 10 11 1 0 + 11 12 2 0 + 12 13 1 0 + 13 14 1 0 + 14 10 2 0 + 14 15 1 0 + 15 16 1 0 + 16 17 1 0 + 15 18 2 0 + 18 6 1 0 + 18 19 1 0 + 19 2 1 0 +M END +> (941) +1189 + +> (941) +Khellin + +> (941) +-2.4 + +> (941) +(B) medium + +> (941) +CC1=CC(=O)c2c(OC)c3ccoc3c(OC)c2O1 + +$$$$ +4,7-Dimethyl-1,10-phenanthroline + RDKit 2D + + 16 18 0 0 0 0 0 0 0 0999 V2000 + -2.1332 0.6928 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -0.8205 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.8205 -1.5498 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.1332 -2.2973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.4277 -1.5498 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.1368 -3.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.4558 -2.2973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7503 -1.5498 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7503 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.0631 0.6928 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.0631 2.1879 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7503 2.9537 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.4558 2.1879 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 4.0907 0.0732 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.4558 0.6928 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.4277 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 4 6 1 0 + 3 7 1 0 + 7 8 2 0 + 8 9 1 0 + 9 10 1 0 + 10 11 2 0 + 11 12 1 0 + 12 13 2 0 + 10 14 1 0 + 2 15 1 0 + 15 9 2 0 + 15 13 1 0 + 1 16 1 0 + 16 5 2 0 +M END +> (942) +1190 + +> (942) +4,7-Dimethyl-1,10-phenanthroline + +> (942) +-3.97 + +> (942) +(A) low + +> (942) +n(c(c(c(c1)C)ccc2c(ccn3)C)c23)c1 + +$$$$ +DL-1,2-Diphenylethanol + 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0 0 + -1.0028 -1.5132 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2917 -0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7138 -1.2033 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0907 -2.3426 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5889 0.0182 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 4.0872 0.0382 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.8573 -1.2500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.0571 -1.2325 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 4.8208 1.3475 0.0000 S 0 0 0 0 0 0 0 0 0 0 0 0 + 6.3215 1.3677 0.0000 P 0 0 0 0 0 0 0 0 0 0 0 0 + 5.7079 2.3990 0.0000 S 0 0 0 0 0 0 0 0 0 0 0 0 + 7.0552 2.6770 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 8.5559 2.6972 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 9.1425 3.7441 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 7.0889 0.0780 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 8.5896 0.0968 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 9.2033 -0.9344 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7138 1.2033 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0825 2.3453 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2917 0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 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0 0 0 0 0 + 0.9933 -0.3863 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.6400 2.4100 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.2100 3.5700 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.6200 4.0100 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.2100 5.4500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.0900 3.1500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.4700 1.6000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.2100 2.4900 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.8600 1.9800 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 4 5 2 0 + 4 6 1 0 + 6 7 1 0 + 7 8 2 0 + 7 9 1 0 + 9 2 1 0 + 9 10 1 0 + 10 11 1 0 + 9 12 1 0 + 12 13 2 3 + 13 14 1 0 + 14 15 1 0 + 14 16 1 0 + 16 17 1 0 + 17 18 1 0 + 18 15 1 0 + 18 19 1 0 + 19 12 1 0 +M END +> (947) +1196 + +> (947) +Reposal + +> (947) +-2.64 + +> (947) +(B) medium + +> (947) +O=C1NC(=O)NC(=O)C1(CC)C2=CC(C3)CCC3C2 + +$$$$ +Anisomycin + RDKit 2D + + 19 20 0 0 0 0 0 0 0 0999 V2000 + 2.5956 -2.7031 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5973 -1.5031 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6003 1.4977 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8990 0.7455 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.0317 -0.7359 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -5.4979 -1.0529 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -6.2524 0.2436 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -7.4462 0.3649 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -5.2526 1.3618 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -5.5549 2.8291 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -4.4338 3.8270 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.2948 3.4494 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.6759 5.0024 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 7 1 0 + 7 8 1 0 + 8 9 1 0 + 9 10 1 0 + 10 11 1 0 + 11 12 1 0 + 11 13 1 0 + 13 8 1 0 + 13 14 1 0 + 14 15 1 0 + 15 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0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5972 -1.5031 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5951 -3.0039 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.5548 -3.6021 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8933 -3.7570 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8912 -5.2578 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.9292 -5.8600 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8889 -6.4578 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.8509 -5.8560 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 2 4 1 0 + 4 5 2 0 + 4 6 1 0 + 6 7 1 0 + 7 8 2 0 + 8 9 1 0 + 9 10 2 0 + 10 11 1 0 + 11 12 1 0 + 12 13 1 0 + 13 14 2 0 + 13 15 1 0 + 15 16 1 0 + 16 17 1 0 + 16 18 1 0 + 16 19 1 0 + 11 20 2 0 + 20 7 1 0 +M END +> (950) +1200 + +> (950) +Karbutilate + +> (950) +-2.93 + +> (950) +(B) medium + +> (950) +CN(C)C(=O)Nc1cccc(OC(=O)NC(C)(C)C)c1 + +$$$$ +Bensulide + RDKit 2D + + 23 23 0 0 0 0 0 0 0 0999 V2000 + 7.5411 -10.3549 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.5019 -9.7549 0.0000 C 0 0 0 0 0 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0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1950 1.5032 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1928 3.0040 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.2308 3.6061 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 4.1526 3.6022 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5978 1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2806 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5002 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0037 2.7002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2989 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5978 1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 1 0 + 6 7 2 0 + 7 8 1 0 + 8 9 2 0 + 9 10 1 0 + 10 11 1 0 + 11 12 2 0 + 11 13 1 0 + 9 14 1 0 + 14 15 2 0 + 15 6 1 0 + 15 16 1 0 + 16 17 1 0 + 16 18 1 0 + 18 4 1 0 + 18 19 2 0 + 19 1 1 0 +M END +> (955) +1206 + +> (955) +Metiazinic_Acid + +> (955) +-3.94 + +> (955) +(A) low + +> (955) +c1ccc2Sc3ccc(CC(=O)O)cc3N(C)c2c1 + +$$$$ +Piroxicam + RDKit 2D + + 23 25 0 0 0 0 0 0 0 0999 V2000 + -3.6486 1.3517 0.0000 C 0 0 0 0 0 0 0 0 0 0 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0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.9020 -3.7404 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6004 -2.9949 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 2 4 1 0 + 4 5 2 0 + 5 6 1 0 + 6 7 2 0 + 7 8 1 0 + 8 9 2 0 + 9 4 1 0 + 9 10 1 0 + 10 11 1 0 + 11 12 2 0 + 12 13 1 0 + 12 14 1 0 + 14 15 1 0 + 14 16 2 0 + 16 17 1 0 + 17 18 2 0 + 18 11 1 0 +M END +> (958) +1210 + +> (958) +Mefenamic_Acid + +> (958) +-3.78 + +> (958) +(A) low + +> (958) +OC(=O)c1ccccc1Nc2c(C)c(C)ccc2 + +$$$$ +Ancymidol + RDKit 2D + + 19 21 0 0 0 0 0 0 0 0999 V2000 + 2.5956 -2.7031 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5973 -1.5031 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5988 1.5004 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.6378 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0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.3383 1.3500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -2.7000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 1 0 + 4 5 2 0 + 4 6 1 0 + 6 7 1 0 + 7 8 1 0 + 7 9 1 0 + 9 10 2 0 + 9 11 1 0 + 11 12 1 0 + 6 13 1 0 + 13 14 2 0 + 14 15 1 0 + 14 16 1 0 + 16 17 2 0 + 17 18 1 0 + 18 19 2 0 + 19 13 1 0 + 19 20 1 0 +M END +> (964) +1217 + +> (964) +Metalaxyl + +> (964) +-1.6 + +> (964) +(B) medium + +> (964) +COCC(=O)N(C(C)C(=O)OC)c1c(C)cccc1C + +$$$$ +d,l-Mepivacaine + RDKit 2D + + 18 19 0 0 0 0 0 0 0 0999 V2000 + 1.5548 -3.6021 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5951 -3.0039 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5973 -1.5031 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.3383 1.3500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -2.7000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8933 -3.7570 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8934 -5.2570 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1924 -6.0070 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.4915 -5.2571 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.4915 -3.7571 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.8542 -5.8570 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1925 -3.0070 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 4 5 2 0 + 5 6 1 0 + 6 7 2 0 + 7 8 1 0 + 5 9 1 0 + 4 10 1 0 + 10 8 2 0 + 10 11 1 0 + 2 12 1 0 + 12 13 1 0 + 13 14 1 0 + 14 15 1 0 + 15 16 1 0 + 13 17 1 0 + 12 18 1 0 + 18 16 1 0 +M END +> (965) +1219 + +> (965) +d,l-Mepivacaine + +> (965) +-1.55 + +> (965) +(B) medium + +> (965) +O=C(Nc(c(ccc1)C)c1C)C(N(CCC2)C)C2 + +$$$$ +Parethoxycaine + RDKit 2D + + 19 19 0 0 0 0 0 0 0 0999 V2000 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.0031 3.0008 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -1.3039 3.7494 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.3064 4.9494 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0031 -3.0008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0351 -3.6026 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3039 -3.7494 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3070 -5.2502 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.6078 -5.9988 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.6109 -7.4996 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 3.9117 -8.2481 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.9142 -9.4481 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3140 -8.2549 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3178 -9.4549 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 4 5 1 0 + 5 6 1 0 + 3 7 2 0 + 7 8 1 0 + 8 9 2 0 + 9 1 1 0 + 9 10 1 0 + 10 11 2 0 + 10 12 1 0 + 12 13 1 0 + 13 14 1 0 + 14 15 1 0 + 15 16 1 0 + 16 17 1 0 + 15 18 1 0 + 18 19 1 0 +M END +> (966) +1220 + +> (966) +Parethoxycaine + +> (966) +-2.71 + +> (966) +(B) medium + +> (966) +c1cc(OCC)ccc1C(=O)OCCN(CC)CC + +$$$$ +Sparteine + RDKit 2D + + 17 20 0 0 0 0 0 0 0 0999 V2000 + -3.5779 2.4896 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5194 3.5481 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.1032 3.1456 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.7305 1.6697 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 0.6858 1.6697 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.6697 0.6709 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.4174 -0.1491 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.6697 -0.7454 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.1754 -1.1330 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.5332 -2.5940 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 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0 0 0 0 0 0 0 + 5.2000 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.5000 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 7.7999 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 9.0999 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 10.3999 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 11.6999 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 12.9999 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 14.2999 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 15.5999 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 16.8999 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 18.1999 0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 19.2393 0.5997 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 2 4 1 0 + 4 5 1 0 + 5 6 1 0 + 6 7 1 0 + 7 8 1 0 + 8 9 1 0 + 9 10 1 0 + 10 11 1 0 + 11 12 1 0 + 12 13 1 0 + 13 14 1 0 + 14 15 1 0 + 15 16 1 0 + 16 17 1 0 +M END +> (968) +1222 + +> (968) +Pentadecanoic_Acid + +> (968) +-4.31 + +> (968) +(A) low + +> (968) +O=C(O)CCCCCCCCCCCCCC + +$$$$ +Metolazone + RDKit 2D + + 24 26 0 0 0 0 0 0 0 0999 V2000 + -3.6486 1.3517 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6111 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2964 1.4973 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2964 1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5929 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.6321 1.3486 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5929 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2964 -1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2964 -1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2928 -2.6973 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6111 -0.7486 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -3.9086 -1.5029 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8985 -3.0030 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -5.1925 -3.7616 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -6.4965 -3.0203 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -6.5065 -1.5203 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -5.2125 -0.7617 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -5.2206 0.4383 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8926 -1.4991 0.0000 S 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8923 -2.6991 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 4.9322 -0.8997 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 4.9316 -2.0996 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 1 0 + 4 5 2 0 + 5 6 1 0 + 6 7 1 0 + 6 8 2 0 + 8 9 1 0 + 9 10 2 0 + 10 4 1 0 + 10 11 1 0 + 11 12 2 0 + 11 13 1 0 + 13 2 1 0 + 13 14 1 0 + 14 15 2 0 + 15 16 1 0 + 16 17 2 0 + 17 18 1 0 + 18 19 2 0 + 19 14 1 0 + 19 20 1 0 + 8 21 1 0 + 21 22 1 0 + 21 23 2 0 + 21 24 2 0 +M END +> (969) +1224 + +> (969) +Metolazone + +> (969) +-3.78 + +> (969) +(A) low + +> (969) +CC2Nc1cc(Cl)c(cc1C(=O)N2c3ccccc3C)S(N)(=O)=O + +$$$$ +Difenoxuron + RDKit 2D + + 21 22 0 0 0 0 0 0 0 0999 V2000 + 2.5956 -2.7031 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5973 -1.5031 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6003 1.4977 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8990 0.7455 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -5.2007 1.4909 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -6.4972 0.7364 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -6.4920 -0.7636 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -7.7876 -1.5212 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -7.7802 -3.0220 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -6.7379 -3.6166 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -9.0758 -3.7796 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -9.0700 -4.9795 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -10.1182 -3.1850 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -5.1903 -1.5091 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8939 -0.7546 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 7 1 0 + 7 8 1 0 + 8 9 2 0 + 9 10 1 0 + 10 11 2 0 + 11 12 1 0 + 12 13 1 0 + 13 14 2 0 + 13 15 1 0 + 15 16 1 0 + 15 17 1 0 + 11 18 1 0 + 18 19 2 0 + 19 8 1 0 + 6 20 1 0 + 20 21 2 0 + 21 3 1 0 +M END +> 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-1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3383 1.3500 0.0000 Br 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8990 -0.7507 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1977 -1.5013 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.4971 -0.7520 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.4979 0.7480 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 7.5374 1.3474 0.0000 Br 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1992 1.4987 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8998 0.7493 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.6015 -3.0012 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.5631 -3.6028 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.9021 -3.7502 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.9048 -5.2510 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.9447 -5.8498 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.8664 -5.8525 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 1 3 1 0 + 3 4 2 0 + 4 5 1 0 + 5 6 2 0 + 6 7 1 0 + 6 8 1 0 + 8 9 2 0 + 9 3 1 0 + 1 10 1 0 + 10 11 2 0 + 11 12 1 0 + 12 13 2 0 + 13 14 1 0 + 13 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0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8990 -0.7507 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1977 -1.5013 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.4971 -0.7520 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.4979 0.7480 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 7.5374 1.3474 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1992 1.4987 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8998 0.7493 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 2 4 1 0 + 4 5 1 0 + 5 6 2 0 + 5 7 1 0 + 7 8 1 0 + 7 9 1 0 + 9 10 2 0 + 10 11 1 0 + 11 12 2 0 + 12 13 1 0 + 12 14 1 0 + 14 15 2 0 + 15 9 1 0 + 7 16 1 0 + 16 17 2 0 + 17 18 1 0 + 18 19 2 0 + 19 20 1 0 + 19 21 1 0 + 21 22 2 0 + 22 16 1 0 +M END +> (974) +1230 + +> (974) +Chloropropylate + +> (974) +-4.53 + +> (974) +(A) low + +> (974) +CC(C)OC(=O)C(O)(c1ccc(Cl)cc1)c2ccc(Cl)cc2 + +$$$$ +Triflupromazine + RDKit 2D + + 24 26 0 0 0 0 0 0 0 0999 V2000 + 2.6443 7.1832 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.6346 5.9832 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 3.6692 5.3752 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3293 5.2425 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3173 3.7417 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0120 3.0009 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5002 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2989 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5978 1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8968 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8968 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5978 -1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2989 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5002 0.0000 S 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2806 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5978 -1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8968 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8968 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1965 1.5005 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.2361 0.9011 0.0000 F 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1961 2.7005 0.0000 F 0 0 0 0 0 0 0 0 0 0 0 0 + 6.2355 2.1010 0.0000 F 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5978 1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2806 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 2 4 1 0 + 4 5 1 0 + 5 6 1 0 + 6 7 1 0 + 7 8 1 0 + 8 9 2 0 + 9 10 1 0 + 10 11 2 0 + 11 12 1 0 + 12 13 2 0 + 13 8 1 0 + 13 14 1 0 + 14 15 1 0 + 15 16 2 0 + 16 17 1 0 + 17 18 2 0 + 18 19 1 0 + 19 20 1 0 + 19 21 1 0 + 19 22 1 0 + 18 23 1 0 + 23 24 2 0 + 24 7 1 0 + 24 15 1 0 +M END +> (975) +1231 + +> (975) +Triflupromazine + +> (975) +-5.3 + +> (975) +(A) low + +> (975) +CN(C)CCCN2c1ccccc1Sc3ccc(C(F)(F)F)cc23 + +$$$$ +Piperine + RDKit 2D + + 21 23 0 0 0 0 0 0 0 0999 V2000 + -7.2775 5.3768 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -6.2413 5.9820 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -6.2494 7.4828 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -7.5509 8.2286 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -7.5559 9.7286 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -6.2593 10.4829 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.9578 9.7372 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.9528 8.2372 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.9380 5.2378 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.9300 3.7369 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.6267 2.9927 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.6187 1.4919 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3155 0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3155 -0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0028 -1.5132 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2917 -0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7138 -1.2033 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5889 0.0182 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7138 1.2033 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2917 0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0028 1.5132 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 4 5 1 0 + 5 6 1 0 + 6 7 1 0 + 3 8 1 0 + 8 7 1 0 + 2 9 1 0 + 9 10 2 3 + 10 11 1 0 + 11 12 2 3 + 12 13 1 0 + 13 14 2 0 + 14 15 1 0 + 15 16 2 0 + 16 17 1 0 + 17 18 1 0 + 18 19 1 0 + 16 20 1 0 + 20 19 1 0 + 13 21 1 0 + 21 20 2 0 +M END +> (976) +1232 + +> (976) +Piperine + +> (976) +-3.46 + +> (976) +(A) low + +> (976) +O=C(N(CCCC1)C1)C=CC=Cc(ccc(OCO2)c23)c3 + +$$$$ +Napropamide + RDKit 2D + + 20 21 0 0 0 0 0 0 0 0999 V2000 + -4.9082 8.1170 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8717 7.5123 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8775 6.0115 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -5.1820 5.2692 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -6.2178 5.8751 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5812 5.2552 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.5395 5.8509 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5870 3.7544 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.6288 3.1588 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2907 2.9981 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2964 1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6111 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6111 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2964 -1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2964 -1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5929 -0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5929 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2964 1.4973 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 0.7486 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 1 0 + 4 5 1 0 + 3 6 1 0 + 6 7 2 0 + 6 8 1 0 + 8 9 1 0 + 8 10 1 0 + 10 11 1 0 + 11 12 2 0 + 12 13 1 0 + 13 14 2 0 + 14 15 1 0 + 15 16 2 0 + 16 17 1 0 + 17 18 2 0 + 18 19 1 0 + 19 20 2 0 + 20 11 1 0 + 20 15 1 0 +M END +> (977) +1234 + +> (977) +Napropamide + +> (977) +-3.57 + +> (977) +(A) low + +> (977) +CCN(CC)C(=O)C(C)Oc1cccc2ccccc12 + +$$$$ +Scopolamine + RDKit 2D + + 22 25 0 0 0 0 0 0 0 0999 V2000 + -2.5769 1.7563 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.7271 2.0986 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -2.7365 3.5760 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.3935 2.9212 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0913 1.5110 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.9978 0.3693 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.4585 0.3693 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.4154 1.5110 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -5.4059 2.4008 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -4.0796 2.9212 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.3789 1.2178 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8613 -0.2034 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0698 -1.1054 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.3331 -0.4972 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.3207 0.6329 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.9337 1.7688 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.8155 -1.9184 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.2861 -2.2141 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.7653 -3.6355 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.7740 -4.7612 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.3034 -4.4656 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.8242 -3.0442 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 1 0 + 4 5 1 0 + 5 6 1 0 + 6 7 1 0 + 7 2 1 0 + 7 8 1 0 + 8 9 1 0 + 9 10 1 0 + 10 3 1 0 + 10 8 1 0 + 5 11 1 0 + 11 12 1 0 + 12 13 2 0 + 12 14 1 0 + 14 15 1 0 + 15 16 1 0 + 14 17 1 0 + 17 18 2 0 + 18 19 1 0 + 19 20 2 0 + 20 21 1 0 + 21 22 2 0 + 22 17 1 0 +M END +> (978) +1235 + +> (978) +Scopolamine + +> (978) +-0.48 + +> (978) +(C) high 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0 0 0 0 0 0 0 0 0 + -2.9441 -2.9021 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.6939 0.9965 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.0926 2.0350 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 4 5 1 0 + 5 6 1 0 + 6 7 1 0 + 7 8 1 0 + 8 9 1 0 + 7 10 1 0 + 6 11 1 0 + 11 9 1 0 + 4 12 1 0 + 12 8 1 0 + 2 13 1 0 + 13 14 1 0 + 14 15 2 0 + 15 16 1 0 + 16 17 2 0 + 17 18 1 0 + 14 19 1 0 + 19 18 2 0 + 13 20 1 0 + 20 21 1 0 +M END +> (979) +1236 + +> (979) +Hyoscyamine + +> (979) +-1.91 + +> (979) +(B) medium + +> (979) +O=C(OC(CC(N(C1C2)C)C2)C1)C(c(cccc3)c3)CO + +$$$$ +Butachlor + RDKit 2D + + 21 21 0 0 0 0 0 0 0 0999 V2000 + 6.2253 -8.1139 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1873 -7.5117 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1894 -6.0109 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8912 -5.2578 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8933 -3.7570 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5951 -3.0039 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5973 -1.5031 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8999 -0.7576 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.9040 0.4424 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 5.1972 -1.5121 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.2387 -0.9161 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5972 1.5031 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5955 2.7031 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2990 -0.7500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.0031 -3.0008 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0432 -3.5994 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 1 0 + 4 5 1 0 + 5 6 1 0 + 6 7 1 0 + 7 8 1 0 + 8 9 2 0 + 8 10 1 0 + 10 11 1 0 + 7 12 1 0 + 12 13 2 0 + 13 14 1 0 + 14 15 1 0 + 13 16 1 0 + 16 17 2 0 + 17 18 1 0 + 18 19 2 0 + 19 12 1 0 + 19 20 1 0 + 20 21 1 0 +M END +> (980) +1237 + +> (980) +Butachlor + +> (980) +-4.19 + +> (980) +(A) low + 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0 0 0 0 + 4.5625 0.3285 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5625 1.8067 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.3032 2.5367 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.2968 3.7367 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0440 1.7885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0413 2.9885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8030 2.5185 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4562 1.7885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4380 0.3285 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6790 -0.3832 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.9565 0.3102 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 2 4 1 0 + 4 5 2 0 + 5 6 1 0 + 6 7 1 0 + 7 8 2 3 + 8 9 1 0 + 9 10 1 0 + 10 11 1 0 + 11 12 1 0 + 12 13 2 0 + 12 14 1 0 + 14 9 1 0 + 14 15 1 0 + 14 16 1 0 + 16 17 1 0 + 17 18 1 0 + 18 8 1 0 + 18 19 1 0 + 19 5 1 0 + 19 20 2 0 + 20 1 1 0 +M END +> (982) +1239 + +> (982) +Equilin + +> (982) +-5.28 + +> (982) +(A) low + +> (982) +c1c(O)cc2CC=C3C4CCC(=O)C4(C)CCC3c2c1 + +$$$$ +Thebainone_A + RDKit 2D + + 22 25 0 0 0 0 0 0 0 0999 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1 0 + 8 9 2 0 + 9 10 1 0 + 10 11 1 0 + 10 12 1 0 + 12 13 1 0 + 13 14 1 0 + 14 15 1 0 + 15 16 1 0 + 16 17 1 0 + 17 18 1 0 + 18 19 2 0 + 13 20 1 0 + 20 17 1 0 + 12 21 1 0 + 21 16 1 0 + 5 22 1 0 + 22 9 1 0 + 2 23 1 0 + 23 22 2 0 + 1 24 1 0 +M END +> (1001) +1265 + +> (1001) +Quinidine + +> (1001) +-3.37 + +> (1001) +(A) low + +> (1001) +O(c(ccc(nccc1C(O)C(N(CCC2C3C=C)C3)C2)c14)c4)C + +$$$$ +Ethinyl_Estradiol + RDKit 2D + + 22 25 0 0 0 0 0 0 0 0999 V2000 + 2.2475 3.1071 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.3032 2.5367 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5984 3.2544 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.6339 3.8610 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0440 1.7885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0440 0.3467 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8030 -0.3650 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4380 0.3285 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6790 -0.3832 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6790 -1.8250 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.9382 -2.5732 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.1792 -1.8432 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -5.2209 -2.4390 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -4.1792 -0.4015 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4197 -2.5550 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.9565 0.3102 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4562 1.7885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8212 -1.8067 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5625 0.3285 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8030 2.5185 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.0061 1.1862 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5625 1.8067 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 3 0 + 2 5 1 0 + 5 6 1 0 + 6 7 1 0 + 7 8 1 0 + 8 9 1 0 + 9 10 2 0 + 10 11 1 0 + 11 12 2 0 + 12 13 1 0 + 12 14 1 0 + 10 15 1 0 + 9 16 1 0 + 16 14 2 0 + 8 17 1 0 + 7 18 1 0 + 18 15 1 0 + 6 19 1 0 + 5 20 1 0 + 20 17 1 0 + 5 21 1 0 + 2 22 1 0 + 22 19 1 0 +M END +> (1002) +1267 + +> (1002) +Ethinyl_Estradiol + +> (1002) +-4.3 + +> (1002) +(A) low + +> (1002) +OC(C#C)(C(C(C(C(c(c(cc(O)c1)C2)c1)C3)C2)C4)(C3)C)C4 + +$$$$ +Ajmaline + RDKit 2D + + 24 29 0 0 0 0 0 0 0 0999 V2000 + -4.4200 1.3400 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.8400 0.1200 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.9200 -0.8600 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6200 -0.5600 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.5800 -1.3600 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -1.5800 -2.5600 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.5100 -0.5900 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.7500 -0.9200 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.0300 -2.0400 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.9300 -1.2500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.1600 -1.5200 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.2573 -2.5250 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.4018 -2.1645 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.7800 -0.3500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.6584 0.4675 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.6300 0.0200 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2500 1.2800 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.9600 2.4200 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5900 0.0000 C 0 0 0 0 0 0 0 0 0 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+ + 26 28 0 0 0 0 0 0 0 0999 V2000 + -2.3155 0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.3155 -0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.6187 -1.4919 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -3.6251 -2.6919 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0028 -1.5132 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2917 -0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7138 -1.2033 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.1855 -2.6254 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.6552 -2.9294 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.0330 -4.0684 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 4.4530 -2.0330 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5889 0.0182 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.7889 0.0269 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7138 1.2033 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 2.1812 2.6271 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.3808 3.5211 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.6500 2.9355 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.1182 4.3614 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.5870 4.6698 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.0571 6.0943 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 7.5257 6.3996 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 8.5244 5.2803 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 8.0544 3.8559 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.5858 3.5506 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2917 0.7475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.0028 1.5132 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 2 5 1 0 + 5 6 2 0 + 6 7 1 0 + 7 8 1 0 + 8 9 1 0 + 9 10 2 0 + 9 11 1 0 + 7 12 2 0 + 12 13 1 0 + 12 14 1 0 + 14 15 1 0 + 15 16 2 0 + 15 17 1 0 + 17 18 2 3 + 18 19 1 0 + 19 20 2 0 + 20 21 1 0 + 21 22 2 0 + 22 23 1 0 + 23 24 2 0 + 24 19 1 0 + 14 25 1 0 + 25 6 1 0 + 25 26 2 0 + 26 1 1 0 +M END +> (1005) +1271 + +> (1005) +Cinmetacin + +> (1005) +-5.54 + +> (1005) +(A) low + +> (1005) +c1c(OC)cc2c(CC(=O)O)c(C)n(C(=O)C=Cc3ccccc3)c2c1 + +$$$$ +Permethrin + RDKit 2D + + 26 28 0 0 0 0 0 0 0 0999 V2000 + 5.6761 -5.8681 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.6369 -6.4681 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.6673 -7.6677 0.0000 C 0 0 0 0 0 0 0 0 0 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0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6790 -1.8250 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4197 -2.5550 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8212 -1.8067 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8030 -0.3650 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0440 0.3467 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5625 0.3285 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5625 1.8067 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.3032 2.5367 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.2968 3.7367 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0440 1.7885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0413 2.9885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8030 2.5185 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4562 1.7885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4380 0.3285 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6790 -0.3832 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6930 0.8167 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.9565 0.3102 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 3 4 1 0 + 4 5 2 0 + 4 6 1 0 + 2 7 1 0 + 7 8 1 0 + 8 9 2 3 + 9 10 1 0 + 10 11 1 0 + 11 12 1 0 + 12 13 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+C1C(O)CC2=CCC3C4CCC(C(=O)C)C4(C)CCC3C2(C)C1 + +$$$$ +Chlortetracycline + RDKit 2D + + 33 36 0 0 0 0 0 0 0 0999 V2000 + 5.8814 2.1657 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 5.8722 0.9657 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.9070 0.3581 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5671 0.2243 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5671 -1.2660 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.6070 -1.8648 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.2691 -2.0191 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.2722 -3.5199 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 4.3122 -4.1184 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.2340 -4.1217 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.9711 -1.2660 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.9711 0.2243 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.9743 1.4243 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.6891 0.9935 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.6923 2.1935 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -0.6089 0.2243 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.6089 -1.2660 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.9070 -2.0191 0.0000 C 0 0 0 0 0 0 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C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0440 0.3467 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8030 -0.3650 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4380 0.3285 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6790 -0.3832 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6790 -1.8250 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.9382 -2.5732 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.1792 -1.8432 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -5.2209 -2.4390 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -4.1792 -0.4015 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4197 -2.5550 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.9565 0.3102 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6930 0.8167 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4562 1.7885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8212 -1.8067 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5625 0.3285 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8030 2.5185 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0413 2.9885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5625 1.8067 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5964 6.2919 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.6343 6.8943 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 2 0 + 2 3 1 0 + 3 4 1 0 + 4 5 1 0 + 5 6 1 0 + 6 7 1 0 + 7 8 1 0 + 8 9 1 0 + 9 10 1 0 + 10 11 2 3 + 11 12 1 0 + 12 13 2 0 + 12 14 1 0 + 10 15 1 0 + 9 16 1 0 + 16 14 1 0 + 9 17 1 0 + 8 18 1 0 + 7 19 1 0 + 19 15 1 0 + 6 20 1 0 + 5 21 1 0 + 21 18 1 0 + 5 22 1 0 + 4 23 1 0 + 23 20 1 0 + 2 24 1 0 + 24 25 1 0 +M END +> (1016) +1284 + +> (1016) +Testosterone_Propionate + +> (1016) +-5.37 + +> (1016) +(A) low + +> (1016) +O=C(OC(C(C(C(C(C(C(=CC(=O)C1)C2)(C1)C)C3)C2)C4)(C3)C)C4)CC + +$$$$ +Rotenone + RDKit 2D + + 29 33 0 0 0 0 0 0 0 0999 V2000 + -6.9056 -4.9085 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -6.8762 -3.7088 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -5.5600 -2.9900 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.1300 -3.8900 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.6700 -3.1400 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2700 -3.9900 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2000 -3.2400 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.2000 -1.5700 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.7200 -0.7700 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0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0120 3.0009 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 1.5002 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2989 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5978 1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8968 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.8968 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.5978 -1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.2989 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.0000 -1.5002 0.0000 S 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2806 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5978 -1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8968 -0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.8968 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.9360 1.3500 0.0000 Cl 0 0 0 0 0 0 0 0 0 0 0 0 + 2.5978 1.5002 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.2806 0.7501 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.9266 5.2210 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.2327 5.9587 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 2 4 1 0 + 4 5 1 0 + 5 6 1 0 + 6 7 1 0 + 7 8 1 0 + 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+O=C(NCC(=O)O)CCC(C(C(C(C(C(C(C(C1)CC(O)C2)(C2)C)C3)C1O)C4)(C3O)C)C4)C + +$$$$ +Rolitetracycline + RDKit 2D + + 28 31 0 0 0 0 0 0 0 0999 V2000 + -3.2050 0.2243 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.9070 0.9935 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.9070 2.1935 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -0.6089 0.2243 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.6891 0.9935 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.6923 2.1935 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.9711 0.2243 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.9743 1.4243 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.2691 0.9935 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.2691 2.1935 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5671 0.2243 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5671 -1.2660 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.6070 -1.8648 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.2691 -2.0191 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.2722 -3.5199 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 4.3122 -4.1184 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.2340 -4.1217 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.9711 -1.2660 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.6891 -2.0191 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.6089 -1.2660 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.9070 -2.0191 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.9600 -2.5946 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -0.8540 -2.5948 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.2050 -1.2660 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.5030 -2.0191 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -5.8010 -1.2660 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -5.8010 0.2243 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.5030 0.9935 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 2 4 1 0 + 4 5 2 3 + 5 6 1 0 + 5 7 1 0 + 7 8 1 0 + 7 9 1 0 + 9 10 2 0 + 9 11 1 0 + 11 12 2 3 + 12 13 1 0 + 12 14 1 0 + 14 15 1 0 + 15 16 1 0 + 15 17 1 0 + 14 18 1 0 + 18 7 1 0 + 18 19 1 0 + 19 20 1 0 + 20 4 1 0 + 20 21 1 0 + 21 22 1 0 + 21 23 1 0 + 21 24 1 0 + 24 1 2 0 + 24 25 1 0 + 25 26 2 0 + 26 27 1 0 + 27 28 2 0 + 28 1 1 0 +M END +> (1023) +1293 + +> (1023) +Rolitetracycline + +> (1023) +-1.42 + +> (1023) +(B) medium + +> (1023) +c12C(=O)C3=C(O)C4(O)C(=O)C=C(O)C(N(C)C)C4CC3C(O)(C)c1cccc2 + +$$$$ +Hydrocortisone_Tebutate + RDKit 2D + + 33 36 0 0 0 0 0 0 0 0999 V2000 + -4.1792 -0.4015 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.1792 -1.8432 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -5.2209 -2.4390 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -2.9382 -2.5732 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6790 -1.8250 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4197 -2.5550 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8212 -1.8067 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8030 -0.3650 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0440 0.3467 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5625 0.3285 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5625 1.8067 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.3032 2.5367 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.2469 3.1060 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5993 3.2532 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.5927 4.4532 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 5.9034 2.5103 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 7.1993 3.2675 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 8.5033 2.5246 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 8.5099 1.3246 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 9.7992 3.2818 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 11.1033 2.5389 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 12.1394 3.1443 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 12.1457 1.9445 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 11.1099 1.3389 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 2.0440 1.7885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.0061 1.1862 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.8030 2.5185 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4562 1.7885 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.4997 2.3811 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -0.4380 0.3285 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6790 -0.3832 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -1.6930 0.8167 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.9565 0.3102 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 2 0 + 2 4 1 0 + 4 5 2 3 + 5 6 1 0 + 6 7 1 0 + 7 8 1 0 + 8 9 1 0 + 9 10 1 0 + 10 11 1 0 + 11 12 1 0 + 12 13 1 0 + 12 14 1 0 + 14 15 2 0 + 14 16 1 0 + 16 17 1 0 + 17 18 1 0 + 18 19 2 0 + 18 20 1 0 + 20 21 1 0 + 21 22 1 0 + 21 23 1 0 + 21 24 1 0 + 12 25 1 0 + 25 9 1 0 + 25 26 1 0 + 25 27 1 0 + 27 28 1 0 + 28 29 1 0 + 28 30 1 0 + 30 8 1 0 + 30 31 1 0 + 31 5 1 0 + 31 32 1 0 + 31 33 1 0 + 33 1 1 0 +M END +> (1024) +1294 + +> (1024) +Hydrocortisone_Tebutate + +> (1024) +-5.51 + +> (1024) +(A) low + +> (1024) +C1C(=O)C=C2CCC3C4CCC(O)(C(=O)COC(=O)CC(C)(C)C)C4(C)CC(O)C3C2(C)C1 + +$$$$ +Natamycin + RDKit 2D + + 47 50 0 0 0 0 0 0 0 0999 V2000 + 5.1239 2.2882 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 6.1035 3.4242 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.7082 4.5573 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 7.5770 3.1440 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 8.3607 4.0528 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 8.0711 1.7277 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 9.2500 1.5034 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 + 7.0916 0.5916 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 7.4869 -0.5414 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 5.6180 0.8718 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 4.6354 -0.2626 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 3.1600 0.0200 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.6900 -1.2100 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.6900 -2.7000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.3900 -3.4500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.3900 -4.9600 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.9300 -5.6900 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -2.1900 -4.9100 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.5100 -5.6100 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.7900 -4.7900 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -6.1600 -5.4700 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -7.6200 -4.5100 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -8.7504 -4.9126 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -7.5900 -3.0500 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -8.9700 -2.0600 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -9.9662 -2.7290 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -8.9400 -0.6300 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -7.5700 0.3300 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -7.5500 1.7100 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -7.5500 3.2000 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -6.2600 2.2900 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -4.7400 1.5700 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.4400 2.2900 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -3.4242 3.4899 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -2.1600 1.5500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.8300 2.2900 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + -0.8300 1.0900 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.5100 1.5100 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 1.8300 2.2900 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1.8300 3.8500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.1318 4.6027 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.1318 5.8027 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 4.1713 4.0031 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + 0.5100 4.6300 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 0.5100 5.8300 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 + -0.8300 3.8500 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 3.1600 1.5200 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 + 1 2 1 0 + 2 3 1 0 + 2 4 1 0 + 4 5 1 0 + 4 6 1 0 + 6 7 1 0 + 6 8 1 0 + 8 9 1 0 + 8 10 1 0 + 10 1 1 0 + 10 11 1 0 + 11 12 1 0 + 12 13 1 0 + 13 14 2 3 + 14 15 1 0 + 15 16 2 3 + 16 17 1 0 + 17 18 2 3 + 18 19 1 0 + 19 20 2 3 + 20 21 1 0 + 21 22 1 0 + 22 23 1 0 + 22 24 1 0 + 24 25 1 0 + 25 26 2 0 + 25 27 1 0 + 27 28 2 3 + 28 29 1 0 + 29 30 1 0 + 29 31 1 0 + 31 30 1 0 + 31 32 1 0 + 32 33 1 0 + 33 34 1 0 + 33 35 1 0 + 35 36 1 0 + 36 37 1 0 + 36 38 1 0 + 38 39 1 0 + 39 40 1 0 + 40 41 1 0 + 41 42 2 0 + 41 43 1 0 + 40 44 1 0 + 44 45 1 0 + 44 46 1 0 + 46 36 1 0 + 39 47 1 0 + 47 12 1 0 +M END +> (1025) +1296 + +> (1025) +Natamycin + +> (1025) +-3.21 + +> (1025) +(A) low + +> (1025) +O1C(C)C(O)C(N)C(O)C1OC2C=CC=CC=CC=CCC(C)OC(=O)C=CC(O3)C3CC(O)CC4(O)OC(C(C(=O)O)C(O)C4)C2 + +$$$$ diff --git a/datamol/io.py b/datamol/io.py index da048597..a709bf35 100644 --- a/datamol/io.py +++ b/datamol/io.py @@ -2,8 +2,9 @@ from typing import Optional from typing import List from typing import Sequence -from typing import TextIO +from typing import IO from typing import Any +from typing import cast import os import io @@ -24,7 +25,7 @@ def read_csv( - urlpath: Union[str, os.PathLike, TextIO], + urlpath: Union[str, os.PathLike, IO], smiles_column: Optional[str] = None, mol_column: str = "mol", **kwargs: Any, @@ -51,7 +52,7 @@ def read_csv( def read_excel( - urlpath: Union[str, os.PathLike, TextIO], + urlpath: Union[str, os.PathLike, IO], sheet_name: Optional[Union[str, int, list]] = 0, smiles_column: Optional[str] = None, mol_column: str = "mol", @@ -71,7 +72,8 @@ def read_excel( df: a `pandas.DataFrame` """ - df = pd.read_excel(urlpath, sheet_name=sheet_name, **kwargs) # type: ignore + df = pd.read_excel(urlpath, sheet_name=sheet_name, **kwargs) + df = cast(pd.DataFrame, df) if smiles_column is not None: PandasTools.AddMoleculeColumnToFrame(df, smiles_column, mol_column) @@ -80,7 +82,7 @@ def read_excel( def read_sdf( - urlpath: Union[str, os.PathLike, TextIO], + urlpath: Union[str, os.PathLike, IO], sanitize: bool = True, as_df: bool = False, smiles_column: Optional[str] = "smiles", @@ -153,7 +155,7 @@ def read_sdf( def to_sdf( mols: Union[Mol, Sequence[Mol], pd.DataFrame], - urlpath: Union[str, os.PathLike, TextIO], + urlpath: Union[str, os.PathLike, IO], smiles_column: Optional[str] = "smiles", mol_column: Optional[str] = None, ): @@ -259,7 +261,7 @@ def to_molblock( def to_smi( mols: Sequence[Mol], - urlpath: Union[str, os.PathLike, TextIO], + urlpath: Union[str, os.PathLike, IO], error_if_empty: bool = False, ): """Save a list of molecules in an `.smi` file. diff --git a/docs/index.md b/docs/index.md index 9a3b24fa..f328ff2a 100644 --- a/docs/index.md +++ b/docs/index.md @@ -75,6 +75,7 @@ See below the associated versions of Python and RDKit, for which a minor version | `datamol` | `python` | `rdkit` | | --------- | ------------ | -------------------- | +| `0.8` | `[3.9, 3.10]` | `[2021.09, 2022.03]` | | `0.7` | `[3.8, 3.9]` | `[2021.09, 2022.03]` | | `0.6` | `[3.8, 3.9]` | `[2021.09]` | | `0.5` | `[3.8, 3.9]` | `[2021.03, 2021.09]` | diff --git a/news/tutos.rst b/news/tutos.rst index a6a3bbc8..7025ca18 100644 --- a/news/tutos.rst +++ b/news/tutos.rst @@ -7,6 +7,7 @@ * Revamped all the datamol tutorials and add new tutorials. Huge thanks to @Valence-jonnyhsu for leading the refactoring of the datamol tutorials. * Improve documentation for `dm.standardize_mol()` * Multiple various docstring and typing improvments. +* Embed the cdk2.sdf and solubility_*.sdf files within the datamol package to prevent issue with the RDKit config dir. **Deprecated:** From 2d0b4f01fd5842e6dfe4bde29ddee9334f02a0fa Mon Sep 17 00:00:00 2001 From: Hadrien Mary Date: Sun, 4 Sep 2022 14:43:52 -0400 Subject: [PATCH 06/15] WIP --- datamol/mol.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/datamol/mol.py b/datamol/mol.py index 99ba5821..c841a695 100644 --- a/datamol/mol.py +++ b/datamol/mol.py @@ -1041,6 +1041,11 @@ def to_scaffold_murcko(mol: Mol, make_generic: bool = False): """ scf = MurckoScaffold.GetScaffoldForMol(mol) + # NOTE(hadim): this is already done in `GetScaffoldForMol` + # Note sure we need it here. + scf.UpdatePropertyCache() + Chem.GetSymmSSSR(scf) # type: ignore + if make_generic: scf = make_scaffold_generic(scf) scf = to_mol(scf) @@ -1069,6 +1074,9 @@ def make_scaffold_generic(mol: Mol, include_bonds: bool = False): for bond in mol.GetBonds(): bond.SetBondType(UNSPECIFIED_BOND) + mol.UpdatePropertyCache() + Chem.GetSymmSSSR(mol) # type: ignore + return mol From 07b8f6838b2bac168a0450a4ee5ddc3fd3b8b8a0 Mon Sep 17 00:00:00 2001 From: Hadrien Mary Date: Sun, 4 Sep 2022 14:47:15 -0400 Subject: [PATCH 07/15] remove copy from add and remove hs --- .github/workflows/doc.yml | 2 +- .github/workflows/test.yml | 2 +- datamol/mol.py | 12 ------------ news/tutos.rst | 1 + 4 files changed, 3 insertions(+), 14 deletions(-) diff --git a/.github/workflows/doc.yml b/.github/workflows/doc.yml index 5e590eb0..d496a7f0 100644 --- a/.github/workflows/doc.yml +++ b/.github/workflows/doc.yml @@ -17,7 +17,7 @@ jobs: uses: actions/checkout@v2 - name: Setup conda - uses: mamba-org/provision-with-micromamba + uses: mamba-org/provision-with-micromamba@main with: environment-file: false cache-downloads: true diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index dddae587..39bccf49 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -30,7 +30,7 @@ jobs: uses: actions/checkout@v2 - name: Setup conda - uses: mamba-org/provision-with-micromamba + uses: mamba-org/provision-with-micromamba@main with: environment-file: false cache-downloads: true diff --git a/datamol/mol.py b/datamol/mol.py index c841a695..edfc5d20 100644 --- a/datamol/mol.py +++ b/datamol/mol.py @@ -947,7 +947,6 @@ def add_hs( add_coords: bool = False, only_on_atoms: Optional[List[int]] = None, add_residue_info: bool = False, - copy: bool = True, ): """Adds hydrogens to the molecule. @@ -958,12 +957,7 @@ def add_hs( only_on_atoms: a list of atoms to add hydrogens only on. add_residue_info: whether to add residue information to the hydrogens. Useful for PDB files. - copy: whether to copy the input molecule. """ - - if copy: - mol = copy_mol(mol) - mol = AddHs( mol, explicitOnly=explicit_only, @@ -980,7 +974,6 @@ def remove_hs( implicit_only: bool = False, update_explicit_count: bool = False, sanitize: bool = True, - copy: bool = True, ): """Removes hydrogens from a molecule. @@ -989,12 +982,7 @@ def remove_hs( implicit_only: whether to only remove implicit hydrogens. update_explicit_count: whether to update the explicit hydrogen count. sanitize: whether to sanitize the molecule after the hydrogens are removed. - copy: whether to copy the input molecule. """ - - if copy: - mol = copy_mol(mol) - mol = RemoveHs( mol, implicitOnly=implicit_only, diff --git a/news/tutos.rst b/news/tutos.rst index 7025ca18..36f7784d 100644 --- a/news/tutos.rst +++ b/news/tutos.rst @@ -16,6 +16,7 @@ **Removed:** * Remove unused and unmaintained `dm.actions` and `dm.reactions` module. +* Remove `copy` args from `add_hs` and `remove_hs` (RDKit already returns copies). **Fixed:** From 7dd78d687c94224bce78cf6f2ef77102a60a1d26 Mon Sep 17 00:00:00 2001 From: Hadrien Mary Date: Sun, 4 Sep 2022 14:49:54 -0400 Subject: [PATCH 08/15] strict mode for doc --- datamol/fragment/_assemble.py | 21 ++++++++++++--------- datamol/utils/fs.py | 2 +- mkdocs.yml | 3 +++ 3 files changed, 16 insertions(+), 10 deletions(-) diff --git a/datamol/fragment/_assemble.py b/datamol/fragment/_assemble.py index 47d4aebe..28672f3a 100644 --- a/datamol/fragment/_assemble.py +++ b/datamol/fragment/_assemble.py @@ -9,6 +9,8 @@ This differs from rdkit BRICSBuild implementation that requires the presence of dummy indicator atoms added by a prior BRICS fragmentation. """ +from typing import Optional + import copy import json import itertools @@ -24,6 +26,7 @@ from rdkit.Chem import rdChemReactions import datamol as dm +from ..types import Mol CCQ = "[$([#6][!#6;!#1]):1]!@[#6;!a:2]>>[*:1].[*:2]" CCQ_RETRO = "[$([#6;!H0][!#6;!#1]):1].[#6;!a;!H0:2]>>[*:1][*:2]" @@ -256,7 +259,7 @@ def _run_at_all_rct(rxn, mol1, mol2): mol = None mSmi = "" try: - mSmi = Chem.MolToSmiles(m) + mSmi = dm.to_smiles(m) mol = dm.to_mol(mSmi) except: pass @@ -264,7 +267,7 @@ def _run_at_all_rct(rxn, mol1, mol2): try: mol.UpdatePropertyCache() mol = dm.sanitize_mol(mol) - mSmi = Chem.MolToSmiles(m) + mSmi = dm.to_smiles(m) mol = dm.to_mol(mSmi) except: pass @@ -298,7 +301,7 @@ def break_mol( all_reactions_type = [all_reactions_type[ind] for ind in p] nx = dm.graph._get_networkx() - mSmi = Chem.MolToSmiles(mol, isomericSmiles=True) + mSmi = dm.to_smiles(mol, isomericSmiles=True) G = nx.DiGraph() node_num = 0 G.add_node(node_num, smiles=mSmi, mol=mol) @@ -340,7 +343,7 @@ def break_mol( seqOk = False break continue - pSmi = Chem.MolToSmiles(prod, isomericSmiles=True) + pSmi = dm.to_smiles(prod, isomericSmiles=True) seqOk = seqOk and (dm.to_mol(pSmi) is not None) notDummies = sum([atm.GetSymbol() != "*" for atm in prod.GetAtoms()]) @@ -357,7 +360,7 @@ def break_mol( continue pSmi = prod.pSmi node_num += 1 - usmi = Chem.MolToSmiles(dm.fix_mol(prod), isomericSmiles=True) + usmi = dm.to_smiles(dm.fix_mol(prod), isomericSmiles=True) G.add_node(node_num, smiles=usmi, mol=prod) G.add_edge(parent, node_num) if usmi not in allNodes: @@ -426,8 +429,8 @@ def build(ll_mols, max_n_mols=float("inf"), mode="brics", frag_rxn=None, ADD_RNX def assemble_fragment_order( - fragmentlist, - seen=None, + fragmentlist: list, + seen: Optional[Mol] = None, allow_incomplete: bool = False, max_n_mols: float = float("inf"), RXNS=None, @@ -453,7 +456,7 @@ def assemble_fragment_order( yield_counter = 0 if seen is None: seen = fragmentlist.pop(0) - seen = [Chem.MolToSmiles(seen)] # only one molecule to assemble + seen = [dm.to_smiles(seen)] # only one molecule to assemble while yield_counter < max_n_mols and len(fragmentlist) > 0: # find all the way to add this fragment to seen frag = fragmentlist.pop(0) @@ -520,7 +523,7 @@ def assemble_fragment_iter( max_n_mols=(max_n_mols - len(seen)), maxdepth=maxdepth - 1, ): - pSmi = Chem.MolToSmiles(p, True) + pSmi = dm.to_smiles(p, True) if pSmi not in seen: seen.add(pSmi) yield p if not as_smiles else pSmi diff --git a/datamol/utils/fs.py b/datamol/utils/fs.py index e77c1ee1..d4a3a094 100644 --- a/datamol/utils/fs.py +++ b/datamol/utils/fs.py @@ -168,7 +168,7 @@ def is_local_path(path: Union[str, os.PathLike]): return get_protocol(str(path)) == "file" -def join(*paths): +def join(*paths: str): """Join paths together. The first element determine the filesystem to use (and so the separator. diff --git a/mkdocs.yml b/mkdocs.yml index 27632413..de92a039 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -9,6 +9,9 @@ remote_branch: "gh-pages" use_directory_urls: false docs_dir: "docs" +# Fail on warnings to detect issues with types and docstring +strict: true + nav: - Overview: index.md - Usage: usage.md From 349f4834ef4c0791949cc5f6829dbfc8b74c9427 Mon Sep 17 00:00:00 2001 From: Hadrien Mary Date: Sun, 4 Sep 2022 14:50:30 -0400 Subject: [PATCH 09/15] news --- news/tutos.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/news/tutos.rst b/news/tutos.rst index 36f7784d..37fc50ef 100644 --- a/news/tutos.rst +++ b/news/tutos.rst @@ -8,6 +8,7 @@ * Improve documentation for `dm.standardize_mol()` * Multiple various docstring and typing improvments. * Embed the cdk2.sdf and solubility_*.sdf files within the datamol package to prevent issue with the RDKit config dir. +* Enable strict mode on the documentation to prevent any issues and inconsistency with the types and docstrings of datamol. **Deprecated:** From 7106f6f6a0586d28e834a4097b0c90babd76c311 Mon Sep 17 00:00:00 2001 From: Hadrien Mary Date: Sun, 4 Sep 2022 14:57:04 -0400 Subject: [PATCH 10/15] ci micromamba simplification --- .github/patch_conda_env.py | 59 -------------------------------------- .github/workflows/test.yml | 40 +++++++------------------- news/tutos.rst | 1 + 3 files changed, 12 insertions(+), 88 deletions(-) delete mode 100644 .github/patch_conda_env.py diff --git a/.github/patch_conda_env.py b/.github/patch_conda_env.py deleted file mode 100644 index adc317fb..00000000 --- a/.github/patch_conda_env.py +++ /dev/null @@ -1,59 +0,0 @@ -import sys -import argparse -import yaml - - -def main(env_path, new_deps): - - # Process dependencies - new_deps = [dep.split("=") for dep in new_deps] - - # Load the conda env file - with open(env_path) as f: - env_spec = yaml.load(f, Loader=yaml.SafeLoader) - - deps = env_spec["dependencies"] - for name, version in new_deps: - - # Find whether the package already exists - existing = list( - filter( - lambda x: True if isinstance(x, str) and x.startswith(name) else False, - deps, - ) - ) - - # Remove the existing package(s) - [deps.pop(deps.index(e)) for e in existing] - - # Add the new package spec if the spec is not None - if version != "DELETE": - if version == "": - deps.append(name) - else: - deps.append(f"{name} ={version}") - - # Add the new dep list to the eocnda env spec - env_spec["dependencies"] = deps - - # Serialize back to YAML and print on stdout - sys.stdout.write(yaml.dump(env_spec)) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Patch a conda env file") - parser.add_argument( - "--env", - metavar="CONDA_ENV_PATH", - type=str, - help="Path to conda env file.", - ) - parser.add_argument( - "-d", - "--deps", - nargs="+", - help="Dependencies to patch.", - ) - - args = parser.parse_args() - main(args.env, args.deps) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 39bccf49..dbc2108f 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -32,35 +32,20 @@ jobs: - name: Setup conda uses: mamba-org/provision-with-micromamba@main with: - environment-file: false + environment-file: env.yml + environment-name: datamol cache-downloads: true - - - name: Setup conda env - run: | - micromamba activate - - # Patch the conda env file to specify the Python and RDKit version - micromamba install -c conda-forge --yes pyyaml - - python .github/patch_conda_env.py --env env.yml -d \ - python="${{ matrix.python-version }}" \ - rdkit="${{ matrix.rdkit-version }}" \ - > env-patched.yml - - - name: Install Dependencies - run: | - micromamba activate - micromamba create -n datamol -f env-patched.yml + extra-specs: | + python=${{ matrix.python-version }} + rdkit=${{ matrix.rdkit-version }} - name: Install library - run: | - micromamba activate datamol - python -m pip install -e . # `-e` required for correct `coverage` run. + shell: bash -l {0} + run: python -m pip install -e . # `-e` required for correct `coverage` run. - name: Run tests - run: | - micromamba activate datamol - pytest + shell: bash -l {0} + run: pytest - name: Codecov Upload uses: codecov/codecov-action@v1 @@ -73,8 +58,5 @@ jobs: env_vars: ${{ matrix.os }},${{ matrix.python-version }},${{ matrix.rdkit-version }} - name: Test building the doc - run: | - micromamba activate datamol - - # Build and serve the doc - mkdocs build + shell: bash -l {0} + run: mkdocs build diff --git a/news/tutos.rst b/news/tutos.rst index 37fc50ef..2b170069 100644 --- a/news/tutos.rst +++ b/news/tutos.rst @@ -9,6 +9,7 @@ * Multiple various docstring and typing improvments. * Embed the cdk2.sdf and solubility_*.sdf files within the datamol package to prevent issue with the RDKit config dir. * Enable strict mode on the documentation to prevent any issues and inconsistency with the types and docstrings of datamol. +* Refactor micromamba CI to use latest and simplify it. **Deprecated:** From 10196327b417b461c6dbe0cf93d8b43ca27fbc26 Mon Sep 17 00:00:00 2001 From: Hadrien Mary Date: Sun, 4 Sep 2022 14:58:32 -0400 Subject: [PATCH 11/15] WIP --- .github/workflows/test.yml | 3 --- 1 file changed, 3 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index dbc2108f..1730dec5 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -40,11 +40,9 @@ jobs: rdkit=${{ matrix.rdkit-version }} - name: Install library - shell: bash -l {0} run: python -m pip install -e . # `-e` required for correct `coverage` run. - name: Run tests - shell: bash -l {0} run: pytest - name: Codecov Upload @@ -58,5 +56,4 @@ jobs: env_vars: ${{ matrix.os }},${{ matrix.python-version }},${{ matrix.rdkit-version }} - name: Test building the doc - shell: bash -l {0} run: mkdocs build From fe50f2a087c0c2d9d0f7309d7b6a96c5f75aedcf Mon Sep 17 00:00:00 2001 From: Hadrien Mary Date: Sun, 4 Sep 2022 14:59:49 -0400 Subject: [PATCH 12/15] WIP --- .github/workflows/test.yml | 2 +- README.md | 1 - docs/index.md | 1 - 3 files changed, 1 insertion(+), 3 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 1730dec5..d8e7072f 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -14,7 +14,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: ["3.9", "3.10"] + python-version: ["3.8", "3.9"] os: ["ubuntu-latest", "macos-latest", "windows-latest"] rdkit-version: ["2021.09", "2022.03"] diff --git a/README.md b/README.md index 001f6773..186c75fa 100644 --- a/README.md +++ b/README.md @@ -94,7 +94,6 @@ See below the associated versions of Python and RDKit, for which a minor version | `datamol` | `python` | `rdkit` | | --------- | ------------ | -------------------- | -| `0.8` | `[3.9, 3.10]` | `[2021.09, 2022.03]` | | `0.7` | `[3.8, 3.9]` | `[2021.09, 2022.03]` | | `0.6` | `[3.8, 3.9]` | `[2021.09]` | | `0.5` | `[3.8, 3.9]` | `[2021.03, 2021.09]` | diff --git a/docs/index.md b/docs/index.md index f328ff2a..9a3b24fa 100644 --- a/docs/index.md +++ b/docs/index.md @@ -75,7 +75,6 @@ See below the associated versions of Python and RDKit, for which a minor version | `datamol` | `python` | `rdkit` | | --------- | ------------ | -------------------- | -| `0.8` | `[3.9, 3.10]` | `[2021.09, 2022.03]` | | `0.7` | `[3.8, 3.9]` | `[2021.09, 2022.03]` | | `0.6` | `[3.8, 3.9]` | `[2021.09]` | | `0.5` | `[3.8, 3.9]` | `[2021.03, 2021.09]` | From 81f1891574c5c6658af01dee63dbcda81adc1878 Mon Sep 17 00:00:00 2001 From: Hadrien Mary Date: Sun, 4 Sep 2022 15:01:15 -0400 Subject: [PATCH 13/15] WIP --- .github/workflows/doc.yml | 14 +++----------- 1 file changed, 3 insertions(+), 11 deletions(-) diff --git a/.github/workflows/doc.yml b/.github/workflows/doc.yml index d496a7f0..5e4c65b9 100644 --- a/.github/workflows/doc.yml +++ b/.github/workflows/doc.yml @@ -19,23 +19,15 @@ jobs: - name: Setup conda uses: mamba-org/provision-with-micromamba@main with: - environment-file: false + environment-file: env.yml + environment-name: datamol cache-downloads: true - - name: Install Dependencies - run: | - micromamba activate - micromamba create -n datamol -f env.yml - - name: Install library - run: | - micromamba activate datamol - python -m pip install . + run: python -m pip install . - name: Deploy the doc run: | - micromamba activate datamol - echo "Configure git" git config --global user.name 'hadim' git config --global user.email 'hadim@users.noreply.github.com' From ac845b4caa701366964e41cdd83be8184ef9fbc4 Mon Sep 17 00:00:00 2001 From: Hadrien Mary Date: Sun, 4 Sep 2022 15:08:19 -0400 Subject: [PATCH 14/15] typo --- datamol/fragment/_assemble.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/datamol/fragment/_assemble.py b/datamol/fragment/_assemble.py index 28672f3a..0d108b5e 100644 --- a/datamol/fragment/_assemble.py +++ b/datamol/fragment/_assemble.py @@ -301,7 +301,7 @@ def break_mol( all_reactions_type = [all_reactions_type[ind] for ind in p] nx = dm.graph._get_networkx() - mSmi = dm.to_smiles(mol, isomericSmiles=True) + mSmi = dm.to_smiles(mol, isomeric=True) G = nx.DiGraph() node_num = 0 G.add_node(node_num, smiles=mSmi, mol=mol) @@ -343,7 +343,7 @@ def break_mol( seqOk = False break continue - pSmi = dm.to_smiles(prod, isomericSmiles=True) + pSmi = dm.to_smiles(prod, isomeric=True) seqOk = seqOk and (dm.to_mol(pSmi) is not None) notDummies = sum([atm.GetSymbol() != "*" for atm in prod.GetAtoms()]) @@ -360,7 +360,7 @@ def break_mol( continue pSmi = prod.pSmi node_num += 1 - usmi = dm.to_smiles(dm.fix_mol(prod), isomericSmiles=True) + usmi = dm.to_smiles(dm.fix_mol(prod), isomeric=True) G.add_node(node_num, smiles=usmi, mol=prod) G.add_edge(parent, node_num) if usmi not in allNodes: From 4aa23dfd5de163191f3f35b5282ee28ab5e9b8a5 Mon Sep 17 00:00:00 2001 From: Hadrien Mary Date: Sun, 4 Sep 2022 15:09:35 -0400 Subject: [PATCH 15/15] WIP --- .github/workflows/doc.yml | 2 +- .github/workflows/test.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/doc.yml b/.github/workflows/doc.yml index 5e4c65b9..82495dca 100644 --- a/.github/workflows/doc.yml +++ b/.github/workflows/doc.yml @@ -24,7 +24,7 @@ jobs: cache-downloads: true - name: Install library - run: python -m pip install . + run: python -m pip install --no-deps . - name: Deploy the doc run: | diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index d8e7072f..cba8741a 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -40,7 +40,7 @@ jobs: rdkit=${{ matrix.rdkit-version }} - name: Install library - run: python -m pip install -e . # `-e` required for correct `coverage` run. + run: python -m pip install --no-deps -e . # `-e` required for correct `coverage` run. - name: Run tests run: pytest
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